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Biological additive manufacturing (Bio-AM) has emerged as a promising approach for the fabrication of biological scaffolds with nano- to microscale resolutions and biomimetic architectures beneficial to tissue engineering applications. However, Bio-AM processes tend to introduce flaws in the construct during fabrication. These flaws can be traced to material nonhomogeneity, suboptimal processing parameters, changes in the (bio)printing environment (such as nozzle clogs), and poor construct design, all with significant contributions to the alteration of a scaffold's mechanical properties. In addition, the biological response of endogenous and exogenous cells interacting with the defective scaffolds could become unpredictable. In this review, we first described extrusion-based Bio-AM. We highlighted the salient architectural and mechanotransduction parameters affecting the response of cells interfaced with the scaffolds. The process phenomena leading to defect formation and some of the tools for defect detection are reviewed. The limitations of the existing developments and the directions that the field should grow in order to overcome said limitations are discussed.

Quality is a key determinant in deploying new processes, products, or services and influences the adoption of emerging manufacturing technologies. The advent of additive manufacturing (AM) as a manufacturing process has the potential to revolutionize a host of enterprise-related functions from production to the supply chain. The unprecedented level of design flexibility and expanded functionality offered by AM, coupled with greatly reduced lead times, can potentially pave the way for mass customization. However, widespread application of AM is currently hampered by technical challenges in process repeatability and quality management. The breakthrough effect of six sigma (6S) has been demonstrated in traditional manufacturing industries (e.g., semiconductor and automotive industries) in the context of quality planning, control, and improvement through the intensive use of data, statistics, and optimization. 6S entails a data-driven DMAIC methodology of five steps—define, measure, analyze, improve, and control. Notwithstanding the sustained successes of the 6S knowledge body in a variety of established industries ranging from manufacturing, healthcare, logistics, and beyond, there is a dearth of concentrated application of 6S quality management approaches in the context of AM. In this article, we propose to design, develop, and implement the new DMAIC methodology for the 6S quality management of AM. First, we define the specific quality challenges arising from AM layerwise fabrication and mass customization (even one-of-a-kind production). Second, we present a review of AM metrology and sensing techniques, from materials through design, process, and environment, to postbuild inspection. Third, we contextualize a framework for realizing the full potential of data from AM systems and emphasize the need for analytical methods and tools. We propose and delineate the utility of new data-driven analytical methods, including deep learning, machine learning, and network science, to characterize and model the interrelationships between engineering design, machine setting, process variability, and final build quality. Fourth, we present the methodologies of ontology analytics, design of experiments (DOE), and simulation analysis for AM system improvements. In closing, new process control approaches are discussed to optimize the action plans, once an anomaly is detected, with specific consideration of lead time and energy consumption. We posit that this work will catalyze more in-depth investigations and multidisciplinary research efforts to accelerate the application of 6S quality management in AM.

Powder bed fusion (PBF) additive manufacturing (AM) provides a great level of flexibility in the design-driven build of metal products. However, the more complex the design, the more difficult it becomes to control the quality of AM builds. The quality challenge persistently hampers the widespread application of AM technology. Advanced imaging (e.g., X-ray computed tomography scans and high-resolution optical images) has been increasingly explored to enhance the visibility of information and improve the AM quality control. Realizing the full potential of imaging data depends on the advent of information processing methodologies for the analysis of design-quality interactions. This paper presents a design of AM experiment to investigate how design parameters (e.g., build orientation, thin-wall width, thin-wall height, and contour space) interact with quality characteristics in thin-wall builds. Note that the build orientation refers to the position of thin-walls in relation to the recoating direction on the plate, and the contour space indicates the width between rectangle hatches. First, we develop a novel generalized recurrence network (GRN) to represent the AM spatial image data. Then, GRN quantifiers, namely degree, betweenness, pagerank, closeness, and eigenvector centralities, are extracted to characterize the quality of layerwise builds. Further, we establish a regression model to predict how the design complexity impacts GRN behaviors in each layer of thin-wall builds. Experimental results show that network features are sensitive to build orientations, width, height, and contour space under the significant level α = 0.05. Thin-walls with the width bigger than 0.1 mm printed under orientation 0∘ are found to yield better quality compared to 60∘ and 90∘. Also, thin-walls build with orientation 60∘ are more sensitive to the changes in contour space compare to the other two orientations. As a result, the orientation 60∘ should be avoided while printing thin-wall structures. The proposed design-quality analysis shows great potential to optimize engineering design and enhance the quality of PBF-AM builds.

The objective of this work is to tackle the challenges of monitoring and detecting subtle process changes in chemical mechanical planarization (CMP), an ultraprecision manufacturing process. Monitoring ultraprecision processes is usually of difficulty due to their innate complexity and low signal-to-noise ratio in sensor signals. Especially for subtle signal variations during small process changes, the conventional statistical process control charts could fail to detect such process anomalies from the sensor signals in the time domain. In this paper, frequency spectra representation of the microelectromechanical systems (MEMS) vibration sensor signals during subtle process changes is investigated, and the signal patterns uncovered by frequency spectra are utilized to formulate a machine learning–driven in situ process monitoring approach to detect process anomalies in CMP. The proposed approach overcomes the obstacles of differentiating subtle signal changes by transforming them into the frequency domain with Fourier transform and Hilbert-Huang transform and classifying the resulted frequency spectra with random forest. Based on frequency analysis, it can unveil the differences in the signals obscured in the time domain and suppress the high-frequency noise. Consequently, the presented machine learning–driven in situ process monitoring approach detects process anomalies by differentiating the deviated frequency spectra with machine learning. It is validated on our experimental CMP testbed for anomaly detection, and outperforms three benchmark statistical process control charts. For instance, it detects a slurry shutoff anomaly in CMP about ten times faster than the benchmark methods.

Bone defects are common and, in many cases, challenging to treat. Tissue engineering is an interdisciplinary approach with promising potential for treating bone defects. Within tissue engineering, three-dimensional (3D) printing strategies have emerged as potent tools for scaffold fabrication. However, reproducibility and quality control are critical aspects limiting the translation of 3D printed scaffolds to clinical use, which remain to be addressed. To elucidate the factors that yield to the generation of defects in bioprinting and to achieve reproducible biomaterial printing, the objective of this article is to frame a systematic approach for optimizing and validating 3D printing of poly(caprolactone) (PCL)-hydroxyapatite (HAp) composite scaffolds. We delineate the effect of PCL-to-HAp ratio, print velocity, print temperature, and extrusion pressure on the architectural and mechanical properties of the 3D printed scaffold. Furthermore, we present an in situ image-based monitoring approach to quantify key quality-related aspects of constructs, such as the ability to deposit material consistently and print elementary shapes with fewer flaws. Our results show that small defects generated during the printing process have a significant role in lowering the mechanical properties of 3D printed polymeric scaffolds. In addition, the in vitro osteoinductivity of the fabricated scaffolds is demonstrated. IMPACT STATEMENT: Identifying quality control measures is essential in the translation of three-dimensional (3D) printed scaffolds into clinical practice. In this article, we highlighted the importance of selected printing parameters on the quality of the 3D printed scaffolds. We also demonstrated that flaws, such as voids, significantly lower the mechanical properties (compressive modulus) of 3D printed polymeric scaffolds.

The goal of this work is to achieve the defect-free production of parts made using Additive Manufacturing (AM) processes. As a step towards this goal, the objective is to detect flaws in AM parts d...

