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Alloy steels are commonly used in many industrial and consumer products to take advantage of their strength, ductility, and toughness properties. In addition, their machinability and weldability performance make alloy steels suitable for a range of manufacturing operations. The advent of additive manufacturing technologies, such as wire and arc additive manufacturing (WAAM), has enabled welding of alloy steels into complex and customized near net-shape products. However, the functional reliability of as-built WAAM products is often uncertain due to a lack of understanding of the effects of process parameters on the material microstructure and mechanical properties that develop during welding, primarily driven by thermal phenomena. This study investigated the influence of thermal phenomena in WAAM on the microstructure and mechanical properties of two alloy steels (G4Si1, a mild steel, and AM70, a high-strength, low-alloy steel). The interrelationships between process parameters, heating and cooling cycles of the welded part, and the resultant microstructure and mechanical properties were characterized. The welded part experienced multiple reheating cycles, a consequence of the layer-by-layer manufacturing approach. Thus, high temperature gradients at the start of the weld formed fine grain structure, while coarser grains were formed as the height of the part increases and the temperature gradient decreased. Microstructural analysis identified the presence of acicular ferrite and equiaxed ferrite structures in G4Si1 welds, as well as a small volume fraction of pearlite along the ferrite grain boundaries. Analysis of AM70 welds found acicular ferrite, martensite, and bainite structures. Mechanical testing for both materials found that the hardness of the material decreased with the increase in the height of the welded part as a result of the decrease in the temperature gradient and cooling rate. In addition, higher hardness and yield strength, and lower elongation at failure was observed for parts printed using process parameters with lower energy input. The findings from this work can support automated process parameter tuning to control thermal phenomena during welding and, in turn, control the microstructure and mechanical properties of printed parts. publishedVersion

Research on human health and safety impacts of wire arc additive manufacturing is often overshadowed by the need for weld quality and mechanical strength improvements. To address this gap, a review of research literature is conducted focusing on the influence of welding process parameters, welding fumes, and fume exposure on worker health. The review uses a causal graph to classify research literature into two domains: manufacturing technology and public health. The graph serves as a precursor to development of a Bayesian network model, whose expected benefits, steps for implementation, and likely challenges that would be encountered during implementation are discussed. publishedVersion

Additive manufacturing is rapidly emerging as an alternative to conventional manufacturing, including subtractive processes, often attributed to its claim for sustainable product development, e.g., reduced cost, reduced energy and material use, and the distributed production of tailored consumer products. However, many of these benefits remain unsubstantiated for large-scale production. The aim of the research herein is to identify and characterize the factors influencing the systemic environmental performance of additive manufacturing as an end use of energy, using exergy analysis and life cycle assessment. These methods have been previously applied to evaluate the environmental performance of conventional and non-conventional manufacturing processes, and offer a validated approach to explore the environmental impacts of additive manufacturing with respect to systemic material and energy losses. In this study, the environmental impacts of direct metal laser sintering (DMLS) of iron metal powder and fused deposition modeling (FDM) of acrylonitrile styrene acrylate polymer filament are characterized by performing a thermodynamic (exergy) analysis of the resources and energy utilized and lost from cradle to gate. It is found that only 10% of total DMLS process inputs contribute to material processing, while 90% of the inputs are lost as bulk waste, heat, and work. For FDM, it is found that only 7% of total process inputs contribute to material processing, while 93% are lost. Following the exergy analysis, life cycle assessment is performed to characterize the environmental impacts of the exergy losses using single-issue indicator (Global Warming Potential, GWP) and aggregate indicator (ReCiPe 2008) methods. The results show that electricity consumption is a key contributor to both focal processes and their related upstream processes. The systemic GWP for DMLS is 69 kg CO2 equivalent, while for FDM it is 89 kg CO2 equivalent, per kilogram of material processed. Using the ReCiPe 2008 method, damage to human health is predicted to outweigh damage to ecosystem quality and resource availability for the DMLS process. For the FDM process, damage to human health and resource availability are predicted to outweigh damage to ecosystems quality. This work concludes that electrical energy use is the key contributor to systemic environmental impacts of additive manufacturing. Thus, it is imperative that we identify solutions to generate clean electrical energy, reduce electricity transmission losses, reduce material processing energy use, and design products that enable efficient additive manufacturing.

Recent advancements in information technology have enabled a paradigm shift in product manufacturing. Specifically, cloud-based distributed manufacturing has become a viable service for small and medium companies and prosumers, since it caters to the needs of these stakeholders. Distributed manufacturing offers significant benefits, including reduced cost, shorter lead time, and increased supply-chain flexibility. With interest in these services on the rise, however, there have not been substantial efforts to assess the sustainability performance of cloud-based distributed manufacturing. Sustainability assessment tools are not able to accommodate the unique attributes of cloud-based manufacturing, including broad geographic distribution, real-time autonomous customer support, and dynamic supply chains and processes driven by customer preferences. Thus, this research aims to develop an adaptive sustainability assessment framework for evaluating cloud-based distributed manufacturing systems using an integrated sustainability indicator for environmental, economic, and social impacts. The framework is demonstrated for distributed additive manufacturing as a service.

