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There is an urgent need for developing collaborative process-defect modeling in metal-based additive manufacturing (AM). This mainly stems from the high volume of training data needed to develop reliable machine learning models for in-situ anomaly detection. The requirements for large data are especially challenging for small-to-medium manufacturers (SMMs), for whom collecting copious amounts of data is usually cost prohibitive. The objective of this research is to develop a secured data sharing mechanism for directed energy deposition (DED) based AM without disclosing product design information, facilitating secured data aggregation for collaborative modeling. However, one major obstacle is the privacy concerns that arise from data sharing, since AM process data contain confidential design information, such as the printing path. The proposed adaptive design de-identification for additive manufacturing (ADDAM) methodology integrates AM process knowledge into an adaptive de-identification procedure to mask the printing trajectory information in metal-based AM thermal history, which otherwise discloses substantial printing path information. This adaptive approach applies a flexible data privacy level to each thermal image based on its similarity with the other images, facilitating better data utility preservation while protecting data privacy. A real-world case study was used to validate the proposed method based on the fabrication of two cylindrical parts using a DED process. These results are expressed as a Pareto optimal solution, demonstrating significant improvements in privacy gain and minimal utility loss. The proposed method can facilitate privacy improvements of up to 30% with as little as 0% losses in dataset utility after de-identification. [ABSTRACT FROM AUTHOR]

Multistage manufacturing processes (MMPs) usually exhibit an interactive relationship between tool wear and product quality degradation. On one hand, the tool wear in a stage may result in the quality degradation of the products fabricated on that stage. On the other hand, the quality degradation at a preceding stage may lead to the change of the operational condition and thus affect the tool wear in subsequent stages. This interaction needs to be considered to accurately predict the residual life distribution (RLD) of MMPs, which will benefit condition-based maintenance and tool inventory management. In this paper, we propose an interaction model that utilizes a linear model to represent the impact of tool wear on quality degradation and a stochastic differential equation model to capture the impact of quality degradation on the instantaneous rate of tool wear. We then propose a Bayesian framework that incorporates real-time quality measurements to online update the RLD of MMPs. Our methodology is a generalization of an existing “QR-chain model,” which is dedicated into a similar research and application area. We conduct numerical studies to test the performance of our methodology and compare with the QR-chain model. The results show that our methodology outperforms the QR-chain model through capturing the impact of quality degradation on the process of tool wear and incorporating real-time quality measurements. [ABSTRACT FROM AUTHOR]

Manufacturing parts with target properties and quality in Laser-Based Additive Manufacturing (LBAM) is crucial toward enhancing the "trustworthiness" of this emerging technology and pushing it into the mainstream. Most of the existing LBAM studies do not use a systematic approach to optimize process parameters (e.g., laser power, laser velocity, layer thickness, etc.) for desired part properties. We propose a novel process optimizationmethod that directly utilizes experimental data from previous studies as the initial experimental data to guide the sequential optimization experiments of the current study. This serves to reduce the total number of time- and cost-intensive experiments needed. We verify our method and test its performance via comprehensive simulation studies that test various types of prior data. The results show that our method significantly reduces the number of optimization experiments, compared with conventional optimization methods. We also conduct a real-world case study that optimizes the relative density of parts manufactured using a Selective LaserMelting system. A combination of optimal process parameters is achieved within five experiments. [ABSTRACT FROM AUTHOR]

This article presents a methodology for modeling degradation signals from components functioning under dynamically evolving environment conditions.In situsensor signals related to the degradation process are utilized as well as the environment conditions, to predict and update, in real-time, the distribution of a component’s residual lifetime. The model assumes that the time-dependent rate at which a component’s degradation signal increases (or decreases) is affected by the severity of the current environmental or operational conditions. These conditions are assumed to evolve as a continuous-time Markov chain. Unique to the proposed model is the union of historical data with real-time, sensor-based data to update the signal parameters, environment parameters, and the residual lifetime distribution of the component within a Bayesian framework. [ABSTRACT FROM PUBLISHER]