A key challenge in metal additive manufacturing is the prevalence of defects, such as discontinuities within the part (e.g., porosity). The objective of this work is to monitor porosity in Laser Po...

In Laser Powder Bed Fusion (LPBF) along, more than 50 process parameters are known to affect print quality. The current state-of-the-art practice in process control only considers a small fraction of them – mainly on laser power and scanning speed affecting temperature gradient and geometry of a melting pool. This letter proposes a system-wide platform involving various machine learning principles and leveraging production data stored in the cloud. The proposed framework aims to identify process parameters that may affect print quality so that a viable process control strategy can be formulated.

The goal of this work is to predict the effect of part geometry and process parameters on the direction and magnitude of heat flow ‒ heat flux ‒ in parts made using metal additive manufacturing (AM) processes. As a step towards this goal, the objective of this paper is to develop and apply the mathematical concept of heat diffusion over graphs to approximate the heat flux in metal AM parts as a function of their geometry. This objective is consequential to overcome the poor process consistency and part quality in AM. Currently, part build failure rates in metal AM often exceed 20%, the causal reason for this poor part yield in metal AM processes is ascribed to the nature of the heat flux in the part. For instance, constrained heat flux causes defects such as warping, thermal stress-induced cracking, etc. Hence, to alleviate these challenges in metal AM processes, there is a need for computational thermal models to estimate the heat flux, and thereby guide part design and selection of process parameters. Compared to moving heat source finite element analysis techniques, the proposed graph theoretic approach facilitates layer-by-layer simulation of the heat flux within a few minutes on a desktop computer, instead of several hours on a supercomputer.

Aerosol jet printing (AJP) is a direct-write additive manufacturing technique, which has emerged as a high-resolution method for the fabrication of a broad spectrum of electronic devices. Despite the advantages and critical applications of AJP in the printed-electronics industry, AJP process is intrinsically unstable, complex, and prone to unexpected gradual drifts, which adversely affect the morphology and consequently the functional performance of a printed electronic device. Therefore, in situ process monitoring and control in AJP is an inevitable need. In this respect, in addition to experimental characterization of the AJP process, physical models would be required to explain the underlying aerodynamic phenomena in AJP. The goal of this research work is to establish a physics-based computational platform for prediction of aerosol flow regimes and ultimately, physics-driven control of the AJP process. In pursuit of this goal, the objective is to forward a three-dimensional (3D) compressible, turbulent, multiphase computational fluid dynamics (CFD) model to investigate the aerodynamics behind: (i) aerosol generation, (ii) aerosol transport, and (iii) aerosol deposition on a moving free surface in the AJP process. The complex geometries of the deposition head as well as the pneumatic atomizer were modeled in the ansys-fluent environment, based on patented designs in addition to accurate measurements, obtained from 3D X-ray micro-computed tomography (μ-CT) imaging. The entire volume of the constructed geometries was subsequently meshed using a mixture of smooth and soft quadrilateral elements, with consideration of layers of inflation to obtain an accurate solution near the walls. A combined approach, based on the density-based and pressure-based Navier–Stokes formation, was adopted to obtain steady-state solutions and to bring the conservation imbalances below a specified linearization tolerance (i.e., 10−6). Turbulence was modeled using the realizable k-ε viscous model with scalable wall functions. A coupled two-phase flow model was, in addition, set up to track a large number of injected particles. The boundary conditions of the CFD model were defined based on experimental sensor data, recorded from the AJP control system. The accuracy of the model was validated using a factorial experiment, composed of AJ-deposition of a silver nanoparticle ink on a polyimide substrate. The outcomes of this study pave the way for the implementation of physics-driven in situ monitoring and control of AJP.

In this work, we accomplished the crack-free directed energy deposition (DED) of a multi-layer Cobalt-Chromium alloy coating (Stellite 21) on Inconel 718 substrate. Stellite alloys are used as coating materials given their resistance to wear, corrosion, and high temperature. The main challenge in DED of Stellite coatings is the proclivity for crack formation during printing. The objective of this work is to characterize the effect of the input energy density and localized laser-based preheating on the characteristics of the deposited coating, namely, crack formation, microstructural evolution, dilution of the coating composition due to diffusion of iron and nickel from the substrate, and microhardness. It is observed that cracking is alleviated on preheating the substrate and depositing the coating at a moderate energy density (~200 J·mm−3). The main finding is that cracking of DED-processed Stellite 21 coating at higher levels of energy density is linked to the elemental segregation of chromium and molybdenum, which form hard and brittle phases in the inter-dendritic regions. Cracking in the inter-dendritic regions is caused by residual stresses resulting from the steep thermal gradients at higher input energy. Localized laser-based preheating and moderate energy density mitigate steep temperature gradients and thereby avoid thermally induced cracking of the Stellite coating along the inter-dendritic regions.

In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other neuroimaging methodologies. In a similar time frame, “deep learning” (a term for the use of artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on linear calculations; a number of studies have applied deep learning techniques to neuroimaging data, but we believe that those have barely scratched the surface of the potential deep learning holds for the field. In this paper, we provide a brief introduction to deep learning for those new to the technique, explore the logistical pros and cons of using deep learning to analyze neuroimaging data – which we term “deep MVPA,” or dMVPA – and introduce a new software toolbox (the “Deep Learning In Neuroimaging: Exploration, Analysis, Tools, and Education” package, DeLINEATE for short) intended to facilitate dMVPA for neuroscientists (and indeed, scientists more broadly) everywhere.

The objective of this work is to provide experimental validation of the graph theory approach for predicting the thermal history of additively manufactured parts. The graph theory approach for thermal modeling in additive manufacturing (AM) was recently published in these transactions. In the present paper, the graph theory approach is validated with in situ infrared thermography data in the context of the laser powder bed fusion (LPBF) additive manufacturing process. We realize the foregoing objective through the following four tasks. First, two kinds of test shapes, namely, a cylinder and cone, are made in two separate builds on a production-type LPBF machine (Renishaw AM250); the material used for these tests is stainless steel (SAE 316L). The intent of both builds is to influence the thermal history of the part by controlling the cooling time between the melting of successive layers, called the interlayer cooling time (ILCT). Second, layer-wise thermal images of the top surface of the part are acquired using an in situ a priori calibrated infrared camera. Third, the thermal imaging data obtained during the two builds is used to validate the graph theory-predicted surface temperature trends. Fourth, the surface temperature trends predicted using graph theory are compared with results from finite element (FE) analysis. The results substantiate the computational advantages of the graph theory approach over finite element analysis. As an example, for the cylinder-shaped test part, the graph theory approach predicts the surface temperature trends to within 10% mean absolute percentage error (MAPE) and approximately 16 K root mean squared error (RMSE) relative to the surface temperature trends measured by the thermal camera. Furthermore, the graph theory-based temperature predictions are made in less than 65 min, which is substantially faster than the actual build time of 171 min. In comparison, for an identical level of resolution and prediction error, the finite element approach requires 175 min.

