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The manufacturing industry is currently facing an increasing demand for customized products, leading to a shift from mass production to mass customization. As a result, operators are required to produce multiple product variants with varying complexity levels while maintaining high-quality standards. Further, in line with the human-centered paradigm of Industry 5.0, ensuring the well-being of workers is equally important as production quality. This paper proposes a novel tool, the "Human-Robot Collaboration Quality and Well-Being Assessment Tool" (HRC-QWAT), which combines the analysis of overall defects generated during product variant manufacturing with the evaluation of human well-being in terms of stress response. The HRC-QWAT enables the evaluation and monitoring of human-robot collaboration systems during product variant production from a broader standpoint. A case study of collaborative human-robot assembly is used to demonstrate the applicability of the proposed approach. The results suggest that the HRC-QWAT can evaluate both production quality and human well-being, providing a useful tool for companies to monitor and improve their manufacturing processes. Overall, this paper contributes to developing a human-centric approach to quality monitoring in the context of human-robot collaborative manufacturing. [ABSTRACT FROM AUTHOR]

The automotive industry is evolving by incorporating innovative tools to improve production processes. A proper manufacturing process influences the behavior of the door grommet during its lifetime. In this paper, molecular dynamics simulations are conducted to evaluate the chemical and physical crosslinking of the EPDM rubber over a range of temperatures using a COMPASS force field. Then, once the ethylene propylene diene monomer (EPDM) model was equilibrated and all possible crosslinks were formed, additional simulations were performed on the model to explore its mechanical behavior. Subsequently, using the superposition principle, viscosity and curing kinetics were evaluated using phenomenological models. To validate the results of the simulations, three injection tests of the door grommet were performed at different temperature conditions. The results indicate that the viscosity and elastic properties increase with increasing levels of crosslink density and that the critical gel point decreases with temperature. Molecular dynamics superposition results in phenomenological models are in reasonable agreement with the kinetic and viscoelastic behavior of EPDM during and after the injection process. The results presented in this paper provide novel molecular-level findings on the crosslinking mechanisms of amorphous polymers and their influence on viscoelastic behavior, which could facilitate the design of the injection process for door grommet applications. [ABSTRACT FROM AUTHOR]

This paper introduces a novel wafer-edge quality inspection method based on analysis of curved-edge diffractive fringe patterns, which occur when light is incident and diffracts around the wafer edge. The proposed method aims to identify various defect modes at the wafer edges, including particles, chipping, scratches, thin-film deposition, and hybrid defect cases. The diffraction patterns formed behind the wafer edge are influenced by various factors, including the edge geometry, topography, and the presence of defects. In this study, edge diffractive fringe patterns were obtained from two approaches: (1) a single photodiode collected curved-edge interferometric fringe patterns by scanning the wafer edge and (2) an imaging device coupled with an objective lens captured the fringe image. The first approach allowed the wafer apex characterization, while the second approach enabled simultaneous localization and characterization of wafer quality along two bevels and apex directions. The collected fringe patterns were analyzed by both statistical feature extraction and wavelet transform; corresponding features were also evaluated through logarithm approximation. In sum, both proposed wafer-edge inspection methods can effectively characterize various wafer-edge defect modes. Their potential lies in their applicability to online wafer metrology and inspection applications, thereby contributing to the advancement of wafer manufacturing processes. [ABSTRACT FROM AUTHOR]

In the development of innovative and high-performance products, design expertise is a critical factor. Nevertheless, novel manufacturing processes often frequently lack an accessible comprehensive knowledge base for product developers. To tackle this deficiency in the context of emerging additive manufacturing processes, substantial design knowledge has already been established. However, novel additive manufacturing processes like continuous fiber-reinforced material extrusion have often been disregarded, complicating the process's wider dissemination. The importance of design knowledge availability is paramount, as well as the need for user-friendly design knowledge preparation, standardized structure, and methodological support for accessing the accumulated knowledge with precision. In this paper, we present an approach that provides formalized opportunistic and restrictive design knowledge, ensuring both the comprehensive exploitation of process-specific potentials and the consideration of restrictive limitations in the construction of components. Opportunistic knowledge, presented as principle cards, is systematically derived, prepared, and made accessible. Moreover, an access system is developed to ensure the comprehensive utilization of process-specific potentials throughout the development process. Furthermore, we propose linking these principles through a synergy and conflict matrix, aiming to consider synergistic principles and identify potential conflicts at an early stage. Additionally, an approach to provide restrictive design knowledge in the form of a design rule catalog is proposed. The application of the knowledge system is demonstrated exemplarily using a weight-optimized component. [ABSTRACT FROM AUTHOR]

The assessment of system performance and identification of failure mechanisms in complex engineering systems often requires the use of computation-intensive finite element software or physical experiments, which are both costly and time-consuming. Moreover, when accounting for uncertainties in the manufacturing process, material properties, and loading conditions, the process of reliability-based design optimization (RBDO) for complex engineering systems necessitates the repeated execution of expensive tasks throughout the optimization process. To address this problem, this paper proposes a novel methodology for RBDO. First, a multi-fidelity surrogate modeling strategy is presented, leveraging partially observed information (POI) from diverse sources with varying fidelity and dimensionality to reduce computational cost associated with evaluating expensive high-dimensional complex systems. Second, a multi-task surrogate modeling framework is proposed to address the concurrent evaluation of multiple constraints for each design point. The multi-task framework aids in the development of surrogate models and enhances the effectiveness of reliability analysis and design optimization. The proposed multi-fidelity multi-task machine learning model utilizes a Bayesian framework, which significantly improves the performance of the predictive model and provides uncertainty quantification of the prediction. Additionally, the model provides a highly accurate and efficient framework for reliability-based design optimization through knowledge sharing. The proposed method was applied to two design case studies. By incorporating POI from various sources, the proposed approach improves the accuracy and efficiency of system performance prediction, while simultaneously addressing the cost and complexity associated with the design of complex systems. [ABSTRACT FROM AUTHOR]

With the emergence of the Industrial Internet of Things and Industry 4.0, industrial automation has grown as an important vertical in recent years. Smart manufacturing techniques are now becoming essential to keep up with the global industrial competition. Decreasing machine's downtime and increasing tool life are crucial factors in reducing machining process costs. Therefore, introducing complete process automation utilizing an intelligent automation system can enhance the throughput of manufacturing processes. To achieve this, intelligent manufacturing systems can be designed to recognize materials they interact with and autonomously decide what actions to take whenever needed. This paper aims to present a generalized approach for fully automated machining processes to develop an intelligent manufacturing system. As an objective to accomplish this, the presence of workpiece material is automatically detected and identified in the proposed system using a convolutional neural network (CNN) based machine learning (ML) algorithm. Furthermore, the computer numerical control (CNC) lathe's machining toolpath is automatically generated based on workpiece images for a surface finishing operation. Machining process parameters (spindle speed and feed rate) are also autonomously controlled, thus enabling full machining process automation. The implemented system introduces cognitive abilities into a machining system, creating an intelligent manufacturing ecosystem. The improvised system is capable of identifying various materials and generating toolpaths based on the type of workpieces. The accuracy and robustness of the system are also validated with different experimental setups. The presented results demonstrate that the proposed approach can be applied in manufacturing systems without the need for significant modification. [ABSTRACT FROM AUTHOR]