Many conventional models that characterize the reliability of multi-component systems are developed on the premise that component failures in a system are independent. By contrast, this article offers a unique perspective on modeling component interdependencies and predicting their residual lifetimes. Specifically, the article provides a stochastic modeling framework for characterizing interactions among the degradation processes of interdependent components of a given system. This is achieved by modeling the behaviors of condition-/degradation-based sensor signals that are associated with each component. The proposed model is also used to estimate the residual lifetime distributions of each component. In addition, a Bayesian framework is used to update the predicted residual lifetime distributions using sensor signals that are correlated with the real-time dynamics associated with the interactions. The robustness and prediction accuracy of the methodology are investigated through a comprehensive simulation study that compares the performance of the proposed model to a counterpart benchmark that does not account for degradation interactions. [ABSTRACT FROM PUBLISHER]

We present a stochastic modeling framework for sensor-based degradation signals that predicts, in real time, the residual lifetime of individual components subjected to a time-varying environment. We investigate the future environmental profile that is deterministic and evolves continuously. Unique to our model is the union of historical data with real-time sensor-based data to update the degradation model and the residual life distribution (RLD) of the component within a Bayesian framework. The performance of our model is evaluated on the basis of degradation signals from both numerical experiments and a case study using real bearing data. The results show that our approach accurately estimates the RLD by incorporating the environmental effects and utilizing the real-time observations. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 6: 260-270, 2013 [ABSTRACT FROM AUTHOR]

This article considers systems that degrade gradually and whose degradation can be monitored using sensor technology. Different degradation modeling techniques, such as the Brownian motion process, gamma process, and random coefficients models, have been used to model the evolution of sensor-based degradation signals with the goal of estimating lifetime distributions of various engineering systems. A parametric stochastic degradation modeling approach to estimate the Residual Life Distributions (RLDs) of systems/components that are operating in the field is presented. The proposed methodology rests on the idea of utilizing in situ degradations signals, communicated from fielded components, to update their respective RLDs in real time. Given the observed partial degradation signals, RLDs are evaluated based on a first-passage time approach. Expressions for the first-passage time for a base case linear degradation model, in which the degradation signal evolves as a Brownian motion, are derived. The model is tested using simulated and real-world degradation signals from a rotating machinery application. [ABSTRACT FROM PUBLISHER]

Background: Universities are at risk for COVID-19 and Fall semester begins in August 2020 for most campuses in the United States. The Southern States, including Mississippi, are experiencing a high incidence of COVID-19. Aims: The objective of this study is to model the impact of face masks and hybrid learning on the COVID-19 epidemic on Mississippi State University (MSU) campus. Methods: We used an age structured deterministic mathematical model of COVID-19 transmission within the MSU campus population, accounting for asymptomatic transmission. We modeled facemasks for the campus population at varying proportions of mask use and effectiveness, and Hyflex model of partial online learning with reduction of people on campus. Results: Facemasks can substantially reduce cases and deaths, even with modest effectiveness. Even 20% uptake of masks will halve the epidemic size. Facemasks combined with Hyflex reduces epidemic size even more. Conclusions: Universal use of face masks and reducing the number of people on campus may allow safer universities reopening. [ABSTRACT FROM AUTHOR]

The process uncertainty induced quality issue remains the major challenge that hinders the wider adoption of additive manufacturing (AM) technology. The defects occurred significantly compromise structural integrity and mechanical properties of fabricated parts. Therefore, there is an urgent need in fast, yet reliable AM component certification. Most finite element analysis related methods characterize defects based on the thermomechanical relationships, which are computationally inefficient and cannot capture process uncertainty. In addition, there is a growing trend in data-driven approaches on characterizing the empirical relationships between thermal history and anomaly occurrences, which focus on modeling an individual image basis to identify local defects. Despite their effectiveness in local anomaly detection, these methods are quite cumbersome when applied to layer-wise anomaly detection. This paper proposes a novel in situ layer-wise anomaly detection method by analyzing the layer-by-layer morphological dynamics of melt pools and heat affected zones (HAZs). Specifically, the thermal images are first preprocessed based on the g-code to assure unified orientation. Subsequently, the melt pool and HAZ are segmented, and the global and morphological transition metrics are developed to characterize the morphological dynamics. New layer-wise features are extracted, and supervised machine learning methods are applied for layer-wise anomaly detection. The proposed method is validated using the directed energy deposition (DED) process, which demonstrates superior performance comparing with the benchmark methods. The average computational time is significantly shorter than the average build time, enabling in situ layer-wise certification and real-time process control. [ABSTRACT FROM AUTHOR]