Aerosol jet printing (AJP) is a direct-write additive manufacturing (AM) method, emerging as the process of choice for the fabrication of a broad spectrum of electronics, such as sensors, transistors, and optoelectronic devices. However, AJP is a highly complex process, prone to intrinsic gradual drifts. Consequently, real-time process monitoring and control in AJP is a bourgeoning need. The goal of this work is to establish an integrated, smart platform for in situ and real-time monitoring of the functional properties of AJ-printed electronics. In pursuit of this goal, the objective is to forward a multiple-input, single-output (MISO) intelligent learning model—based on sparse representation classification (SRC)—to estimate the functional properties (e.g., resistance) in situ as well as in real-time. The aim is to classify the resistance of printed electronic traces (lines) as a function of AJP process parameters and the trace morphology characteristics (e.g., line width, thickness, and cross-sectional area (CSA)). To realize this objective, line morphology is captured using a series of images, acquired: (i) in situ via an integrated high-resolution imaging system and (ii) in real-time via the AJP standard process monitor camera. Utilizing image processing algorithms developed in-house, a wide range of 2D and 3D morphology features are extracted, constituting the primary source of data for the training, validation, and testing of the SRC model. The four-point probe method (also known as Kelvin sensing) is used to measure the resistance of the deposited traces and as a result, to define a priori class labels. The results of this study exhibited that using the presented approach, the resistance (and potentially, other functional properties) of printed electronics can be estimated both in situ and in real-time with an accuracy of ≥ 90%.

Many recent developments in machine learning have come from the field of “deep learning,” or the use of advanced neural network architectures and techniques. While these methods have produced state-of-the-art results and dominated research focus in many fields, such as image classification and natural language processing, they have not gained as much ground over standard multivariate pattern analysis (MVPA) techniques in the classification of electroencephalography (EEG) or other human neuroscience datasets. The high dimensionality and large amounts of noise present in EEG data, coupled with the relatively low number of examples (trials) that can be reasonably obtained from a sample of human subjects, lead to difficulty training deep learning models. Even when a model successfully converges in training, significant overfitting can occur despite the presence of regularization techniques. To help alleviate these problems, we present a new method of “paired trial classification” that involves classifying pairs of EEG recordings as coming from the same class or different classes. This allows us to drastically increase the number of training examples, in a manner akin to but distinct from traditional data augmentation approaches, through the combinatorics of pairing trials. Moreover, paired trial classification still allows us to determine the true class of a novel example (trial) via a “dictionary” approach: compare the novel example to a group of known examples from each class, and determine the final class via summing the same/different decision values within each class. Since individual trials are noisy, this approach can be further improved by comparing a novel individual example with a “dictionary” in which each entry is an average of several examples (trials). Even further improvements can be realized in situations where multiple samples from a single unknown class can be averaged, thus permitting averaged signals to be compared with averaged signals.

The objective of this work is to model and quantify the layer-wise spatial evolution of porosity in parts made using Additive Manufacturing (AM) processes. This is an important research area becaus...

The objective of this paper is to monitor complex process dynamics manifest in multivariate (multidimensional) time series data using a spectral (algebraic) graph theoretic approach. We test the hypothesis that the spectral graph-based topological invariants detect incipient process drifts earlier [lower average run length (ARL1)] and with higher fidelity (consistency of detection) when compared with the conventional statistics-based approaches. The presented approach maps a multidimensional sensor data stream $\mathcal {X}^{N\times d}$ (visualize $N$ as time and $d$ as the number of sensors) as an unweighted and undirected network graph $G(V,E)$ , indexed by its vertices $V$ and edges $E$ , i.e., $\mathcal {X}\mapsto G(V,E)$ . The rationale is that the graph-based topological invariants are surrogate representatives of the system state. We compare the monitoring performance of spectral graph theoretic invariants with conventional statistical features in an exponentially weighted moving average control chart setting. The practical utility of the approach is substantiated in the context of process monitoring in two advanced manufacturing scenarios, namely, ultraprecision machining (UPM) and semiconductor chemical mechanical planarization. These studies corroborate the hypothesis that graph theoretic invariants, when used as monitoring statistics, lead to lower ARL1 and more consistent detections in contrast to conventional statistical features. For instance, in the UPM case, the fault detection delay using graph theoretic invariants is less than 160 ms, compared with over 8 s of delay with statistical features. Note to Practitioners—This paper addresses the critical problem of capturing process drifts from multidimensional (multisensor) data. The novelty of this paper is the development of a graph theoretic approach that combines signals from multiple in situ sensors for detecting abnormal process drifts. We show that this approach, which invokes graph-based topological invariants instead of statistical feature mining, is capable of capturing process drifts at an earlier stage (in terms of detection delay or lower average run length) and higher consistency of detection than conventional statistical features. As a practical consequence of this research, the operator can track the status of a complex process in a tractable control chart setting with only two graph theoretic topological invariants, as opposed to complex black-box models involving several features.

Additive manufacturing (AM) enables flexible part geometry and functionality, and reduces product development life cycle by direct layer-wise fabrication from CAD files. In the last decade, great achievements are made on AM materials, machines, processes, etc. However, the quality of the AM parts is still questionable for industrial specifications. On the one hand, AM part quality variables can be either quantitative, such as dimensional accuracy, or qualitative, such as binary indicators for voids, missing features, or surface roughness. On the other hand, both offline process setting variables and functional in situ process variables can be measured and modeled with both quantitative and qualitative (QQ) quality response variables. In this paper, the QQ quality response variables are modeled by offline process setting variables and in situ process variables via functional QQ models. The modeling of these in situ process variables provides the basis for real-time monitoring and control for AM processes. Simulation studies and experimental data from a fused deposition modeling process are performed to demonstrate the effectiveness of the proposed method. Note to Practitioners —Additive manufacturing (AM) processes have attracted much attention and showed many advantages over the traditional subtractive manufacturing processes. However, the product quality issues make AM intractable for high-quality parts in industrial applications. This paper aims to address the quality issues by modeling both quantitative quality variables, such as dimensional accuracy, and qualitative quality variables, such as the binary (go/no-go) indicator for surface conditions. Both offline process setting variables and in situ process variables are used in the model as predictors. Such a model is important for systematically quality evaluation of AM parts, and provides the basis for future process monitoring and control. The merits of the proposed method are demonstrated with simulation studies and a case study in a fused deposition modeling process.

The goal of this research is the in-situ detection of flaw formation in metal parts made using the laser powder bed fusion (LPBF) additive manufacturing process. This is an important area of research, because, despite the considerable cost and time savings achieved, precision-driven industries, such as aerospace and biomedical, are reticent in using LPBF to make safety–critical parts due to tendency of the process to create flaws. Another emerging concern in LPBF, and additive manufacturing in general, is related to cyber security – malicious actors may tamper with the process or plant flaws inside a part to compromise its performance. Accordingly, the objective of this work is to develop and apply a physics and data integrated strategy for online monitoring and detection of flaw formation in LPBF parts. The approach used to realize this objective is based on combining (twinning) in-situ meltpool temperature measurements with a graph theory-based thermal simulation model that rapidly predicts the temperature distribution in the part (thermal history). The novelty of the approach is that the temperature distribution predictions provided by the computational thermal model were updated layer-by-layer with in-situ meltpool temperature measurements. This digital twin approach is applied to detect flaw formation in stainless steel (316L) impeller-shaped parts made using a commercial LPBF system. Four such impellers are produced emulating three pathways of flaw formation in LPBF parts, these are: changes in the processing parameters (process drifts); machine-related malfunctions (lens delamination), and deliberate tampering with the process to plant flaws inside the part (cyber intrusions). The severity and nature of the resulting flaws, such as porosity and microstructure heterogeneity, are characterized ex-situ using X-ray computed tomography, optical and scanning electron microscopy, and electron backscatter diffraction. The digital twin approach is shown to be effective for detection of the three types of flaw formation causes studied in this work.