This paper details the design and operation of a testbed to evaluate the concept of autonomous manufacturing to achieve a desired manufactured part performance specification. This testbed, the autonomous manufacturing system for phononic crystals (AMSPnC), is composed of additive manufacturing, material transport, ultrasonic testing, and cognition subsystems. Critically, the AMSPnC exhibits common manufacturing deficiencies such as process operating window limits, process uncertainty, and probabilistic failure. A case study illustrates the AMSPnC function using a standard supervised learning model trained by printing and testing an array of 48 unique designs that span the allowable design space. Using this model, three separate performance specifications are defined and an optimization algorithm is applied to autonomously select three corresponding design sets to achieve the specified performance. Validation manufacturing and testing confirms that two of the three optimal designs, as defined by an objective function, achieve the desired performance, with the third being outside the design window in which a distinct bandpass is achieved in phononic crystals (PnCs). Furthermore, across all samples, there is a marked difference between the observed bandpass characteristics and predictions from finite elements method computation, highlighting the importance of autonomous manufacturing for complex manufacturing objectives. [ABSTRACT FROM AUTHOR]

Wire electric discharge machining (WEDM) is a widely used high-precision machining process. It has the capability of cutting high-strength alloys, namely, tool alloys, Hastelloy, and monel with very tight tolerance. Frequent wire breakage adversely affects the product geometric precision, surface integrity, and productivity of the manufacturing establishments. It is essential to understand the causes of wire breakage and to mitigate the frequency of its occurrence. This paper comprehensively reviews the experimental as well as theoretical research work reported on various aspects of wire electrode erosion such as the effect of heat generated in spark plasma, thermal stresses, wire rupture phenomena, and wire fatigue. It also presents important research findings on the causes of wire failure and advancements to reduce wire erosion and wire vibrations. The paper systematically and carefully examines the measures suggested to improve the wire life and depicts the development of various wire health monitoring and control systems. Overall, it presents useful insights and important guidelines for the efficient utilization of the wire EDM process by mitigating the causes of wire erosion and its failure. It will certainly help for the improvement of the precision machining capability of the WEDM process. [ABSTRACT FROM AUTHOR]

Powder bed fusion (PBF) is an additive manufacturing process in which laser heat liquefies blown powder particles on top of a powder bed, and cooling solidifies the melted powder particles. During this process, the laser beam heat interacts with the powder causing thermal emission and affecting the melt pool. This paper aims to predict heat emission in PBF by harnessing the strengths of recurrent neural networks. Long short-term memory (LSTM) networks are developed to learn from sequential data (emission readings), while the learning is guided by process physics including laser power, laser speed, layer number, and scanning patterns. To reduce the computational efforts on model training, the LSTM models are integrated with a new approach for down-sampling the pyrometry raw data and extracting useful statistical features from raw data. The structure and hyperparameters of the LSTM model reflect several iterations of tuning based on the training on the pyrometer readings data. Results reveal useful knowledge on how raw pyrometer data should be processed to work the best with LSTM, how physics features are informative in predicting overheating, and the effectiveness of physics-guided LSTM in emission prediction. [ABSTRACT FROM AUTHOR]

As additive manufacturing (AM) usage increases, designers who wish to maximize AM's potential must reconsider the traditional manufacturing (TM) axioms they may be more familiar with. While research has previously investigated the potential influences that can affect the designs produced in concept generation, little research has been done explicitly targeting the manufacturability of early-stage concepts and how previous experience and the presenting of priming content in manufacturing affect these concepts. The research in this paper addresses this gap in knowledge, specifically targeting differences in concept generation due to designer experience and presenting design for traditional manufacturing (DFTM) and design for additive manufacturing (DFAM) axioms. To understand how designers approach design creation early in the design process and investigate potential influential factors, participants in this study were asked to complete a design challenge centered on concept generation. Before this design challenge, a randomized subset of these participants received priming content on DFTM and DFAM considerations. These participants' final designs were evaluated for both traditional manufacturability and additive manufacturability and compared against the final designs produced by participants who did not receive the priming content. Results show that students with low manufacturing experience levels create designs that are more naturally suited for TM. Additionally, as designers' manufacturing experience levels increase, there is an increase in the number of designs more naturally suited for AM. This correlates with a higher self-reported use of DFAM axioms in the evaluation of these designs. These results suggest that students with high manufacturing experience levels rely on their previous experience when it comes to creating a design for either manufacturing process. Lastly, while the manufacturing priming content significantly influenced the traditional manufacturability of the designs, the priming content did not increase the number of self-reported design for manufacturing (DFM) axioms in the designs. [ABSTRACT FROM AUTHOR]

The new wave of Industry 4.0 is transforming manufacturing factories into data-rich environments. This provides an unprecedented opportunity to feed large amounts of sensing data collected from the physical factory into the construction of digital twin (DT) in cyberspace. However, little has been done to fully utilize the DT technology to improve the smartness and autonomous levels of small and medium-sized manufacturing factories. Indeed, only a small fraction of small and medium-sized manufacturers (SMMs) has considered implementing DT technology. There is an urgent need to exploit the full potential of data analytics and simulation-enabled DTs for advanced manufacturing. Hence, this paper presents the design and development of DT models for simulation optimization of manufacturing process flows. First, we develop a multi-agent simulation model that describes nonlinear and stochastic dynamics among a network of interactive manufacturing things, including customers, machines, automated guided vehicles (AGVs), queues, and jobs. Second, we propose a statistical metamodeling approach to design sequential computer experiments to optimize the utilization of AGV under uncertainty. Third, we construct two new graph models--job flow graph and AGV traveling graph--to track and monitor the real-time performance of manufacturing jobshops. The proposed simulation-enabled DT approach is evaluated and validated with experimental studies for the representation of a real-world manufacturing factory. Experimental results show that the proposed methodology effectively transforms a manufacturing jobshop into a new generation of DT-enabled smart factories. The sequential design of experiments effectively reduces the computation overhead of expensive simulations while optimally scheduling the AGV to achieve production throughput cost-effectively. This research is strongly promised to help SMMs fully utilize big data and DT technology for gaining competitive advantages in the global marketplace. [ABSTRACT FROM AUTHOR]