Background: Diagnosis and treatment management for head and neck squamous cell carcinoma (HNSCC) is guided by routine diagnostic head and neck computed tomography (CT) scans to identify tumor and lymph node features. The extracapsular extension (ECE) is a strong predictor of patients' survival outcomes with HNSCC. It is essential to detect the occurrence of ECE as it changes staging and treatment planning for patients. Current clinical ECE detection relies on visual identification and pathologic confirmation conducted by clinicians. However, manual annotation of the lymph node region is a required data preprocessing step in most of the current machine learning‐based ECE diagnosis studies. Purpose: In this paper, we propose a Gradient Mapping Guided Explainable Network (GMGENet) framework to perform ECE identification automatically without requiring annotated lymph node region information. Methods: The gradient‐weighted class activation mapping (Grad‐CAM) technique is applied to guide the deep learning algorithm to focus on the regions that are highly related to ECE. The proposed framework includes an extractor and a classifier. In a joint training process, informative volumes of interest (VOIs) are extracted by the extractor without labeled lymph node region information, and the classifier learns the pattern to classify the extracted VOIs into ECE positive and negative. Results: In evaluation, the proposed methods are well‐trained and tested using cross‐validation. GMGENet achieved test accuracy and area under the curve (AUC) of 92.2% and 89.3%, respectively. GMGENetV2 achieved 90.3% accuracy and 91.7% AUC in the test. The results were compared with different existing models and further confirmed and explained by generating ECE probability heatmaps via a Grad‐CAM technique. The presence or absence of ECE has been analyzed and correlated with ground truth histopathological findings. Conclusions: The proposed deep network can learn meaningful patterns to identify ECE without providing lymph node contours. The introduced ECE heatmaps will contribute to the clinical implementations of the proposed model and reveal unknown features to radiologists. The outcome of this study is expected to promote the implementation of explainable artificial intelligence‐assiste ECE detection. [ABSTRACT FROM AUTHOR]

This study presents a two-stage stochastic programming model for the design and management of a biomass co-firing supply chain network under feedstock supply uncertainty. To represent a more realistic case, we generate scenarios from prediction errors of the historical and forecasted biomass supply availabilities. We solve the model using a hybrid decomposition algorithm that combines Sample average approximation with an enhanced Progressive hedging algorithm. The proposed algorithm is validated via a real-world case study using data from Mississippi and Alabama. Computational results indicate that the proposed algorithm is capable of producing high quality solutions in a reasonable amount of time. [ABSTRACT FROM AUTHOR]

Real-time health condition monitoring of bearings plays a significant role in the functionality of the rotary machinery. Multi-channel sensor fusion can be more robust for identifying diverse bearing fault diagnosis scenarios. However, the high-dimensional data and complex fault scenarios that can occur in the system pose significant challenges for effective fault diagnosis. State-of-the-art artificial intelligence-based bearing fault diagnosis system involves multi-channel sensor fusion, which usually leverages time–frequency analysis, feature extraction, and supervised learning. Nevertheless, those methods usually require a large training dataset for the machine learning model development. This paper proposes a new multi-channel sensor fusion methodology, named frequency-domain multilinear principal component analysis (FDMPCA), by integrating acoustics and vibration signals with different sampling rates and limited training data. Frequency analysis is firstly leveraged to transform the original signals from time to frequency domain, and the frequency responses of heterogeneous channels form a tensor structure named the frequency-domain (FD) tensor. Subsequently, the FD tensor is decomposed by multilinear principal component analysis (MPCA), resulting in low-dimensional process features for fault diagnosis. Finally, the extracted features can be used to train a Neural Network (NN) model for fault diagnosis. To validate the effectiveness of the proposed method, the bearing fault experiments were conducted on a machinery fault simulator while multiple vibration and acoustic signals were collected. Experimental results demonstrated that the proposed approach can effectively identify the machine fault conditions and outperform the benchmark methods given the limited training data. [ABSTRACT FROM AUTHOR]