Flaw formation in laser powder bed fusion (LPBF) is influenced by the spatiotemporal temperature distribution – thermal history – of the part during the process. Therefore, to prevent flaw formation there is a need for fast and accurate models that can predict the thermal history as a function of the part shape and processing parameters. In previous work, a thermal modeling approach based on graph theory was used to predict the thermal history in LPBF parts in less-than 20% of the time required by finite element-based models with error within 10% of experimental measurements. The present work transitions toward the use of the graph theory approach for predicting flaw formation. The objectives of this paper are to: (1) apply the graph theory approach for predicting the thermal history of several LPBF parts that have different geometries but were all built together on a single build plate; (2) compare the graph theory thermal model with experimental temperature measurements made using an in-situ infrared camera; and (3) relate the thermal history predictions obtained from the graph theory approach to flaw formation in LPBF parts. In pursuit of these objectives, fifteen different Inconel 718 parts encompassing five different shapes were built simultaneously on an open architecture LPBF platform (build time 9.5 h). Second, the LPBF machine was instrumented with an in-situ infrared camera to capture the layer-wise surface temperature of each part as it was being deposited. Third, the thermal history for each part was predicted with the graph theory approach, and the model predictions were assessed against experimental temperature measurements. Fourth, the porosity in certain test parts was quantified with X-ray computed tomography, and their microstructure was characterized with optical and scanning electron microscopy. The results show that the shape of the part has a significant effect on the thermal history, and thereby influences the occurrence of build failures (recoater crash), type and severity of porosity, and morphology of the microstructure. The graph theory approach correctly predicted the thermal history trends that lead to flaw formation in LPBF within a fraction of the build time – the root mean squared prediction error was less-than 20 °C, and computation time was approximately 5 min. The graph theory method has the potential to serve LPBF practitioners as a rapid physics-based approach to guide part design and identify suitable processing parameters in place of expensive and time-consuming empirical trial-and-error optimization.

The goal of this work is to use sensor data for online detection and identification of process anomalies (faults). In pursuit of this goal, we propose Dirichlet process Gaussian mixture (DPGM) models. The proposed DPGM models have two novel outcomes: 1) DP-based statistical process control (SPC) chart for anomaly detection and 2) unsupervised recurrent hierarchical DP clustering model for identification of specific process anomalies. The presented DPGM models are validated using numerical simulation studies as well as wireless vibration signals acquired from an experimental semiconductor chemical mechanical planarization (CMP) test bed. Through these numerically simulated and experimental sensor data, we test the hypotheses that DPGM models have significantly lower detection delays compared with SPC charts in terms of the average run length (ARL1) and higher defect identification accuracies (F-score) than popular clustering techniques, such as mean shift. For instance, the DP-based SPC chart detects pad wear anomaly in CMP within 50 ms, as opposed to over 140 ms with conventional control charts. Likewise, DPGM models are able to classify different anomalies in CMP.

Aerosol jet printing (AJP) is a direct-write additive manufacturing method, which has been utilized particularly for the fabrication of flexible and hybrid electronics (FHE). In spite of the advantages of AJP — e.g., high-resolution material deposition on nonplanar surfaces and accommodation of a wide renege of ink viscosity — AJP inherently is a complex process, prone to nonlinear process changes. Consequently, real-time process monitoring and control (with an understanding of the physics behind aerosol generation and transport) are inevitable. The overarching goal of this work is to establish a physics-based framework for process monitoring and closed-loop control (for correction) in AJP. In pursuit of this goal, the objective is to forward a CFD model to explain the underlying physical phenomena behind aerosol nebulization in AJP. To realize this objective, a 3D compressible, turbulent multi-phase flow CFD model is forwarded. The geometry of the pneumatic atomizer is modeled based on X-ray computed tomography (CT) imaging. The boundary conditions of the problem are defined based on experimental observations. The outcome of this study paves the way for understanding the complex mechanisms of aerosol generation in AJP and also design of efficient atomizers.

Part design and process parameters directly influence the instantaneous spatiotemporal distribution of temperature in parts made using additive manufacturing (AM) processes. The temporal evolution of temperature in AM parts is termed herein as the thermal profile or thermal history. The thermal profile of the part, in turn, governs the formation of defects, such as porosity and shape distortion. Accordingly, the goal of this work is to understand the effect of the process parameters and the geometry on the thermal profile in AM parts. As a step toward this goal, the objectives of this work are two-fold. First, to develop and apply a finite element-based framework that captures the transient thermal phenomena in the fused filament fabrication (FFF) additive manufacturing of acrylonitrile butadiene styrene (ABS) parts. Second, validate the model-derived thermal profiles with experimental in-process measurements of the temperature trends obtained under different material deposition speeds. In the specific context of FFF, this foray is the critical first-step toward understanding how and why the thermal profile directly affects the degree of bonding between adjacent roads (linear track of deposited material), which in turn determines the strength of the part, as well as, propensity to form defects, such as delamination. From the experimental validation perspective, we instrumented a Hyrel Hydra FFF machine with three non-contact infrared temperature sensors (thermocouples) located near the nozzle (extruder) of the machine. These sensors measure the surface temperature of a road as it is deposited. Test parts are printed under three different settings of feed rate, and subsequently, the temperature profiles acquired from the infrared thermocouples are juxtaposed against the model-derived temperature profiles. Comparison of the experimental and model-derived thermal profiles confirms a high degree of correlation therein, with a mean absolute percentage error less than 6% (root mean squared error

The goal of this work is to forward a comprehensive framework, relating to the most recent research works carried out in the area of flexible and hybrid electronics (FHE) fabrication with the aid of aerosol jet printing (AJP) additive manufacturing process. In pursuit of this goal, the objective is to review and classify a wide range of articles, published recently, concerning various aspects of AJP-based device fabrication, such as material synthesis, process monitoring, and control. AJP has recently emerged as the technique of choice for integration as well as fabrication of a broad spectrum of electronic components and devices, e.g., interconnects, sensors, transistors, optical waveguides, quantum dot arrays, photodetectors, and circuits. This is preeminently because of advantages engendered by AJP process. AJP not only allows for high-resolution deposition of microstructures, but also accommodates a wide renege of ink viscosity. However, AJP is intrinsically complex and prone to gradual drifts of the process output (stemming from ink chemistry and formulation). Consequently, a large number of research works in the literature has focused on in situ process characterization, real-time monitoring, and closed-loop control with the aim to make AJP a rapid, reliable, and robust additive manufacturing method for the manufacture of flexible and hybrid electronic devices. It is expected that the market for flexible electronics will be worth over $50 billion by 2020 [1].