This paper introduces a new control method for web tension control on a complex roll-to-roll winding machine used in battery production. A traditional web tension control method cannot perform well enough under high winding speed: the parameter tuning process is time-consuming, the disturbance rejection performance is not satisfying, and the control performance is not stable. A hybrid control method is proposed, and it is easy to be implemented on common programming platform for commercial winding machines with an easy tuning process, while providing superior control performance to the traditional control method. The system modeling used in the control method is much simpler than the modeling in most of the tension control researches, providing better feasibility for industrial application. [ABSTRACT FROM AUTHOR]

The formation of porosity is a major challenge in any composite manufacturing process, particularly in the absence of vacuum assistance. Highly localized injection of polymer matrix into regions of interest in a dry preform is a route to manufacturing multi-matrix fiber-reinforced composites with high filler concentrations, which are otherwise difficult to achieve. Unlike traditional composites, such multi-matrix fiber-reinforced composite systems, which combine multiple resins in continuous form, offer improved structural performance around stress concentrators and multifunctional capabilities. As the process lacks vacuum assistance, porosity becomes a primary issue to be addressed. This paper presents a rheo-kinetic coupled rapid consolidation procedure for optimizing the quality of localized matrix patches. The procedure involves manufacturing trials and analytical consolidation models to determine the best processing program for minimal voidage in the patch. The results provide a step toward an efficient manufacturing process for the optimal design of multi-matrix composites without the need for complex vacuum bag arrangements, thus reducing cost and time while opening avenues to improve overall composite performance. [ABSTRACT FROM AUTHOR]

With the development and gradual maturity of additive manufacturing (AM) over the years, AM has reached a stage where implementation into a conventional production system becomes possible. With AM suitable for small volumes of highly customized production, there are various ways of implementing AM in a conventional production line. The aim of this paper is to present a strategic design approach to implementing AM with conventional manufacturing in a complementary manner for parallel processing of production orders of large quantities in a make-to-stock environment. By assuming that a single machine in conventional manufacturing can be operated using AM, splitting of production orders is allowed. Therefore, production can be conducted by both conventional and AM processes simultaneously, with the latter being able to produce various make-to-stock parts in a single build. A genetic algorithm with a scheduling and rule-based heuristic for part allocation on the build plate of AM process is used to solve a multi-objective implementation problem of AM with conventional manufacturing, with cost, scheduling, and sustainability being the considered performance measures. By obtaining a knee-point solution using varying numbers of population size and generation number, an experiment involving an industry case study of implementing the fused deposition modeling (FDM) process with injection molding process shows the greatest impact, i.e., increase, in cost. Except for material efficiency, improvements are shown in scheduling and carbon footprint objectives. [ABSTRACT FROM AUTHOR]

Massive continuous streaming data are generated over time during production in a multistage manufacturing process. This paper aims to develop a product-oriented synchronization and effective information extraction of continuous streaming data and further model the relationships among variables for knowledge discovery. Take the steel rolling process as an example; this paper proposes a three-step data analytics procedure for product-oriented synchronization of continuous streaming data, effective information extraction, and further conducting relationship mining between the roll gap adjustment operations and product shapes based on the product-oriented data. The developed procedure first converts the continuous streaming data generated over time in a production process to product-oriented data set, then extracts the information related to the causes and effects of roll gap adjustments, and finally fits the model describing the relationship among the roll gap adjustments, the change of rolling torques, and the change of product dimensions. This data analytics procedure facilitates the decision-making in the steel rolling process and illustrates an effective application of massive in-situ sensing data towards intelligent decision-making in data-rich manufacturing processes. [ABSTRACT FROM AUTHOR]

Industrial artificial intelligence (IAI) and other analysis tools with obfuscated internal processes are growing in capability and ubiquity within industrial settings. Decision-makers share their concern regarding the objective evaluation of such tools and their impacts at the system level, facility level, and beyond. One application where this style of tool is making a significant impact is in Condition Monitoring Systems (CMSs). This paper addresses the need to evaluate CMSs, a collection of software and devices that alert users to changing conditions within assets or systems of a facility. The presented evaluation procedure uses CMSs as a case study for a broader philosophy evaluating the impacts of IAI tools. CMSs can provide value to a system by forewarning faults, defects, or other unwanted events. However, evaluating CMS value through scenarios that did not occur is rarely easy or intuitive. Further complicating this evaluation are the ongoing investment costs and risks posed by the CMS from imperfect monitoring. To overcome this, an industrial facility needs to regularly and objectively review CMS impacts to justify investments and maintain competitive advantage. This paper's procedure assesses the suitability of a CMS for a system in terms of risk and investment analysis. This risk-based approach uses the changes in the likelihood of good and bad events to quantify CMS value without making any one-time pointwise estimates. Fictional case studies presented in this paper illustrate the procedure and demonstrate its usefulness and validity. [ABSTRACT FROM AUTHOR]

This paper presents formulations for hybrid casting and additive manufacturing (AM) in the density-based topology optimization. A location-based Heaviside function is introduced to represent the parting surface. The optimized part on two sides of the parting surface can be fabricated with casting, additive manufacturing, or both. Through the location-based Heaviside function and density gradient, two global constraints are formulated to remove undercuts and overhangs for casting and AM, respectively, inside the design domain. Since density gradient vanishes on the design domain boundary, two extra density-based global constraints are developed to control the overhangs and undercuts outside the design domain. Due to the smoothed parameterization of the parting surface, we are able to optimize the part and partition surface (including location and parting direction) simultaneously for hybrid casting and additive manufacturing. The proposed formulations for hybrid manufacturing processes are validated through 2D and 3D numerical examples. The proposed approach further enlarges the design space with manufacturing constraints, and has the potential to be used in the design for hybrid and multi-component manufacturing. [ABSTRACT FROM AUTHOR]

Hybrid manufacturing technology has enabled manufacturers to combine advantages of mainly subtractive and additive manufacturing technologies. A single machine supports producing products with complex geometry, at high quality, and with a high degree of automation. To benefit from these advantages, decisions taken in the process planning stage of such a sophisticated manufacturing system should be optimized. The objective of this paper is to determine the optimal process plan considering both the engineering and manufacturing aspects of the hybrid technology. A comprehensive process planning model is proposed. The model specifies the optimal sequence of additive and subtractive features that minimizes the production cycle time. In addition, the model sets the optimal part orientations such that the time needed for building support structures, performing post-processing and inspection operations, changing cutting tools and printing nozzles, and unclamping the part is minimized. The model is comprehensive as it considers productive and non-productive times, precedence, technological, quality, and manufacturing restrictions imposed on hybrid manufacturing systems. The proposed model is nonlinear; due to this nonlinearity, the model is intractable. A linearization scheme is applied to formulate an equivalent linear model that is solvable to optimality by commercial solvers. Case studies on test and industrial parts are provided to evaluate the computational performance of the proposed model. Integrating the proposed model in hybrid manufacturing (HM) systems ensures adopting the HM technology in its optimal direction. HM technology is an enabler of establishing a smart manufacturing system which is one of the pillars of Industry 4.0. [ABSTRACT FROM AUTHOR]