The objective of this research is to study an effective thermal data stream prediction method for additive manufacturing (AM) processes using thermal image streams in a layer-wise manner. Reliable physics-based models have been developed to delineate the underlying thermomechanical dynamics of AM processes. However, the computational cost is extremely high. We proposed a tensor-based surrogate modeling methodology to predict the layer-wise relationship in thermal data stream of the AM parts, which is time efficient compared to available physics-based prediction models. We constructed a network tensor structure for freeform shapes based on thermal image streams obtained in metal-based AM processes. Then, we simplified the network tensor structure by concatenating images to reach a layer-wise structure. Subsequent layers were predicted based on the antecedent layer using the tensor regression model. A generalized multilinear structure, called the higher order partial least squares (HOPLS), was used to estimate the tensor regression model parameters. Through the proposed method, high-dimensional thermal histories of AM components were predicted accurately in a computationally efficient manner. Prediction performance indices (i.e., Q² = 0.999 and root-mean-square errors of prediction (RMSEP) = 31.212 °C) demonstrated a significantly more efficient layer-wise prediction of thermal data stream--a larger Q² (0 = Q² = 1) and a smaller RMSEP indicated a better prediction performance. The proposed thermal data stream prediction was validated on simulated thermal images from finite element (FE) simulations. [ABSTRACT FROM AUTHOR]

Laser-based additive manufacturing (LBAM) provides unrivalled design freedom with the ability to manufacture complicated parts for a wide range of engineering applications. Melt pool is one of the most important signatures in LBAM and is indicative of process anomalies and part defects. High-speed thermal images of the melt pool captured during LBAM make it possible for in situ melt pool monitoring and porosity prediction. This paper aims to broaden current knowledge of the underlying relationship between process and porosity in LBAM and provide new possibilities for efficient and accurate porosity prediction. We present a deep learning-based data fusion method to predict porosity in LBAM parts by leveraging the measured melt pool thermal history and two newly created deep learning neural networks. A PyroNet, based on Convolutional Neural Networks, is developed to correlate in-process pyrometry images with layer-wise porosity; an IRNet, based on Long-term Recurrent Convolutional Networks, is developed to correlate sequential thermal images from an infrared camera with layer-wise porosity. Predictions from PyroNet and IRNet are fused at the decision-level to obtain a more accurate prediction of layer-wise porosity. The model fidelity is validated with LBAM Ti-6Al-4V thin-wall structure. This is the first work that manages to fuse pyrometer data and infrared camera data for metal additive manufacturing (AM). The case study results based on benchmark datasets show that our method can achieve high accuracy with relatively high efficiency, demonstrating the applicability of the method for in situ porosity detection in LBAM. [ABSTRACT FROM AUTHOR]

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 during the process by combining predictions from a physical model (simulation) with in-situ sensor signatures in a machine learning framework. We hypothesize that flaws in AM parts are detected with significantly higher statistical fidelity (F-score) when both in-situ sensor data and theoretical predictions are pooled together in a machine learning model, compared to an approach that is based exclusively on machine learning of sensor data (black-box model) or physics-based predictions (white-box model). We test the hypothesized efficacy of such a gray-box model or digital twin approach in the context of the laser powder bed fusion (LPBF) and directed energy deposition (DED) AM processes. For example, in the DED process, we first predicted the instantaneous spatiotemporal distribution of temperature in a thin-wall titanium alloy part using a computational heat transfer model based on graph theory. Subsequently, we combined the preceding physics-derived thermal trends with in-situ temperature measurements obtained from a pyrometer in a readily implemented supervised machine learning framework (support vector machine). We demonstrate that the integration of temperature predictions from an ab initio heat transfer model and in-situ sensor data is capable of detecting flaws in the DED-produced thin-wall part with F-score approaching 90%. By contrast, the F-score decreases to nearly 80% when either temperature measurements from the in-situ sensor or temperature distribution predictions from the theoretical model are used alone by themselves. This work thus demonstrates an early foray into the digital twin paradigm for real-time process monitoring in AM via seamless integration of physics-based modeling (simulation), in-situ sensing, and data analytics (machine learning). [ABSTRACT FROM AUTHOR]

Distortion in laser-based additive manufacturing (LBAM) is a critical issue that adversely affects the geometric integrity of additively manufactured parts and generally exhibits a complicated dependence on the underlying material. The differences in properties between distinct materials prevent the immediate application of a distortion model learned for one material to another, which introduces the challenge in LBAM of learning a distortion model for a new material system given past experiments. Current methods for investigating the distortion of different material systems typically involve finite element analysis or a large number of experiments in an empirical study. However, these methods do not learn from previous experiments and can incur significant costs in terms of computation, time, or resources. We propose a Bayesian model transfer methodology that is both physics-based and data-driven to leverage past experiments on previously studied material systems for more efficient distortion modeling of new systems. This method transfers distortion models across distinct materials based on the statistical effect equivalence framework by formulating the differences between two materials as a lurking variable. Our method reduces the experimentation and effort needed for specifying distortion models for new material systems. We validate our methodology in a case study of distortion model transfer from Ti-6Al-4V disks to 316L stainless steel disks. This case study is the first instance of model transfer between material systems and illustrates the ability of the Bayesian model transfer methodology to address the issue of comprehensive distortion modeling across varying material systems in LBAM. [ABSTRACT FROM AUTHOR]