The goal of this work is to quantify the link between the design features (geometry), in-situ process sensor signatures, and build quality of parts made using laser powder bed fusion (LPBF) additive manufacturing (AM) process. This knowledge is critical for establishing design rules for AM parts, and to detecting impending build failures using in-process sensor data. As a step towards this goal, the objectives of this work are two-fold: 1) Quantify the effect of the geometry and orientation on the build quality of thin-wall features. To explain further, the geometry-related factor is the ratio of the length of a thin-wall (l) to its thickness (t) defined as the aspect ratio (length-to-thickness ratio, l/t), and the angular orientation (θ) of the part, which is defined as the angle of the part in the X-Y plane relative to the re-coater blade of the LPBF machine. 2) Assess the thin-wall build quality by analyzing images of the part obtained at each layer from an in-situ optical camera using a convolutional neural network. To realize these objectives, we designed a test part with a set of thin-wall features (fins) with varying aspect ratio from Titanium alloy (Ti-6Al-4V) material — the aspect ratio l/t of the thin-walls ranges from 36 to 183 (11 mm long (constant), and 0.06 mm to 0.3 mm in thickness). These thin-wall test parts were built under three angular orientations of 0°, 60°, and 90°. Further, the parts were examined offline using X-ray computed tomography (XCT). Through the offline XCT data, the build quality of the thin-wall features in terms of their geometric integrity is quantified as a function of the aspect ratio and orientation angle, which suggests a set of design guidelines for building thin-wall structures with LPBF. To monitor the quality of the thin-wall, in-process images of the top surface of the powder bed were acquired at each layer during the build process. The optical images are correlated with the post build quantitative measurements of the thin-wall through a deep learning convolutional neural network (CNN). The statistical correlation (Pearson coefficient, ρ) between the offline XCT measured thin-wall quality, and CNN predicted measurement ranges from 80% to 98%. Consequently, the impending poor quality of a thin-wall is captured from in-situ process data.

The goal of this work is to predict the effect of part geometry and process parameters on the instantaneous spatial distribution of heat, called the heat flux or thermal history, in metal parts as they are being built layer-by-layer using additive manufacturing (AM) processes. In pursuit of this goal, the objective of this work is to develop and verify a graph theory-based approach for predicting the heat flux in metal AM parts. This objective is consequential to overcome the current poor process consistency and part quality in AM. One of the main reasons for poor part quality in metal AM processes is ascribed to the heat flux in the part. For instance, constrained heat flux because of ill-considered part design leads to defects, such as warping and thermal stress-induced cracking. Existing non-proprietary approaches to predict the heat flux in AM at the part-level predominantly use mesh-based finite element analyses that are computationally tortuous — the simulation of a few layers typically requires several hours, if not days. Hence, to alleviate these challenges in metal AM processes, there is a need for efficient computational thermal models to predict the heat flux, and thereby guide part design and selection of process parameters instead of expensive empirical testing. Compared to finite element analysis techniques, the proposed mesh-free graph theory-based approach facilitates layer-by-layer simulation of the heat flux within a few minutes on a desktop computer. To explore these assertions we conducted the following two studies: (1) comparing the heat diffusion trends predicted using the graph theory approach, with finite element analysis and analytical heat transfer calculations based on Green’s functions for an elementary cuboid geometry which is subjected to an impulse heat input in a certain part of its volume, and (2) simulating the layer-by-layer deposition of three part geometries in a laser powder bed fusion metal AM process with: (a) Goldak’s moving heat source finite element method, (b) the proposed graph theory approach, and (c) further comparing the heat flux predictions from the last two approaches with a commercial solution. From the first study we report that the heat flux trend approximated by the graph theory approach is found to be accurate within 5% of the Green’s functions-based analytical solution (in terms of the symmetric mean absolute percentage error). Results from the second study show that the heat flux trends predicted for the AM parts using graph theory approach agrees with finite element analysis with error less than 15%. More pertinently, the computational time for predicting the heat flux was significantly reduced with graph theory, for instance, in one of the AM case studies the time taken to predict the heat flux in a part was less than 3 minutes using the graph theory approach compared to over 3 hours with finite element analysis. While this paper is restricted to theoretical development and verification of the graph theory approach for heat flux prediction, our forthcoming research will focus on experimental validation through in-process sensor-based heat flux measurements.

Part design and process parameters directly influence the spatiotemporal distribution of temperature and associated heat transfer in parts made using additive manufacturing (AM) processes. The temporal evolution of temperature in AM parts is termed herein as thermal profile or thermal history. The thermal profile of the part, in turn, governs the formation of defects, such as porosity and shape distortion. Accordingly, the goal of this work is to understand the effect of the process parameters and the geometry on the thermal profile in AM parts. As a step towards this goal, the objectives of this work are two-fold: (1) to develop and apply a finite element-based framework that captures the transient thermal phenomena in the fused filament fabrication (FFF) additive manufacturing of acrylonitrile butadiene styrene (ABS) parts, and (2) validate the model-derived thermal profiles with experimental in-process measurements of the temperature trends obtained under different feed rate settings (viz., the translation velocity, also called scan speed or deposition speed, of the extruder on the FFF machine). In the specific context of FFF, this foray is the critical first-step towards understanding how and why the thermal profile directly affects the degree of bonding between adjacent roads (linear track of deposited material), which in turn determines the strength of the part, as well as, propensity to form defects, such as delamination. From the experimental validation perspective, we instrumented a Hyrel Hydra FFF machine with three non-contact infrared temperature sensors (thermocouples) located near the nozzle (extruder) of the machine. These sensors measure the surface temperature of a road as it is deposited. Test parts are printed under three different settings of feed rate, and subsequently, the temperature profiles acquired from the infrared thermocouples are juxtaposed against the model-derived temperature profiles. Comparison of the experimental and model-derived thermal profiles confirms a high-degree of correlation therein, with maximum absolute error less than 10%. This work thus presents one of the first efforts in validation of thermal profiles in FFF via in-process sensing. In our future work, we will focus on predicting defects, such as delamination and inter-road porosity based on the thermal profile.

The objective of this paper is to experimentally validate the graph-based approach that was advanced in our previous work for predicting the heat flux in metal additive manufactured parts. We realize this objective in the specific context of the directed energy deposition (DED) additive manufacturing process. Accordingly, titanium alloy (Ti6Al4V) test parts (cubes) measuring 12.7 mm × 12.7 mm × 12.7 mm were deposited using an Optomec hybrid DED system at the University of Nebraska-Lincoln (UNL). A total of six test parts were manufactured under varying process settings of laser power, material flow rate, layer thickness, scan velocity, and dwell time between layers. During the build, the temperature profiles for these test parts were acquired using a single thermocouple affixed to the substrate (also Ti6Al4V). The graph-based approach was tailored to mimic the experimental DED process conditions. The results indicate that the temperature trends predicted from the graph theoretic approach closely match the experimental data; the mean absolute percentage error between the experimental and predicted temperature trends were in the range of 6% ∼ 15%. This work thus lays the foundation for predicting distortion and the microstructure evolved in metal additive manufactured parts as a function of the heat flux. In our forthcoming research we will focus on validating the model in the context of the laser powder bed fusion process.

Aerosol jet printing (AJP) is a direct-write, additive manufacturing technique, which has emerged as the process of choice for the fabrication of a broad spectrum of electronics – such as, interconnects, sensors, transistors, electrodes, and antennae – toward consistent and uniform manufacture of flexible and hybrid electronic devices. The AJP has paved the way for rapid high-resolution device fabrication; it accommodates a wide range of ink viscosity and allows for material deposition with high placement accuracy, edge definition, and adhesion on non-planer surfaces. Despite the unique advantages and engendered strategic applications, the AJP process is intrinsically unstable and complex, prone to non-linear gradual drifts, which stem from process, machine, and metrical interactions. Consequently, real-time process monitoring and control, corroborated with physical models, is a burgeoning need.