This paper studies the effects of plasma-induced cracks on rock cutting to support the concept of a plasma-integrated drag bit for accelerated geothermal drilling through hard rocks. For this, a single polycrystalline diamond compact (PDC) drilling technique is used in cutting granite to compare thrust and cutting forces between plasma-treated and untreated rock samples. The cracks are produced using underwater plasma at 80 J per pulse. This energy level does not produce visible damage to the rock. The cutting tests are conducted at a cutting speed of 12.7 m/min and four feed rates of 0.127, 0.201, 0.267, and 0.414 mm/s to represent actual drilling scenarios. The results show a general trend of reduction in both thrust and cutting forces for these feed rates, but the magnitude of reduction highly depends on the feed rate. The maximum force reduction of around 50% is found at the 0.267 mm/s feed rate with statistical significance. Cases with a higher force reduction are also found to have rougher surface topography, which indicates more excessive fracturing and, thus, a cracks-accelerated material removal process. This study provides initial evidence of using underwater plasma to increase the downhole drilling rate of hard rocks. [ABSTRACT FROM AUTHOR]

Uneven distribution of machining-induced residual stresses (MIRS) caused by dynamic cutting conditions and material internal properties has a significant impact on fatigue resistance, stress corrosion resistance, and accuracy retention of aerospace structural parts. Owing to the challenges in describing residual stress distribution property, this paper proposes a distribution consistency characterization model with high accuracy, as well as a quantitative evaluation strategy in milling titanium alloy. MIRS reduction surrogate model embedded with mechanism knowledge describes the general profile of MIRS in the explicit form. Compared with the surrogate model, the prediction accuracy of MIRS under Bayesian calibration is improved by more than 25.68%. To evaluate the MIRS distribution consistency, self-consistent indexes are innovatively proposed based on the extraction of four fundamental MIRS features in parametric form. The sensitivity analyses between three machining parameters and self-consistent indexes are carried out, guiding in selecting machining parameters in consideration of MIRS distribution consistency. Consequently, this work can provide a novel insight into determining the optimal machining parameters with comprehensive consideration of MIRS features magnitudes and distribution consistency. According to the proposed procedure, there exists a potential for future extensions to other materials and cutting processes with more complex stress distributions. [ABSTRACT FROM AUTHOR]

Unique aspects in the development of bistable load-type fluidic oscillators that satisfy the requirement of producing large-amplitude pressure fluctuations during the charging of vessels for potential implementation in industrial processes such as the superplastic forming process are addressed in this paper. A pseudo-3D computational fluid dynamic model is shown to be capable of accurately predicting the experimental values of the dimensionless frequencies and pressure fluctuation amplitudes as well as the experimental Schlieren images of the flow field obtained over a wide range of operating conditions. The pseudo-3D model is also used to provide details of the fluid motion in the oscillator which could not be measured experimentally when investigating the operation of the device. The flow switching mechanism is identified as a consequence of a reduction of the flow deflection angle due to the increase of the downstream pressure load by the charging of feedback tanks. Some examples of the usefulness of the model as a cost-effective industrial design tool are also demonstrated. The effects of changing the number and size of the feedback tank volumes on the device frequency and amplitude of the oscillation are clearly shown using dimensionless variables. [ABSTRACT FROM AUTHOR]

The conventional manufacturing processes for aerogel insulation materials mostly rely on the supercritical drying, which suffers from issues of massive energy consumption, high-cost equipment, and prolonged processing time. Considering the large market demand for the aerogel insulation material in the next decade, a cost-effective and scalable fabrication technique is highly desired. In this paper, a direct ink writing (DIW) method is used to three-dimensionally fabricate the silica aerogel insulation material, followed by room-temperature and ambient pressure drying. Compared with the supercritical drying and freeze-drying techniques, the reported method significantly reduces the fabrication time and costs. The cost-effective DIW technique offers the capability to print complex hollow internal structures, coupled with the porous aerogel structure, it is found to be beneficial for the thermal insulation property. The addition of fiber to the ink assures the durability of the fabricated product, without sacrificing the thermal insulation performance. The foam ink preparation methods and the printability are demonstrated in this paper, along with the printing of complex three-dimensional geometries. The thermal insulation performance of the printed objects is characterized, and the mechanical properties were also examined. The proposed approach is found to have a 56% reduction in the processing time. The printed silica aerogels exhibit a low thermal conductivity of 0.053 W m-1 K-1. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) is a set of manufacturing processes that are capable of producing complex parts directly from a computer model of the part. This review provides a history of the early antecedents of these processes. In addition, the different classes of AM processes and their commercialization are presented and discussed along with their fields of use. This paper emphasizes AM processes that produce production quality parts. The review also addresses design issues and the commercial state of the art for production of polymer, metal, and ceramic parts. A main emphasis of this paper is the development and motivations for AM especially during its nascent years. The paper is written for the general readership of manufacturing professionals and researchers. [ABSTRACT FROM AUTHOR]

Today's manufacturing systems are becoming increasingly complex, dynamic, and connected. The factory operations face challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in artificial intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data. The focus of this paper is threefold: (1) review the state-of-the-art applications of AI to representative manufacturing problems, (2) provide a systematic view for analyzing data and process dependencies at multiple levels that AI must comprehend, and (3) identify challenges and opportunities to not only further leverage AI for manufacturing, but also influence the future development of AI to better meet the needs of manufacturing. To satisfy these objectives, the paper adopts the hierarchical organization widely practiced in manufacturing plants in examining the interdependencies from the overall system level to the more detailed granular level of incoming material process streams. In doing so, the paper considers a wide range of topics from throughput and quality, supervisory control in human-robotic collaboration, process monitoring, diagnosis, and prognosis, finally to advances in materials engineering to achieve desired material property in process modeling and control. [ABSTRACT FROM AUTHOR]