In this study, the crack‐growth approach is used to predict the fatigue life of 17‐4 precipitation hardening (PH) stainless steel (SS) fabricated via an additive manufacturing (AM) system in different orientations (ie, vertical and horizontal) before and after heat treatment. To perform fatigue‐life calculations, the effective stress intensity factor as a function of crack‐growth rate was obtained from testing modified compact specimens with different crack orientations in as‐built and heat‐treated conditions. The plasticity‐induced crack closure model, FASTRAN, was used to calculate fatigue lives based on the size of process‐induced defects. Results indicated that in the presence of large voids (ie, lack‐of‐fusion defects), the total fatigue life of AM 17‐4 PH SS in as‐built and heat‐treated conditions is dominated by crack growth. Effect of build orientation on fatigue life of AM 17‐4 PH SS was also captured based on the size of defects projected on a plane perpendicular to the loading direction. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) is a novel fabrication technique capable of producing highly complex parts. Nevertheless, a major challenge is the quality assurance of the AM fabricated parts. While there are several ways of approaching this problem, how to develop informative process signatures to detect part anomalies for quality control is still an open question. The objective of this study is to build a new layer-wise process signature model to characterize the thermal-defect relationship. Based on melt pool images, we propose novel layer-wise key process signatures, which are calculated using multilinear principal component analysis (MPCA) and are directly correlated with the layer-wise quality of the part. The resultant layer-wise quality features can be used to predict the overall defect distribution of a fabricated layer during the build. The proposed model is validated through a case study based on a direct laser deposition experiment, where the layer-wise quality of the part is predicted on the fly. The accuracy of prediction is calculated using three measures (i.e., recall, precision, and F-score), showing reasonable success of the proposed methodology in predicting layer-wise quality. The proposed quality prediction methodology enables online process correction to eliminate anomalies and to ultimately improve the quality of the fabricated parts. [ABSTRACT FROM AUTHOR]

This study proposes a novel optimization framework that simultaneously considers interdependence of flow networks, resource restrictions, and process-and-system level costs under a unified decision framework for the design and management of an integrated Additive Manufacturing (AM) supply chain network. A two-stage stochastic programming model is proposed that minimizes the facility location and capacity selection decisions at the first-stage prior to realizing any customer demand information. However, after the demand information is revealed, a number of second-stage decisions, such as optimal layer thickness for AM products, production, post-processing, procurement, storage, and transportation decisions, are made. Due to the need to solve our proposed optimization framework in a realistic-size network problem, a hybrid decomposition algorithm, combining the Sample Average Approximation algorithm with an Adaptive Large Neighborhood Search algorithm, is proposed. The performance of the proposed algorithm is validated by developing a case study using data from Alabama and Mississippi. Based on a set of numerical experiments, the effect of process-and-system level factors on the design and management of an AM supply chain network are analyzed. Numerous managerial insights, particularly on layer thickness, customer demand variability, mean demand variation, powder safety stock, and wastage rate on overall system performance, are gained which are crucial for the sustainment of this new manufacturing and supply chain paradigm. [ABSTRACT FROM AUTHOR]

Highlights • A state-space model based method is proposed to predict RUL under time-varying operating conditions. • Two factors are analyzed separately: changes in degradation rate and jumps in degradation signals. • A time-scale transformation is applied to a Wiener process to describe the time-varying degradation rates. • A signal transformation function is applied to smooth the jumps in degradation signals. Abstract The growth of the Industrial Internet of Things (IIoT) has generated a renewed emphasis on research of prognostic degradation modeling whereby degradation signals, such as vibration signals, temperature and acoustic emissions, are used to estimate the state-of-health and predict the remaining useful life (RUL). Besides the inherent system state, external operating conditions, such as the rotational speed and load also play a significant role in the behavior of degradation signals. Time-varying operating conditions often cause two major effects on the degradation signals. First, they change the degradation rate of systems. Second, they cause signal jumps at condition change-points. These two factors make RUL prediction more difficult under time-varying operating conditions. This paper proposes a RUL prediction method by introducing these two factors into a state-space model. Changes in the degradation rate are introduced into a state transition function, and jumps in the degradation signals are introduced into a measurement function. The separate analysis of these two factors makes it possible to distinguish their own contributions to RUL prediction, thus avoiding false alarms and improving the prediction accuracy. The effectiveness of the proposed method is demonstrated using both a simulation study and an accelerated degradation test of rolling element bearings. [ABSTRACT FROM AUTHOR]