Aerosol jet printing (AJP) is a complex process for additive electronics that is often unstable. To overcome this instability, observation while printing and control of the printing process using image-based monitoring is demonstrated. This monitoring is validated against images taken after the print and shown highly correlated and useful for the determination of printed linewidth. These images and the observed linewidth are used as input for closed-loop control of the printing process, with print speed changed in response to changes in the observed linewidth. Regression is used to relate these quantities and forms the basis of proportional and proportional integral control. Electrical test structures were printed with controlled and uncontrolled printing, and it was found that the control influenced their linewidth and electrical properties, giving improved uniformity in both size and electrical performance.

The goal of this work is to predict the effect of part geometry and process parameters on the instantaneous spatiotemporal distribution of temperature, also called the thermal field or temperature history, in metal parts as they are being built layer-by-layer using additive manufacturing (AM) processes. In pursuit of this goal, the objective of this work is to develop and verify a graph theory-based approach for predicting the temperature distribution in metal AM parts. This objective is consequential to overcome the current poor process consistency and part quality in AM. One of the main reasons for poor part quality in metal AM processes is ascribed to the nature of temperature distribution in the part. For instance, steep thermal gradients created in the part during printing leads to defects, such as warping and thermal stress-induced cracking. Existing nonproprietary approaches to predict the temperature distribution in AM parts predominantly use mesh-based finite element analyses that are computationally tortuous—the simulation of a few layers typically requires several hours, if not days. Hence, to alleviate these challenges in metal AM processes, there is a need for efficient computational models to predict the temperature distribution, and thereby guide part design and selection of process parameters instead of expensive empirical testing. Compared with finite element analyses techniques, the proposed mesh-free graph theory-based approach facilitates prediction of the temperature distribution within a few minutes on a desktop computer. To explore these assertions, we conducted the following two studies: (1) comparing the heat diffusion trends predicted using the graph theory approach with finite element analysis, and analytical heat transfer calculations based on Green’s functions for an elementary cuboid geometry which is subjected to an impulse heat input in a certain part of its volume and (2) simulating the laser powder bed fusion metal AM of three-part geometries with (a) Goldak’s moving heat source finite element method, (b) the proposed graph theory approach, and (c) further comparing the thermal trends predicted from the last two approaches with a commercial solution. From the first study, we report that the thermal trends approximated by the graph theory approach are found to be accurate within 5% of the Green’s functions-based analytical solution (in terms of the symmetric mean absolute percentage error). Results from the second study show that the thermal trends predicted for the AM parts using graph theory approach agree with finite element analyses, and the computational time for predicting the temperature distribution was significantly reduced with graph theory. For instance, for one of the AM part geometries studied, the temperature trends were predicted in less than 18 min within 10% error using the graph theory approach compared with over 180 min with finite element analyses. Although this paper is restricted to theoretical development and verification of the graph theory approach, our forthcoming research will focus on experimental validation through in-process thermal measurements.

The modern manufacturing industry faces increasing demands to customize products according to personal needs, thereby leading to the proliferation of complex designs. To cope with design complexity, manufacturing systems are increasingly equipped with advanced sensing and imaging capabilities. However, traditional statistical process control methods are not concerned with the stream of in-process imaging data. Also, very little has been done to investigate nonlinearity, irregularity, and inhomogeneity in the image stream collected from manufacturing processes. This paper presents the joint multifractal and lacunarity analysis to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics in the manufacturing process. Experimental studies show that the proposed method not only effectively characterizes surface finishes for quality control of ultraprecision machining but also provides an effective model to link process parameters with fractal characteristics of in-process images acquired from additive manufacturing. This, in turn, will allow a swift response to processes changes and consequently reduce the number of defective products. The proposed multifractal method shows strong potentials to be applied for process monitoring and control in a variety of domains such as ultraprecision machining and additive manufacturing.

It remains unclear how the visual system is able to extract affective content from complex scenes even with extremely brief (< 100 millisecond) exposures. One possibility, suggested by findings in machine vision, is that low-level features such as unlocalized, two-dimensional (2-D) Fourier spectra can be diagnostic of scene content. To determine whether Fourier image amplitude carries any information about the affective quality of scenes, we first validated the existence of image category differences through a support vector machine (SVM) model that was able to discriminate our intact aversive and neutral images with ~ 70% accuracy using amplitude-only features as inputs. This model allowed us to confirm that scenes belonging to different affective categories could be mathematically distinguished on the basis of amplitude spectra alone. The next question is whether these same features are also exploited by the human visual system. Subsequently, we tested observers’ rapid classification of affective and neutral naturalistic scenes, presented briefly (~33.3 ms) and backward masked with synthetic textures. We tested categorization accuracy across three distinct experimental conditions, using: (i) original images, (ii) images having their amplitude spectra swapped within a single affective image category (e.g., an aversive image whose amplitude spectrum has been swapped with another aversive image) or (iii) images having their amplitude spectra swapped between affective categories (e.g., an aversive image containing the amplitude spectrum of a neutral image). Despite its discriminative potential, the human visual system does not seem to use Fourier amplitude differences as the chief strategy for affectively categorizing scenes at a glance. The contribution of image amplitude to affective categorization is largely dependent on interactions with the phase spectrum, although it is impossible to completely rule out a residual role for unlocalized 2-D amplitude measures.

Extrusion-based bioprinting involves extrusion of bioinks through nozzles to create three-dimensional structures. The bioink contains living organisms with biological relevance for emerging applications such as tissue scaffolds, organs-on-a-chip, regenerative medicine, and drug delivery systems. Bioinks, which are mixtures of biomaterials and living cells, influence the quality of printed constructs through their physical, mechanical, biological, and rheological behavior. Printability is a property of a bioink used to describe its ability to create well-defined structures. Amongst all contributing factors, rheological properties and printing parameters are primary factors that influence the quality of bioprinted constructs. With the increasing popularity of extrusion bioprinting, different approaches for controlling these properties and parameters have emerged. This review highlights the role of rheology and process parameters in extrusion bioprinting and discusses qualitative and quantitative methods proposed to measure and define the printability of bioinks. Finally, an overview of key challenges and future trends in extrusion bioprinting is provided.

Laser Powder Bed Fusion (LPBF) is the predominant metal additive manufacturing technique that benefits from a significant body of academic study and industrial investment given its ability to create complex geometry parts. Despite LPBF’s widespread use, there still exists a need for process monitoring to ensure reliable part production and reduce post-build quality assessments. Towards this end, we develop and evaluate machine learning-based predictive models using height map-derived quality metrics for single tracks and the accompanying pyrometer and high-speed video camera data collected under a wide range of laser power and laser velocity settings. We extract physically intuitive low-level features representative of the meltpool dynamics from these sensing modalities and explore how these vary with the linear energy density. We find our Sequential Decision Analysis Neural Network (SeDANN) model – a scientific machine learning model that incorporates physical process insights – outperforms other purely data-driven black box models in both accuracy and speed. The general approach to data curation and adaptable nature of SeDANN’s scientifically informed architecture should benefit LPBF systems with an evolving suite of sensing modalities and post-build quality measurements.