Inspired by porous morphology in nature, such as bone and lung tissues, synthetic porous materials are widely adopted in engineering applications that require lightweight, thermal resistance, energy absorption, and structural flexibility. One of the main challenges in the current porous material manufacturing techniques is their limited control over individual pore size, connectivity, and distribution. This paper presents a novel additive manufacturing process to fabricate porosity-embedded structures by integrating stereolithography and inkjet printing using a sacrificial liquid-water. A solenoid-based inkjet nozzle dispenses water droplets onto a layer of liquid photopolymer resin. Then the resin layer is photocured by a mask image projection device using a digital light processing device. The photocuring process defines the layer profile and captures the deposited water droplets in the solidified layer. The refilled fresh resin will further embed water droplets and form a new layer for the subsequent water droplet deposition. Three-dimensional (3D) structures with embedded water droplets can be printed layer-by-layer. The captured water will evaporate when heated, leaving an air-filled porous 3D structure. By selectively depositing water droplets and varying inkjet printing parameters, including pressure, nozzle opening time, and jetting frequency, the micropores whose sizes from 100 µm to 500 µm and distributions within the 3D-printed part can be modulated. This hybrid process can fabricate 3D structures with homogenously distributed pores and graded polymer structures with varying porosities. The elastic modulus of 3D-printed foam structures in different pore distributions has been tested and compared. [ABSTRACT FROM AUTHOR]

Precision medicine is an emerging healthcare delivery approach that considers variability between patients, such as genetic makeups, in contrast to the current one-size-fits-all approach that is designed to treat the average patient. The White House launched the Precision Medicine Initiative in 2015, starting an endeavor to reshape healthcare delivery. To translate the concept of precision medicine from the bench to practice, advanced manufacturing will play an integral part, including the fabrication of personalized drugs and drug delivery devices and drug screening platforms. These products are highly customized and require robust yet flexible manufacturing systems. The advanced manufacturing field has rapidly evolved in the past five years. In this state-of-the-art review, products manufactured for precision medicine will be introduced, followed by a brief review of processing materials and their characteristics. A review on different manufacturing processes applicable to those aforementioned products is provided. The current status of the development of regulatory submission and quality control considerations are also discussed. Finally, this paper presents a future outlook on manufacturing processes used for precision medicine. [ABSTRACT FROM AUTHOR]

This paper describes the application of a new manufacturing process for low-cost and rapid consolidation and curing of advanced thermoset composites that avoids the use of expensive prepreg, autoclaving, and thermally induced curing. The process, called VIPE, uses a novel tooling design that combines vacuum infusion (VI) of a dry preform with resin, a rigidly backed pressure focusing layer (P) made of an elastomer to consolidate the wet preform with uniform pressure, and high-energy electron beam curing (E). A VIPE tool is engineered and fabricated to manufacture 3D laminate bicycle seats composed of woven carbon fiber textile and an electron beam-curable epoxy acrylate. Details of the tooling design discussed include computational fluid dynamics (CFD) simulation of the vacuum infusion, iterative structural finite element analysis (FEA) to synthesize the pressure focusing layer (PFL), structural FEA to design the top mold made of a composite sandwich structure for electron beam transparency, and Monte Carlo electron absorption simulations to specify the e-beam energy level. Ten parts are fabricated using the matched tool (bottom aluminum mold covered with silicone layer and top mold with carbon/epoxy skins separated by foam core) after the dry textile preform contained within is infused with resin, the tool halves are clamped under load, and a 3.0 MeV e-beam machine bombards the tool for less than 1 min. Part thickness, part stiffness, surface roughness, and fiber and void volume fractions measurements show that aerospace quality parts with low cycle times are achievable, although there is high variability due to the small number of replicates and need for process optimization. [ABSTRACT FROM AUTHOR]

The accurate evaluation of the instantaneous undeformed chip thickness (IUCT) plays a crucial role in the modeling of milling processes. However, the vibrations of the tool-workpiece system can make conventional IUCT models either inaccurate or not applicable. This paper introduces the concept of surface function to describe the milled surface, through which the IUCT can be readily computed. The evolution of this surface function is governed by a partial differential equation (PDE) in the form of a balance law, and the material removal process is characterized by discontinuous conditions at the cutters. A finite volume algorithm is adopted to solve the proposed PDE with discontinuous conditions at the cutters. Through a case study of the asymmetric cutting process, the surface function method demonstrates two main advantages over conventional methods: (i) a detailed description of IUCT evolution considering the influence of the initial shape of the workpiece and (ii) a general framework to accurately compute the IUCT. This method shows a promising potential for computing the IUCT in numerical simulations of chattering phenomenon in the milling process. [ABSTRACT FROM AUTHOR]

The objective of this review paper is to summarize the current status and identify the knowledge gaps in ceramic binder jetting additive manufacturing, with a particular focus on density. This paper begins with an overview of ceramic binder jetting. Then, it discusses different aspects of density, including various terminologies, measurement methods, and achieved values. Afterward, it reviews two categories of techniques to increase the part density: material preparation techniques (powder granulation, mixing powders of different sizes, using slurry feedstock, and mixing different materials) and postprocessing techniques (sintering, chemical reaction, infiltration, and isostatic pressing). Finally, it presents the knowledge gaps in the literature. [ABSTRACT FROM AUTHOR]

Despite the rapid growth and widespread recognition of additive manufacturing (AM) technologies, the geometrical inaccuracy of the manufactured products remains a challenging issue and effective prediction of the geometric deviations in AM is critical for the solution of this issue. The layerwise characteristic of the AM process has motivated the investigation of deviation from in-plane and out-of-plane perspectives, the latter has been seldom studied and will be the focus of this paper. In this paper, an out-of-plane deviation modeling method will be proposed based on statistical modal analysis. Owing to the inconvenience in data acquisition, AM simulation is conducted to obtain the layer-level out-of-plane deviation on parts manufactured by the selective laser melting process. Discrete cosine transform is adopted to identify the major deviation modes from the data. The statistical relationship between mode coefficients and related part and process parameters is studied based on the Gaussian process model. To gain data for model training, experimental design is conducted to sample parameter combinations as simulation input. A case study is presented to demonstrate the proposed method and the effectiveness is validated on test data. The method can be applied in multiple domains of AM, such as quality control and tolerancing, to provide high-fidelity prediction of geometric deviations. [ABSTRACT FROM AUTHOR]

This paper proposes a detailed methodology for constructing an additive manufacturing (AM) digital twin for the laser powder bed fusion (LPBF) process. An important aspect of the proposed digital twin is the incorporation of model uncertainty and process variability. A virtual representation of the LPBF process is first constructed using a physics-based model. To enable faster computation required in uncertainty analysis and decision-making, the physics-based model is replaced by a cheaper surrogate model. A two-step surrogate model is proposed when the quantity of interest is not directly observable during manufacturing. The data collected from the monitoring sensors are used for diagnosis (of current part quality) and passed on to the virtual representation for model updating. The model updating consists of Bayesian calibration of the uncertain parameters and the discrepancy term representing the model prediction error. The resulting digital twin is thus tailored for the particular individual part being produced and is used for probabilistic process parameter optimization (initial, before starting the printing) and online, real-time adjustment of the LPBF process parameters, in order to control the porosity in the manufactured part. A robust design optimization formulation is used to minimize the mean and standard deviation of the difference between the target porosity and the predicted porosity. The proposed methodology includes validation of the digital twin in two stages. Validation of the initial model in the digital twin is performed using available data, whereas data collected during manufacturing are used to validate the overall digital twin. [ABSTRACT FROM AUTHOR]