Additive manufacturing is rapidly emerging as an alternative to conventional manufacturing, including subtractive processes, often attributed to its claim for sustainable product development, e.g., reduced cost, reduced energy and material use, and the distributed production of tailored consumer products. However, many of these benefits remain unsubstantiated for large-scale production. The aim of the research herein is to identify the systemic contributors to environmental impacts for additive manufacturing using exergy analysis and life cycle impact assessment. These methods have been previously applied to evaluate the environmental performance of conventional and non-conventional manufacturing processes. Thus, they offer a validated approach to evaluate the environmental impacts of additive manufacturing with respect to systemic material and energy losses. In this study, the environmental impacts of direct metal laser sintering (DMLS) of iron metal powder is characterized by performing a thermodynamic analysis of the resources and energy utilized and lost from cradle-to-gate. It is found that only 6% of total process inputs in DMLS contribute to material processing, while 94% is lost as bulk waste, heat, and work. Life cycle impact assessments are performed to characterize the environmental impacts of these losses using single issue indicator (Global Warming Potential, GWP) and aggregate indicator (ReCiPe 2008) methods. The results show that electricity consumption is a key contributor to the focal process and its related upstream processes. The systemic GWP impact for DMLS was found to be 7.2 kg CO 2 equivalent. ReCiPe 2008 indicates damage to human health outweighs damage to ecosystem quality (E=84%, H=58%, I=51%) and resource availability (E=74%, H=29%, I=19%) for the DMLS process.

Increased competitiveness in the manufacturing industry demands optimizing performance at each level of an enterprise. Optimizing performance in terms of indicators such as manufacturing cost requires knowledge of cost-inducing variables from product design and manufacturing, and optimization of these variables. However, the number of variables that affect manufacturing cost is very high and optimizing all variables is time intensive and computationally difficult. Thus, it is important to identify and optimize select few variables that have high potential for inducing cost. Towards that goal, a dimension reduction method combining dimensional analysis conceptual modelling framework and graph centrality theory is proposed. The proposed method integrates existing knowledge of the cost inducing variables, their interactions, and input-output relationship for different functions or behavior of a system, in the form of a causal graph. Propagation of optimization objectives in the causal graph is checked to identify contradictory influences on the variables in the graph. Following the contradiction analysis, graph centrality theory is used to rank the different regions within the graph based on their relative importance to the optimization problem and to cluster the variables into two optimization groups namely, less important variables and most important variables relative to optimizing cost. The optimization problem is formulated to fix less important variables at their highest or lowest levels based on their interaction to cost and to optimize the more important variables to minimize cost. The proposed dimension reduction method is demonstrated for an optimization problem, to minimize the production cost of the bladder and key mechanism for a high-field superconducting magnet at CERN, capable of producing a 16 Tesla magnetic field. It was found that the graph region representing the electromagnetic force and resultant stress generated during energizing of the magnet ranked highest for influence on the bladder and key manufacturing cost. An optimization of the stress and its associated variables to minimize the manufacturing cost is performed using a genetic algorithm solver in Matlab.

Key performance indicators (KPIs) are used to monitor and improve production cost, quality, and time. A plethora of manufacturing KPIs are currently in use, with others continually being developed to meet organizational needs. However, obtaining the optimum KPI values at different organizational levels is challenging due to the complex interactions between manufacturing decisions, variables, and the desired targets. A Bayesian network is developed to characterize the interrelationships between manufacturing decisions and variables, selected KPI, and total production cost. For an additive manufacturing case, the approach enables appropriate KPI value estimation for achieving desired production cost targets in a manufacturing enterprise. publishedVersion

Implementation of additive manufacturing into product manufacturing suffers from the challenge of part defects prediction. Due to interdependencies of design variables and manufacturing parameters in achieving suitable part quality, modelling methods are necessary to provide simulation capabilities for part quality analysis at early stages of product development. A systematic methodology is proposed to extract cause-effect relationships among variables and to transform this causal model into a Bayesian network. The Bayesian network is then used to predict the effect of specific design and manufacturing parameters on part defects and to estimate the needed input parameters backwards, based on acceptable output values. publishedVersion

Additive manufacturing (AM) continues to rise in popularity due to its various advantages over traditional manufacturing processes. AM interests industry, but achieving repeatable production quality remains problematic for many AM technologies. Thus, modeling different process variables in AM using machine learning can be highly beneficial in creating useful knowledge of the process. Such developed artificial neural network (ANN) models would aid designers and manufacturers to make informed decisions about their products and processes. However, it is challenging to define an appropriate ANN topology that captures the AM system behavior. Toward that goal, an approach combining dimensional analysis conceptual modeling (DACM) and classical ANNs is proposed to create a new type of knowledge-based ANN (KB-ANN). This approach integrates existing literature and expert knowledge of the AM process to define a topology for the KB-ANN model. The proposed KB-ANN is a hybrid learning network that encompasses topological zones derived from knowledge of the process and other zones where missing knowledge is modeled using classical ANNs. The usefulness of the method is demonstrated using a case study to model wall thickness, part height, and total part mass in a fused deposition modeling (FDM) process. The KB-ANN-based model for FDM has the same performance with better generalization capabilities using fewer weights trained, when compared to a classical ANN.