This work addresses the following open research question: what non-contact measurement techniques and analytical approaches are required to assess the surface finish of a workpiece in situ during conventional machining without stopping the machine tool? The goal is to track variations in surface finish during conventional machining without stopping the machine tool so that quick compensatory action can be taken in case of a process drift. In pursuit of this goal, the objective of this work is non-contact, vision-based online measurement of surface finish in a conventional machining operation, such as outside diameter (OD) turning on a lathe. To realize the foregoing objective, algebraic graph theoretic image processing is used. The approach is based on converting an image of a surface into an unweighted and undirected network graph. The graph theoretic invariant, Fiedler number (λ2), is estimated, and subsequently invoked as a discriminant of workpiece surface roughness. The advantage of the proposed approach is that it eschews complex image filtering and segmentation steps. The central hypothesis is that the graph-based topological quantifier Fiedler number (λ2) estimates surface finish in a conventional machining operation with accuracy Sa ± 2 μm (arithmetic average areal surface roughness, μm) when Sa is in the range of 1-10 μm. This hypothesis is tested on conventional cylindrical (outside diameter) turning of shafts by using an optical imaging setup (CCD camera) incorporated into a lathe machine. Through statistical modeling it is demonstrated that the Fiedler number (λ2) tracks surface finish variations in situ for steel and aluminum alloy shafts (4340 and 6061 grades, respectively) in near real-time with a maximum error of approximately Sa ± 2 μm. This measurement error was verified to be within 15% of the actual measured surface finish. Tests were also carried out under three different rotational speeds (0 rpm, 45 rpm, 245 rpm); and the approach was consequently attested to be robust to rotational speeds. The computational time for estimating the surface finish from this approach was assessed to be within tenth of a second, thus validating its practical applicability.

The objective of this work is to realize real-time monitoring of process conditions in advanced manufacturing using multiple heterogeneous sensor signals. To achieve this objective we propose an approach invoking the concept of sparse estimation called online sparse estimation-based classification (OSEC). The novelty of the OSEC approach is in representing data from sensor signals as an underdetermined linear system of equations and subsequently solving the underdetermined linear system using a newly developed greedy Bayesian estimation method. We apply the OSEC approach to two advanced manufacturing scenarios, namely, a fused filament fabrication additive manufacturing process and an ultraprecision semiconductor chemical–mechanical planarization process. Using the proposed OSEC approach, process drifts are detected and classified with higher accuracy compared with popular machine learning techniques. Process drifts were detected and classified with a fidelity approaching 90% (F-score) using OSEC....

The objective of this paper is to quantify the precision of a novel approach for computational heat transfer modeling based on spectral graph theory. Two benchmark heat transfer problems with planar boundaries, for which exact analytical solutions are available, are used to determine the precision of temperature predictions obtained from spectral graph, finite difference (one-dimensional), and finite element (three-dimensional) methods. These studies show that the spectral graph approach captures the temperature trends in the benchmark case studies with error in the range of 2% to 10% depending on the location in the body. These verification studies also provide an approach to calibrate the numerical parameters in the new method. The spectral graph approach is applied for predicting the thermal history of a complex three-dimensional additive manufactured (3D printed) part. The temperature trends in a 50-layer part are computed 2.3 times faster than a commercial finite-element software package, and the results differ by less than 7.5%. Further improvements in the computational speed of the spectral graph approach are expected through code optimization and parallelization. This work has far-reaching practical implications for predicting thermal-induced defects in a variety of manufacturing processes including casting and additive manufacturing.

The objective of this work is to forward a 3D computational fluid dynamics (CFD) model to explain the aerodynamics behind aerosol transport and deposition in aerosol jet printing (AJP). The CFD model allows for: (i) mapping of velocity fields as well as particle trajectories; and (ii) investigation of post-deposition phenomena of sticking, rebounding, spreading, and splashing. The complex geometry of the deposition head was modeled in the ANSYS-Fluent environment, based on a patented design as well as accurate measurements, obtained from 3D X-ray CT imaging. The entire volume of the geometry was subsequently meshed, using a mixture of smooth and soft quadrilateral elements, with consideration of layers of inflation to obtain an accurate solution near the walls. A combined approach — based on the density-based and pressure-based Navier-Stokes formation — was adopted to obtain steady-state solutions and to bring the conservation imbalances below a specified linearization tolerance (10−6). Turbulence was modeled, using the realizable k-ε viscose model with scalable wall functions. A coupled two-phase flow model was set up to track a large number of injected particles. The boundary conditions were defined based on experimental sensor data. A single-factor factorial experiment was conducted to investigate the influence of sheath gas flow rate (ShGFR) on line morphology, and also validate the CFD model.

The objective of this work is to detect in situ the occurrence of lack-of-fusion defects in titanium alloy (Ti-6Al-4 V) parts made using directed energy deposition (DED) additive manufacturing (AM). We use data from two types of in-process sensors, namely, a spectrometer and an optical camera which are integrated into an Optomec MR-7 DED machine. Both sensors are focused on capturing the dynamic phenomena around the melt pool region. To detect lack-of-fusion defects, we fuse (combine) the data from the in-process sensors invoking the concept of Kronecker product of graphs. Subsequently, we use the features derived from the graph Kronecker product as inputs to a machine learning algorithm to predict the severity (class or level) of average length of lack-of-fusion defects within a layer, which is obtained from offline X-ray computed tomography of the test parts. We demonstrate that the severity of lack-of-fusion defects is classified with statistical fidelity (F-score) close to 85% for a two-level classification scenario, and approximately 70% for a three-level classification scenario. Accordingly, this work demonstrates the use of heterogeneous in-process sensing and online data analytics for in situ detection of defects in DED metal AM process.

The rapid development of modern science and technology brings with it a high demand for manufacturing quality. The surface integrity of a machined part is a critical factor which needs to be considered in the selection of the appropriate machining processes. Surface integrity is also tightly linked with tool wear. Tool wear is one of the most significant and necessary parameters to be considered for machining sustainability. By monitoring and predicting tool wear, it is possible to improve sustainability by reducing the scrap rate due to poor surface integrity. In this work, data-dependent systems (DDS), a stochastic modeling and analysis technique, was applied to study the power of spindle motor during a hard milling operation. The objective was to correlate the spindle power to tool wear conditions using DDS analysis. The spindle power was monitored, and the time series trends were decomposed to study the frequency variation with different severities of tool wear conditions and processing parameters. Analysis of variance (ANOVA) was also used to determine factors significant to the power by a spindle motor. Experiments indicate that low-level frequency of spindle power is correlated with the amount of tool wear, cutting speed, and feed per tooth. The results suggest that effective tool wear monitoring may be achieved by focusing on low-level frequencies highlighted by DDS methodology.

The goal of this work is to understand the effect of process conditions on lack of fusion porosity in parts made using laser powder bed fusion (LPBF) additive manufacturing (AM) process, and subsequently, to detect the onset of process conditions that lead to lack of fusion-related porosity from in-process sensor data. In pursuit of this goal, the objectives of this work are twofold: (1) quantify the count (number), size and location of pores as a function of three LPBF process parameters, namely, the hatch spacing (H), laser velocity (V), and laser power (P); and (2) monitor and identify process conditions that are liable to cause porosity through analysis of in-process layer-by-layer optical images of the build invoking multifractal and spectral graph theoretic features. These objectives are important because porosity has a significant impact on the functional integrity of LPBF parts, such as fatigue life. Furthermore, linking process conditions to defects via sensor signatures is the first step toward in-process quality assurance in LPBF. To achieve the first objective, titanium alloy (Ti–6Al–4V) test cylinders of 10 mm diameter × 25 mm height were built under differing H, V, and P settings on a commercial LPBF machine (EOS M280). The effect of these process parameters on count, size, and location of pores was quantified based on X-ray computed tomography (XCT) images. To achieve the second objective, layerwise optical images of the powder bed were acquired as the parts were being built. Spectral graph theoretic and multifractal features were extracted from the layer-by-layer images for each test part. Subsequently, these features were linked to the process parameters using machine learning approaches. Through these image-based features, process conditions under which the parts were built were identified with the statistical fidelity over 80% (F-score).