Fast tool servo (FTS) systems have emerged as a promising technology in ultraprecision manufacturing, where sophisticated contouring in the manufacturing process poses significant challenges to the dynamical tracking of periodical references at nanoscale. The situation is further complicated by the existence of time delays, load variations, as well as disturbances. Comprehensive handling of these problems in a unified framework is crucial to the manufacturing precision of FTS systems. In this paper, we propose a modified robust repetitive control structure for FTS systems with time delays to improve the tracking performance by loop shaping. On top of the specific structure of the robust repetitive controller, a parallel structure is first introduced to optimize the low-pass filter to increase controller gains at the fundamental and harmonic frequencies, such that an improved tracking performance is obtained but at the cost disturbance rejection capabilities. To this end, a cascaded structure is then introduced to further shape the sensitivity function to compensate the external disturbances especially near the maximum sensitivity frequency. Taking the robustness against model uncertainties into consideration, we further formulate the multistage design of the proposed controller as an H∞ optimization problem solving by skew Toeplitz approach. Real-time experiments are conducted on a prototype of the FTS system demonstrating excellent tracking performance of the proposed control approach. [ABSTRACT FROM AUTHOR]

An integrated flow-stress (IFS) model provides a seamless and mechanistic connection between the two distinct regimes during the manufacturing process of composite materials, namely, fluid flow in the pregelation stage of the thermoset resin and stress development in the composite when it acts as a solid material. In this two-part paper, the two- and three-phase isotropic IFS models previously developed by the authors are extended to the general case of composite materials with orthotropic constituents. Part I presents the two-phase, fluid-solid, orthotropic model formulation for the case where the fluid phase solidifies during the course of curing. Part II extends the orthotropic formulation to a three-phase model that includes a gas phase as the third constituent of the composite material system. A broader definition of material properties in poroelasticity formulation is adopted in the development of the general orthotropic formulation. The model is implemented in a two-dimensional (2D) plane strain u-v-P finite element (FE) code and its capability in predicting the flow-compaction behavior and stress development is demonstrated through application to a case study involving an L-shaped unidirectional laminate undergoing curing on a conforming convex tool. Comparison of the results with those obtained from sole modeling of the stress development reveals the importance of capturing the simultaneous and interactive effect of the mechanisms involved during the entire process cycle using an IFS modeling approach presented in this paper. [ABSTRACT FROM AUTHOR]

Modeling and simulation for additive manufacturing (AM) is commonly used in industry. Nevertheless, a central issue remaining is the integration of different models focusing on different objectives and targeting different levels of details. The objective of this work is to increase the prediction capability of characteristics and performances of additively manufactured parts and to co-design parts and processes. The paper contributes to this field of research by integrating part's performance model and additive technology process model into a single early integrated model. The paper uses the dimensional analysis conceptual modeling (DACM) framework in an AM perspective to generate causal graphs integrating the AM equipment and the part to be printed. DACM offers the possibility of integrating existing knowledge in the model. The framework supported by a computer tool produces a set of governing equations representing the relationships among the influencing variables of the integrated model. The systematic identification of the weaknesses and contradictions in the system and qualitative simulation of the system are some of the potential uses of the model. Ultimately, it is a way to create better designs of machines and parts, to control and qualify the manufacturing process, and to control three-dimensional (3D) printing processes. The DACM framework is tested on two cases of a 3D printer using the fused filament fabrication (FFF) powder bed fusion. The analysis, applied to the global system formed of the 3D printer and the part, illustrates the existence of contradictions. The analysis supports the early redesign of both parts and AM process (equipment) and later optimization of the control parameters. [ABSTRACT FROM AUTHOR]

The chief objective of manufacturing process improvement efforts is to significantly minimize process resources such as time, cost, waste, and consumed energy while improving product quality and process productivity. This paper presents a novel physics-informed optimization approach based on artificial intelligence (AI) to generate digital process twins (DPTs). The utility of the DPT approach is demonstrated in the case of finish machining of aerospace components made from gamma titanium aluminide alloy (γ-TiAl). This particular component has been plagued with persistent quality defects, including surface and sub-surface cracks, which adversely affect resource efficiency. Previous process improvement efforts have been restricted to anecdotal post-mortem investigation and empirical modeling, which fail to address the fundamental issue of how and when cracks occur during cutting. In this work, the integration of in-situ process characterization with modular physics-based models is presented, and machine learning algorithms are used to create a DPT capable of reducing environmental and energy impacts while significantly increasing yield and profitability. Based on the preliminary results presented here, an improvement in the overall embodied energy efficiency of over 84%, 93% in process queuing time, 2% in scrap cost, and 93% in queuing cost has been realized for γ-TiAl machining using our novel approach. [ABSTRACT FROM AUTHOR]

The use of lighter materials is one of the efficient means to mitigate the increasing demands on fuel resources, reduce CO2 emissions. Mg is one of the lightest material available and possesses exciting range of properties such as low density and high strength to weight ratio. Despite such exciting properties, the applications of Mg and its alloys were very limited in aerospace, automotive, and biomedical industries but recently the application is picking-up. The restricted application is attributed to anisotropy, poor corrosive resistance, and inflammability of Mg. The current review addresses the barriers limiting the widespread application of Mg based materials. Furthermore, the mitigation of the problems of anisotropy, poor corrosion resistance, ductility, and inflammability of Mg are critically reviewed. The findings of this research provide insights of the processing techniques, properties and how to address the potential barriers of limited applications. The review paper will assist and motivate the researchers to ponder and overcome numerous problems related to Mg and its alloys by understanding the importance of each problem discussed in this review. An attempt has also been made to arrange research status on issues and the mitigation thereof with respect to Mg and its alloys as single reference point. [ABSTRACT FROM AUTHOR]

Solar direct steam generation (DSG) systems are well suited for process steam applications and are able to provide steam at the pressure required by common industrial processes. Nevertheless, reliable control has always been a challenge for solar DSG system hindering its wider adoption. In this paper, a control strategy for solar DSG systems is presented. The control strategy is based on proportional-integral-derivative (PID) control theory combined with model-based feedforward control. Experimental data demonstrate that the control strategy provides good performance in terms of stability and setpoint tracking. The error in setpoint tracking for the load pressure controller is shown to be as low as ±0.005 MPa under real life conditions. The said strategy is currently implemented in two commercially operating plants providing solar steam for industrial processes. [ABSTRACT FROM AUTHOR]