Additive manufacturing (AM) continues to rise in popularity due to its various advantages over traditional manufacturing processes. AM interests industry, but achieving repeatable production quality remains problematic for many AM technologies. Thus, modeling the influence of process variables on the production quality in AM can be highly beneficial in creating useful knowledge of the process and product. An approach combining dimensional analysis conceptual modeling, mutual information based analysis, experimental sampling, factors selection, and modeling based on Knowledge-Based Artificial Neural Network (KB-ANN) is proposed for Fused Deposition Modeling (FDM) process. KB-ANN reduces the excessive amount of training samples required in traditional neural networks. The developed KB-ANN’s topology for FDM, integrates existing literature and expert knowledge of the process. The KB-ANN is compared to conventional ANN using prescribed performance metrics. This research presents a methodology to concurrently perform experiments, classify influential factors, limit the effect of noise in the modeled system, and model using KB-ANN. This research can contribute to the qualification efforts of AM technologies.

Plastic waste can be handled with several traditional waste management strategies, including landfilling, incineration, and recycling. Several novel strategies for recycling plastic waste have been proposed and researched for practical use, however the sustainability of the novel processes have not been analyzed in detail. This paper outlines a strategy for recycling polyethylene terephthalate (PET) waste through pyrolysis. A sustainability assessment of the process is conducted to analyze the environmental, economic, and social performance of recycling PET waste into crude oil. Environmental performance is evaluated using life cycle assessment, and the ReCiPe, cumulative energy demand (CED), and cumulative exergy demand (CExD) impact assessment methods. Net present value analysis is used to assess the economic impacts of the process. To complete a holistic sustainability assessment, social benefits and impacts are presented through a qualitative review of the process. In addition, an environmental impact analysis of the production of virgin PET using the recovered crude oil is presented and compared with traditional virgin PET production in terms of sustainability performance metrics. CED and CExD impact results emphasize the conceivable evidence of plastic waste as an energy source. The research highlights the use of standardized impact assessment methods to realize the environmental, economic, and social benefits of recycling PET into crude oil. Furthermore, a review of various process improvements broadens the potential for optimization of the novel process to further elucidate the benefits of plastic recycling.

Additive manufacturing has emerged as an arena that is receiving intense interest from numerous technology domains, traditional and non-traditional manufacturers. With this growing interest, concerns have arisen regarding the relative performance of these novel processes compared to conventional techniques from economic, environmental, and social perspectives. Sustainability-related benefits can be realized through additive manufacturing, and it is often promoted as a sustainable technology. For appropriate future development and application, however, it will be important to understand relative costs, environmental impacts, and human health effects of processes and materials. Prior research addressing sustainability and additive manufacturing is briefly reviewed. A life cycle assessment is then conducted to understand the environmental performance of a novel additive manufacturing process known as fast mask-image-projection based stereolithography (Fast MIP-SL). In Fast MIP-SL, projection light is patterned by a digital micromirror device as a mask image to selectively cure liquid photopolymer resin, and a two-way movement design is adopted to quickly recoat material. The cradle-to-gate life cycle assessment considers the impacts related to the curing of one resin type and the consumption of electricity in the production of parts of various geometries. Using the ReCiPe 2008 method (hierarchist weighting), it is found that damage to resource availability dominates ecosystems and human health damage types for each part assessed.Copyright © 2015 by ASME

Additive manufacturing has been presented as a novel and competitive method to achieve unprecedented part shapes and material complexities. Though this holds true in niche markets, the economic viability of additive manufacturing for large-scale industrial production is still in question. Companies often struggle to justify their investment in additive manufacturing due to challenges in the integration of such technologies into mainstream production. First, most additive technologies exhibit a relatively low production rate when compared with traditional production processes. Second, there is a lack of robust design for additive manufacturing methods and tools that enable the leveraging of the attendant unique capabilities, including the ability to form organic part geometries and automated part consolidations. Third, there is a dearth of systematic part screening methods to evaluate manufacturability in additive manufacturing. To tackle the challenge of manufacturability evaluation, the present work proposes a novel approach derived from latent semantic analysis and dimensional analysis to evaluate parts and their production for a variety of selected metrics. The selected metrics serve as descriptors of design features and manufacturing functions, which are developed using functional modeling and dimensional analysis theory. Singular-value decomposition and Euclidean distance measurement techniques are used to determine the relative manufacturability for a set of parts for a specified manufacturing process technology. The utility of the method is demonstrated for laser powder bed fusion technology. While demonstrated for additive manufacturing here, the developed approach can be expanded for any given set of manufacturing processes. Expansion of this systemic manufacturability analysis method can support part design decision-making, process selection, and design and manufacturing optimization.