One major challenge of implementing Directed Energy Deposition (DED) Additive Manufacturing (AM) for production is the lack of understanding of its underlying process–structure–property relationship. Parts manufactured using the DED technologies may be too inconsistent and unreliable to meet the stringent requirements for many industrial applications. The objective of this research is to characterize the underlying thermo-physical dynamics of the DED process, captured by melt pool signals, and predict porosity during the build. Herein we propose a novel porosity prediction method based on the temperature distribution of the top surface of the melt pool as an AM part is being built. Self-Organizing Maps (SOMs) are then used to further analyze the two-dimensional melt pool image streams to identify similar and dissimilar melt pools. X-ray tomography is used to experimentally locate porosity within the Ti-6Al-4V thin-wall specimen, which is then compared with predicted porosity locations based on the melt pool analysis. Results show that the proposed method based on the temperature distribution of the melt pool is able to predict the location of porosity almost 96% of the time when the appropriate SOM model using a thermal profile is selected. Results are also compared with a previous study, that focuses only on the shape and size of the melt pool. We find that the incorporation of thermal distribution significantly improves the accuracy of porosity prediction. The significance of the proposed methodology based on the melt pool profiles is that this can lead the way toward in situ monitoring and minimize or even eliminate pores within the AM parts. [ABSTRACT FROM AUTHOR]

This research presents a two-stage stochastic programming model that is used to design and manage a biomass co-firing supply chain network under feedstock supply uncertainty. The model we propose extends current models by taking congestion effects into account. A non-linear cost term is added in the objective function representing the congestion factor which increases exponentially as flow of biomass approaches the capacity of multi-modal facility. We first linearize the model and then use a nested decomposition algorithm to obtain a feasible solution in a reasonable amount of time. The nested decomposition algorithm that we propose combine constraint Generation algorithm with a sample average approximation and Progressive Hedging (PH) algorithm. We apply some heuristics such as rolling horizon algorithm and variable fixing technique to enhance the performance of the PH algorithm. We develop a case study using data from the states of Mississippi and Alabama and use those regions to test and validate the performance of the proposed algorithm. Numerical experiments show that the proposed algorithm can solve large-scale problems with a larger number of scenarios and time periods to a near optimal solution in a reasonable amount of time. Results obtained from the experiments reveal that the delivery cost increases and less hubs with higher capacity are selected if we take congestion cost into account. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) refers to a group of manufacturing techniques that produce components in a layer-by-layer fashion. By virtue of its capability to produce parts with complex geometry and functionally graded materials, AM has been introduced and used in multiple industrial sectors and is leading the way into the “third industrial revolution”. One of the key considerations in Additively Manufactured Products (AMP) is the issue of sustainability which attracts increasing attention as modern society becomes more aware of environmental concerns. As a response to the growing requirement for sustainability, industries and customers are inclined to assess products and services through the perspective of life cycle sustainability. The integration of life cycle sustainability and AMP is still an open question and is the primary motivation for this study. Since the eventual goal of all advanced technologies is to serve and satisfy human society, social sustainability must be an essential part of all decisions regarding AMP. Limited studies that integrate social sustainability into AMP life cycle analysis is a current research gap in the body of the literature, and thus this paper will address it. The primary merit of this paper is developing a suitable full-dimension sustainability life cycle assessment framework to obtain a fundamental understanding of the performance of AMP life cycle sustainability. The proposed framework applies a more systemic AMP life span including AMP design, AM, usage, and end-of-life (EOL) handling. An AM produced gear is used as an example to illustrate the life cycle sustainability assessment framework. The results show that the AM stage has the highest influence on economic and environmental dimensions of sustainability and the EOL stage has the highest social impact. The results also indicate that AM mass production can significantly reduce the impact of sustainability. [ABSTRACT FROM AUTHOR]