This paper presents an analytical thermo-mechanical model of shear localization and shear band formation in orthogonal cutting of high-strength metallic alloys. The deformation process of the workpiece material includes three stages: homogeneous deformation, shear localization, and chip segmentation. A boundary layer analysis is used to analytically predict the temperature, stress, and strain rate variations in the primary shear zone associated with the shear localization. The predictions of shear band spacing and width from the proposed model are verified by experimental characterization of the chip morphology. The rolling of shear bands on the tool rake face is discussed from the experimental observations. The cutting tool temperature, which is influenced by the heat generated during the shear band formation, is simulated and compared with finite element simulations. The proposed analytical model reveals the fundamental mechanism of the complete shear localization process in orthogonal cutting and predicts the stress and temperature variations with high computational efficiency. [ABSTRACT FROM AUTHOR]

Assembly through mating a pair of machined surfaces plays a crucial role in many manufacturing processes such as automotive powertrain production, and the mating errors during the assembly (i.e., gaps between surfaces) can cause significant internal leakage and functional performance problems. The surface mating errors are difficult to diagnose because they are not measurable. Current in-plant quality control for surface mating focuses on controlling the surface flatness of each individual part before they are mated, and the mating errors are indirectly evaluated by a pressurized sealing test to check whether any pressure drop occurs. However, it does not provide any clue to engineers about the origins and the root cause of the internal leakage. To address these limitations, this paper presents a pressurized color-tracking method to directly measure internal leak areas. By using the measurements of leak areas and the profiles of surfaces mated as training data along with Hagen-Poiseuille law, this paper develops a novel diagnostic method to predict potential leak areas (leakage paths) given the measurements on the profiles of mating surfaces. The effectiveness and robustness of the proposed method are verified by a simulation study and an experiment. The approach provides practical guidance for the subsequent assembly process as well as troubleshooting in surface machining processes. [ABSTRACT FROM AUTHOR]

The need for an open, inclusive, and neutral procedure in selecting key performance indicators (KPIs) for sustainable manufacturing has been increasing. The reason is that manufacturers seek to determine what to measure to improve environmental sustainability of their products and manufacturing processes. A difficulty arises in understanding and selecting specific indicators from many stand-alone indicator sets available. This paper presents a procedure for individual manufacturers to select KPIs for measuring, monitoring, and improving environmental aspects of manufacturing processes. The procedure is the basis for a guideline, being proposed for standardization within ASTM International. That guide can be used for (1) identifying candidate KPIs from existing sources, (2) defining new candidate KPIs, (3) selecting appropriate KPIs based on KPI criteria, and (4) composing the selected KPIs with assigned weights into a set. The paper explains how the developed procedure complements existing indicator sets and sustainability-measurement approaches at the manufacturing process level. [ABSTRACT FROM AUTHOR]

This paper represents a novel approach capable of in-process damping parameter control for nanopositioning systems by implementing a fluidic pressure-fed mechanism (FPFM). The designed internal fluidic channels inside the nanopositioning stage fabricated by a metal additive manufacturing process can be filled with various fluids such as air, water, and oil and pneumatically or hydraulically pressurized. The damping was experimentally characterized with respect to fluids and corresponding pressure level (80 psi, which was the maximum safe operating pressure in the current laboratory setting) through a free-vibration test, hammering test, and sine input sweeping test in open-loop and closed-loop positioning control conditions. As a result, the FPFM revealed the following characteristics: (1) damping increases when the internal fluidic channels filled with fluids and pressure level at 80 psi, (2) the dynamic system showed the highest damping when the water exists in internal channels, and (3) the existence of fluids and certain pressure in the fluidic channel does not have a negative influence on the motion quality and positioning control while the nanopositioning system is in operating. Additionally, the tracking error was reduced by FPFM. It is expected that the FPFM method will be utilized for vibration and noise control applications for high precision dynamic systems. [ABSTRACT FROM AUTHOR]

Single-phase cold plate liquid cooling attracts more and more attention to high-performance computing (HPC), cloud computing, and general computing data centers for the thermal management of modern microprocessors and adjacent components due to liquid's inherent advantage of higher specific heat compared to air. Deionized (DI) water is usually used as a coolant for liquid cooling in data centers. On the contrary, propylene glycol/water is recommended as a coolant for single-phase cold plate liquid cooling in this study for following reasons. The inhibited propylene glycol-based fluids of 25+% vol have the benefit of being biostatic and not requiring addition of biocides. They also offer freezing protection in the usage of data centers in cold climates. The cold plates made from copper is prone to oxide even under the room temperature, and the dissimilarity between brazing material and copper can also cause galvanic corrosion in the usage. In this paper, a study was carried out to investigate cold plate corrosion with inhibited propylene glycol/water using design of experiments (DOE) method. This study shows manufacturing process plays an important role on corrosion of cold plates made from copper and the corrosion risk can be mitigated by enabling new manufacturing processes, including friction stir welding (FSW) and nickel plating to the inside surface of the cold plate in the manufacturing process. [ABSTRACT FROM AUTHOR]

Design-for-manufacturing (DFM) concepts have traditionally focused on design simplification; this is highly effective for relatively simple, mass-produced products, but tends to be too restrictive for more complex designs. Effort in recent decades has focused on creating methods for generating and imposing specific, process-derived technical manufacturability constraints for some common problems. This paper presents an overview of the problem and its design implications, a discussion of the nature of the manufacturability constraints, and a survey of the existing approaches and methods for generating/enforcing the minimally restrictive manufacturability constraints within several design domains. Five major design perspectives or viewpoints were included in the survey, including the system design (top-down), product/component design (bottom-up), the manufacturing process-dominant case (product/component design under a specific process), the part-redesign perspective, and sustainability perspective. Manufacturability constraints within four design levels or scales were explored as well, ranging from macro-scale to sub-micro-scale design. Very little previous work was found in many areas, revealing several gaps in the literature. What is clearly needed is a more general, design-method-independent approach to collecting and enforcing manufacturability constraints. [ABSTRACT FROM AUTHOR]

Motivated by cyber-physical vulnerabilities in precision manufacturing processes, there is a need to externally examine the operational performance of computer numerically controlled (CNC) manufacturing systems. The overarching objective of this work is to design and fabricate a proof-of-concept CNC machine evaluation device, ultimately re-configurable to the mill, lathe, and 3D printing machine classes. This device will assist in identifying potential cyber-physical security threats in manufacturing systems by identifying perturbations outside the expected variations of machining processes and comparing the desired command inputted into the numerical controller and the actual machine performance (e.g., tool displacement, frequency). In this paper, a requirement-driven prototype device design method is presented and tested, and the results will be used to improve future iterations of the design. The first design iteration is tested on a Kuka KR 6 R700 series robotic arm, and machine movement comparisons are performed ex situ using Keyence laser measurement sensors. Data acquisition is performed with a Raspberry Pi 4 microcomputer, controlled by custom, cross-platform python code, and includes a touch screen human-computer interface (HCI). A device design adapted for a CNC mill is also presented, and the Haas TM-2 is used as a case study, which can be operated by CNC operators to assess machine performance, as needed, before a critical manufacturing process. This research will enable the broader goal of recognizing repeating advanced manufacturing performance deviations, which occur outside of a machine's normal variability, that could signal a malicious "attack.". [ABSTRACT FROM AUTHOR]