The goal of this work is in situ monitoring of the functional properties of aerosol jet-printed electronic devices. In pursuit of this goal, the objective is to develop a multiple-input, single-output (MISO) machine learning model to estimate the device functional properties in a near real-time fashion as a function of process parameters as well as 2D/3D features of line morphology. The aim is to use the MISO model for in situ estimation and thus, monitoring of line/device resistance in aerosol jet printing (AJP) process. To realize this objective, silver nanoparticle structures are printed by varying three process parameters: (i) sheath gas flow rate (ShGFR), (ii) exhaust gas flow rate (EGFR), and (iii) print speed (PS). Subsequently, line morphology is captured in situ using a high-resolution charge-coupled device (CCD) camera, mounted coaxial to the nozzle. Besides, utilizing 2D/3D quantifiers (introduced in the authors’ previous publications), the line morphology is further quantified, and the extracted features (e.g., line width, overspray, cross-sectional area, etc.) are fed as inputs to a novel sparse representation-based classification (SRC) model. The four-point probe method is used for measurement of resistance, and definition of a priori classification labels. The outcome of this research paves the way for future control of device functional properties in AJP process.

Nowadays manufacturing industry faces increasing demands to customize products according to personal needs. This trend leads to a proliferation of complex product designs. To cope with this complexity, manufacturing systems are equipped with advanced sensing capabilities. However, traditional statistical process control methods are not concerned with the stream of in-process imaging data. Also, very little has been done to investigate nonlinearity, irregularity, and inhomogeneity in image stream collected from manufacturing processes. This paper presents the multifractal spectrum and lacunarity measures to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics of the underlying manufacturing process. Experimental studies show that the proposed method not only effectively characterizes the surface finishes for quality control of ultra-precision machining but also provides an effective model to link process parameters with fractal characteristics of in-process images acquired from additive manufacturing. This, in turn, will allow a swift response to processes changes and consequently reduce the number of defective products. The proposed fractal method has strong potentials to be applied for process monitoring and control in a variety of domains such as ultra-precision machining, additive manufacturing, and biomanufacturing.

The goal of this work is to detect the onset of material cross-contamination in laser powder bed fusion (L-PBF) additive manufacturing (AM) process using data from in-situ sensors. Material cross-contamination refers to trace foreign materials that may be introduced in the powder feedstock used in the process due to reasons, such as poor cleaning of the AM machine after previous builds, or inadequate quality control during production and storage of the feedstock powder material. Material cross-contamination may lead to deleterious changes in the microstructure of the AM part and consequently affect its functional properties. Accordingly, the objective of this work is to develop and apply a spectral graph theoretic approach to detect the occurrence of material cross-contamination in real-time during the build using in-process sensor signatures, such as those acquired from a photodetector. To realize this objective Inconel alloy 625 test parts were made on a custom-built L-PBF apparatus integrated with multiple sensors, including a photodetector (300 nm to 1100 nm). During the process the powder bed was contaminated with two types of foreign materials, namely, tungsten and aluminum powders under varying degrees of severity. Offline X-ray Computed Tomography (XCT) and metallurgical analyses indicated that contaminant particles may cascade to over eight subsequent layers of the build, and enter up to three previously deposited layers. This research takes the first-step towards detecting cross-contamination in AM by tracking the process signatures from the photodetector sensor hatch-by-hatch invoking spectral graph transform coefficients. These coefficients are subsequently traced on a Hoteling T2 statistical control chart. Using this approach, instances of Type II statistical error in detecting the onset of material cross-contamination was 5% in the case of aluminum, in contrast, traditional stochastic time series modeling approaches, e.g., ARMA had corresponding error exceeding 15%.

The goal of this work is to understand the effect of process conditions on part porosity in laser powder bed fusion (LPBF) Additive Manufacturing (AM) process, and subsequently, detect the onset of process conditions that lead to porosity from in-process sensor data. In pursuit of this goal, the objectives of this work are two-fold: (1) Quantify the count (number), size and location of pores as a function of three LPBF process parameters, namely, the hatch spacing (H), laser velocity (V), and laser power (P). (2) Monitor and identify process conditions that are liable to cause porosity through analysis of in-process layer-by-layer optical images of the build invoking multifractal and spectral graph theoretic features. This is important because porosity has a significant impact on the functional integrity of LPBF parts, such as fatigue life. Furthermore, linking process conditions to sensor signatures and defects is the first-step towards in-process quality assurance in LPBF. To achieve the first objective, titanium alloy (Ti-6Al-4V) test cylinders of 10 mm diameter × 25 mm height were built under differing H, V, and P settings on a commercial LPBF machine (EOS M280). The effect of these parameters on count, size and location of pores was quantified based on X-ray computed tomography (XCT) images. To achieve the second objective, layerwise optical images of the powder bed were acquired as the parts were being built. Spectral graph theoretic and multifractal features were extracted from the layer-by-layer images for each test part. Subsequently, these features were linked to the process parameters using machine learning approaches. Through these image-based features, process conditions under which the parts were built was identified with the statistical fidelity over 80% (F-score).

The rapid development of modern science and technology brings with it a high demand for manufacturing quality. The surface integrity of a machined part is a critical factor which needs to be considered in the selection of the appropriate machining processes. By monitoring and predicting tool wear, it is possible to improve sustainability by reducing the scrap rate due to poor surface integrity. In this work, Data Dependent Systems (DDS), a stochastic modeling and analysis technique, was applied to study spindle motor energy consumption during a hard milling operation. The objective was to correlate the spindle power to tool wear conditions using DDS analysis. The spindle power was monitored and the time series trends were decomposed to study the frequency variation with different severities of tool wear conditions and processing parameters. Analysis of Variance (ANOVA) was also used to determine factors significant to the energy consumption by a spindle motor. Experiments indicate that low-level frequency of spindle power is correlated with the amount of tool wear, cutting speed, and feed per tooth. Results suggest that effective tool wear monitoring may be achieved by focusing on low-level frequencies (0.1 rad/sec) highlighted by DDS methodology.

With the advances in automation technologies, data science, process modeling and process control, industries worldwide are at the precipice of what is described as the fourth industrial revolution (Industry 4.0). This term was coined in 2011 by the German federal government to define their strategy related to high tech industry [1], specifically multidisciplinary sciences involving physics-based process modeling, data science and machine learning, cyber-physical systems, and cloud computing coming together to drive operational excellence and support sustainable manufacturing. The boundaries between Information Technologies (I.T.) and Operation Technologies (O.T.) are quickly dissolving and the opportunities for taking lab-scale manufacturing science research to plant and enterprise wide deployment are better than ever before. There are still questions to be answered, such as those related to the future of manufacturing research and those related to meeting such demands with a highly skilled workforce. Furthermore, in this new environment it is important to understand how process modeling, monitoring, and control technologies will be transformed. The aim of the paper is to provide state-of-the-art review of Smart Manufacturing and Industry 4.0 within scope of process monitoring, modeling and control. This will be accomplished by giving comprehensive background review and discussing application of smart manufacturing framework to conventional (machining) and advanced (additive) manufacturing process case studies. By focusing on process modeling, monitoring, analytics, and control within the larger vision of Industry 4.0, this paper will provide a directed look at the efforts in these areas, and identify future research directions that would accelerate the pace of implementation in advanced manufacturing industry.