The goal of this work is to minimize geometric inaccuracies in parts printed using a fused filament fabrication (FFF) additive manufacturing (AM) process by optimizing the process parameters settings. This is a challenging proposition, because it is often difficult to satisfy the various specified geometric accuracy requirements by using the process parameters as the controlling factor. To overcome this challenge, the objective of this work is to develop and apply a multi-objective optimization approach to find the process parameters minimizing the overall geometric inaccuracies by balancing multiple requirements. The central hypothesis is that formulating such a multi-objective optimization problem as a series of simpler single-objective problems leads to optimal process conditions minimizing the overall geometric inaccuracy of AM parts with fewer trials compared to the traditional design of experiments (DOE) approaches. The proposed multi-objective accelerated process optimization (m-APO) method accelerates the optimization process by jointly solving the subproblems in a systematic manner. The m-APO maps and scales experimental data from previous subproblems to guide remaining subproblems that improve the solutions while reducing the number of experiments required. The presented hypothesis is tested with experimental data from the FFF AM process; the m-APO reduces the number of FFF trials by 20% for obtaining parts with the least geometric inaccuracies compared to full factorial DOE method. Furthermore, a series of studies conducted on synthetic responses affirmed the effectiveness of the proposed m-APO approach in more challenging scenarios evocative of large and nonconvex objective spaces. This outcome directly leads to minimization of expensive experimental trials in AM. [ABSTRACT FROM AUTHOR]

A laser powder bed fusion (L-PBF) additive manufacturing (AM) method was employed for fabricating a multi-layered, Ti-6Al-4V oscillating heat pipe (ML-OHP). The 50.8 × 38.1 × 15.75 mm 3 ML-OHP consisted of four inter-connected layers of circular mini-channels, as well an integrated, hermetic-grade fill port. A series of experiments were conducted to characterize the ML-OHP thermal performance by varying power input (up to 50 W), working fluid (water, acetone, Novec™ 7200, and n-pentane), and operating orientation (vertical bottom-heating, horizontal, and vertical top-heating). The ML-OHP was found to operate effectively for all working fluids and orientations investigated, demonstrating that the OHP can function in a multi-layered form, and further indicating that one can ‘stack’ multiple, interconnected OHPs within flat media for increased thermal management. The ML-OHP evaporator size was found to depend on the layer-wise heat penetration which subsequently depends on power input and the ML-OHP design and material selection. Using neutron radiography, electron scanning microscopy and surface metrology, the ML-OHP channel structure was characterized and found to possess sintered Ti-6Al-4V powder along its periphery. The sintered channel surface, although a byproduct of the L-PBF manufacturing process, was found to behave as a secondary wicking structure for enhanced capillary pumping and wall/fluid heat transfer within the OHP. With the newfound capabilities of AM, many high heat flux thermal management devices, specifically those that employ mini- or micro-channels, can be ‘re-invented’ to possess embedded channels with atypical geometries, arrangements and surface conditions. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to understand the effect of four different factors: building orientation, heat treatment (solution annealing and aging), thermal history and process parameters on the mechanical properties and microstructural features of 17-4 precipitation hardening (PH) stainless steel (SS) parts produced using selective laser melting (SLM).Design/methodology/approach Various sets of test samples were built on a ProX 100™ SLM system under argon environment. Characterization studies were conducted using mechanical tensile and compression test, microhardness test, optical microscopy, X-ray diffraction and scanning electron microscopy.Findings Results indicate that building orientation has a direct effect on the mechanical properties of SLM parts, as vertically built samples exhibit lower yield and tensile strengths and elongation to failure. Post-SLM heat treatment proved to have positive effects on part strength and hardness, but it resulted in reduced ductility. Longer inter-layer time intervals between the melting of successive layers allow for higher austenite content because of lower cooling rates, thus decreasing material hardness. On the other hand, tensile properties such as elongation to failure, yield strength and tensile strength were not significantly affected by the change in inter-layer time intervals. Similar to other AM processes, SLM process parameters were shown to be instrumental in achieving desirable part properties. It is shown that without careful setting of process parameters, parts with defects (porosity and unmelted powder particles) can be produced.Originality/value Although the manufacturing of 17-4 PH SS using SLM has been investigated in the literature, the paper provides the first comprehensive study on the effect of different factors on mechanical properties and microstructure of SLM 17-4 PH. Optimizing process parameters and using heat treatment are shown to improve the properties of the part. [ABSTRACT FROM AUTHOR]