Manufacturing is a significant contributor to global greenhouse gas emissions and there is an urgent need to reduce the energy consumption of production processes. An important step towards this goal is proactively estimating process energy consumption at the detailed design stage. This is a challenging task as variabilities in factors such as process specifications, machine tool architecture, and workpiece geometry can significantly reduce the accuracy of the estimated energy consumption. This paper discusses a methodology for machine-specific energy estimation in milling processes at the detailed design stage based on the unit process life cycle inventory (UPLCI) model. We develop an adjusted UPLCI model that includes adjustment factors for uncertainties in machine tool specifications and the specific cutting energy of a workpiece material. These adjustment factors are calculated through experimental measurement of energy consumption for a reference test part on a specific machine tool. To validate the adjusted UPLCI model, we conducted a case study that measured the energy consumption for machining three parts made of Aluminum 6082 on two separate three-axis vertical milling machines, a Chevalier QP2040-L and a Leadwell MCV-OP. Results show that the UPLCI model consistently overestimated the total energy consumption for machining the three validation parts across both machine tools. We also found that the adjusted UPLCI model significantly reduced the estimation errors for the same tests for both machine tools. [ABSTRACT FROM AUTHOR]

Laser directed energy deposition (DED) is one of the most promising additive manufacturing processes for restoring high-value components. The damaged components can have complex free-form shapes, which necessitate depositions with an inclined nozzle, where the gravity can adversely affect the powder flow dynamics and the powder catchment efficiency (PCE). PCE is defined as the fraction of the total mass flowrate entering the melt pool, and a low PCE can render the process inviable. In this paper, the effect of nozzle inclination on the powder flow dynamics and resulting PCEs have been studied. It was found that the powder flow dynamics is altered significantly in an inclined nozzle and results in an asymmetric and skewed powder jet. The PCE deteriorates rapidly with an increase in the nozzle inclination due to the progressive defocusing and falls below 20% at 75 deg. A discrete phase model has been developed to understand the powder flow dynamics at different inclinations and process conditions. The mass flow distribution asymmetries on the focal plane at various nozzle inclinations have been analyzed via the model. The model can predict PCEs at different nozzle inclinations with reasonable accuracy ranging from 5.4% at 0-deg inclination to 29.2% at 45-deg inclination. The carrier gas flow, particle size, and laser diameter affect the PCE significantly and can be used to counter the enhanced powder loss at large nozzle inclinations. Process maps have been developed to identify the favorable, acceptable, and low PCE regions to select optimal DED parameters. [ABSTRACT FROM AUTHOR]

The manufacturing process of brim forming in paperboard containers consists of taking a thin paperboard shell and forming a brim to provide additional stiffness to the structure. A paper cup is an example of such a structure manufactured at rates exceeding 300 units per minute. A realistic model for the manufacturing process is not available and the effects of process and material parameters are not well understood. In this study, a finite element model of this highly nonlinear problem is presented. The model takes into account the material orthotropy and nonlinear elastic-plastic behavior, die paperboard contact interaction during loading and unloading, and friction between the metal die and paperboard, die geometry, and environmental conditions. Model predictions of the forcedisplacement curve agree well with the experimentally observed results. [ABSTRACT FROM AUTHOR]

The shapes of machined surfaces play a critical role affecting powertrain performance, and therefore, it is necessary to characterize the shapes with high resolution. State-of-the-art approaches for surface shape characterization are mostly data-driven by interpolating and extrapolating the spatial data but its precision is limited by the density of measurements. This paper explores the new opportunity of improving surface shape prediction through considering the similarity of multiple similar manufacturing processes. It is a common scenario when the process of interest lacks sufficient data whereas rich data could be available from other similar-but-not-identical processes. It is reasonable to transfer the insights gained from other relevant processes into the surface shape prediction. This paper develops an engineering-guided multitask learning (EG-MTL) surface model by fusing surface cutting physics in engineering processes and the spatial data from a number of similar-but-not-identical processes. An iterative multitask Gaussian process learning algorithm is developed to learn the model parameters. Compared with the conventional multitask learning, the proposed method has the advantages in incorporating the insights on cutting force variation during machining and is potentially able to improve the prediction performance given limited measurement data. The methodology is demonstrated based on the data from real-world machining processes in an engine plant. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) for metals is a widely researched, continuously enhanced manufacturing process and is implemented across various industries. However, the AM process exhibits variation that affects the geometric quality of the end product. The effect of process variation on geometric quality is rarely considered during the design stages. In this paper, sources that influence the geometric quality in a metal AM process are reviewed from a robust design perspective and further sorted into control factors and noise factors. A framework for geometric robustness analysis of AM products is presented as an outcome. This framework would facilitate development of methods and tools to produce geometry assured AM products. Also, the prospects of variation simulation to support geometric robustness analysis and the challenges associated with it are discussed. [ABSTRACT FROM AUTHOR]

Extrusion-based three-dimensional (3D) bio-printing deposits cell-laden bio-ink with high spatial resolution and may offer living tissue regeneration. Due to the biocompatibility, very low cytotoxicity, and high-water content, natural hydrogels are commonly considered as the cell-laden bio-ink for scaffold fabrication. However, due to the low mechanical integrity, a large-scale scaffold (>10 layers) with intricate architecture is a challenge. In this paper, we developed and characterized a novel bio-ink consisting of alginate, carboxymethyl cellulose (CMC), and 2,2,6,6 tetramethyl-1-piperidinyloxy (TEMPO)-mediated nanofibrillated cellulose (TO-NFC) for bio-printing applications. The potential of cellulose derivatives in terms of rheological property to satisfy scaffold architecture and cell viability is explored with a relatively small amount of solid content (<5%). By combining alginate, CMC, and TO-NFC as a hybrid hydrogel, we design to overcome their individual challenges as bio-ink. At the design stage, we have considered two main characteristics: printability and shape fidelity with quantitative indices following their rheological characteristics. Our proposed hydrogel blend (5% solid content) demonstrates a 0% collapse rate for 3-mm pillar distance and 25% fusion rate for 5 mm × 5 mm pore size which can ensure shape fidelity. We fabricated 42 layers and a 9-mm tall scaffold structure with relatively lower applied pressure (10 psi). The proposed hybrid hydrogel is used to prepare bio-ink encapsulating cells, and cell viability is measured as 90% after 10 days of incubation. [ABSTRACT FROM AUTHOR]