The effects of building orientation and post-fabrication heat treatment (solution annealing plus peak-aging) on fully-reversed strain-controlled fatigue behavior of 17-4 precipitation hardening (PH) stainless steel (SS) fabricated via Selective Laser Melting (SLM) is investigated. For this particular alloy, post-SLM heat treatment was found to be necessary in order to improve its tensile strength and fatigue behavior in low cycle fatigue (LCF), where the effects of microstructural impurities are less pronounced. However, the selected heat treatment was found to have detrimental influence on the SLM 17-4 PH SS high cycle fatigue (HCF) performance. The heat treatment resulted in precipitation hardening, allowing the SLM parts to become more sensitive to impurities in HCF, where the crack initiation stage dominates the total fatigue lifetime. Building orientation played a significant role on fatigue behavior in both LCF and HCF, owing to the relative orientation of deposited layers with respect to applied load. Un-melted regions (i.e. inter-layer cavities/voids), resulting from insufficient fusion or low laser penetration depth, were found to be the most detrimental type of defects on fatigue strength of SLM 17-4 PH SS due to their relatively large size and irregular shape. These specific regions that formed during fabrication of vertically-orientated samples were more detrimental than those of horizontally-built ones as they provided more stress concentration under loading, leading to lower fatigue strength. Although the process parameters were optimized based on maximizing density, the results of this study imply that this criterion is not sufficient for improving fatigue behavior, as the split-shaped un-melted regions, containing a large area with small volume, cannot be taken into account through conventional density measurements. [ABSTRACT FROM AUTHOR]

• Effects of heat treatment and crack orientation on FCG behavior of AM 17-4 PH SS. • FCG test results for AM 17-4 PH SS over a wide range of stress ratios and rates. • New methods to generate more accurate crack-growth rates in the near-threshold regime. • Microstructural characterization and failure mechanism of AM 17-4 PH SS. The effects of heat treatment and crack orientation on fatigue-crack-growth (FCG) behavior of 17-4 precipitation hardening (PH) stainless steel (SS) fabricated via a laser powder bed fusion (LPBF) additive manufacturing (AM) system were investigated. Accurate representation of FCG thresholds, the region defining crack growth as either very slow or nonexistent, is extremely important in adoption of AM for structural applications. In this study, new methods called compression pre-cracking constant-amplitude (CPCA) and load-reduction (CPLR) were used to generate FCG rate data in the near-threshold (low-rate) regime using non-standard or modified compact specimens. For comparison, the current ASTM load-reduction (LR) method was also used. Results indicate that FCG behavior of AM 17-4 PH SS in the Paris regime was comparable to that of wrought 17-4 PH SS in the peak-age condition. Although the post-manufacturing heat treatment was found to be necessary in order to improve tensile strength of this alloy, the results of this study indicate that this particular heat treatment process had no influence on FCG behavior of LPBF 17-4 PH SS. The effect of crack orientation with respect to the build direction however was evident. FCG rates were slightly lower for specimens with a crack parallel to the build direction (i.e. longitudinal crack) in the Paris regime as compared to that of specimens with a perpendicular crack (i.e. transverse crack). The near-threshold FCG behavior for specimens with a longitudinal crack did not show the usual stress ratio (R)-shift from low to high R. The low- R data was vastly different than the data generated using the specimens with a transverse crack. Our results also imply that both the current ASTM standard LR method and the new CPLR method induce remote closure, which prematurely slows down crack growth and produces an abnormally high threshold. [ABSTRACT FROM AUTHOR]

This study presents a mathematical model that designs a reliable multi-modal transportation network for a biofuel supply chain system while site-dependent facility failure and congestion are taken into consideration. The proposed model locates the multi-modal facilities and biorefineries and determines the optimal production, storage, and routing plans in such a way that the overall system cost is minimized. We propose a hybrid Constraint generation-based Rolling horizon algorithm to solve this challenging NP -hard problem. The performance of this algorithm is tested in a example case study with numerical analysis showing that the hybrid algorithm can find near-optimal solutions to large-scale problem instances in a reasonable amount of time. Results indicate that the effect of congestion reduces the usage of multi-modal facilities in the biofuel supply chain network while bio-refineries and multi-modal facilities tend to move away from coastal areas when disruption probabilities are taken into consideration. [ABSTRACT FROM AUTHOR]