In this article, an active learning strategy is introduced for reducing evaluation cost associated with system architecture design problems and is demonstrated using a circuit synthesis problem. While established circuit synthesis methods, such as efficient enumeration strategies and genetic algorithms (GAs), are available, evaluation of candidate architectures often requires computationally-expensive simulations, limiting the scale of solvable problems. Strategies are needed to explore architecture design spaces more efficiently, reducing the number of evaluations required to obtain good solutions. Active learning is a semi-supervised machine learning technique that constructs a predictive model. Here we use active learning to interactively query architecture data as a strategy to choose which candidate architectures to evaluate in a way that accelerates effective design search. Active learning is used to iteratively improve predictive model accuracy with strategically-selected training samples. The predictive model used here is an ensemble method, known as random forest. Several query strategies are compared. A circuit synthesis problem is used to test the active learning strategy; two complete data sets for this case study are available, aiding analysis. While active learning has been used for structured outputs, such as sequence labeling task, the interface between active learning and engineering design, particularly circuit synthesis, has not been well studied. The results indicate that active learning is a promising strategy in reducing the evaluation cost for the circuit synthesis problem, and provide insight into possible next steps for this general solution approach.
{"title":"Reducing Evaluation Cost for Circuit Synthesis Using Active Learning","authors":"Tinghao Guo, Daniel R. Herber, James T. Allison","doi":"10.1115/detc2018-85654","DOIUrl":"https://doi.org/10.1115/detc2018-85654","url":null,"abstract":"In this article, an active learning strategy is introduced for reducing evaluation cost associated with system architecture design problems and is demonstrated using a circuit synthesis problem. While established circuit synthesis methods, such as efficient enumeration strategies and genetic algorithms (GAs), are available, evaluation of candidate architectures often requires computationally-expensive simulations, limiting the scale of solvable problems. Strategies are needed to explore architecture design spaces more efficiently, reducing the number of evaluations required to obtain good solutions. Active learning is a semi-supervised machine learning technique that constructs a predictive model. Here we use active learning to interactively query architecture data as a strategy to choose which candidate architectures to evaluate in a way that accelerates effective design search. Active learning is used to iteratively improve predictive model accuracy with strategically-selected training samples. The predictive model used here is an ensemble method, known as random forest. Several query strategies are compared. A circuit synthesis problem is used to test the active learning strategy; two complete data sets for this case study are available, aiding analysis. While active learning has been used for structured outputs, such as sequence labeling task, the interface between active learning and engineering design, particularly circuit synthesis, has not been well studied. The results indicate that active learning is a promising strategy in reducing the evaluation cost for the circuit synthesis problem, and provide insight into possible next steps for this general solution approach.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116737987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Geometric tolerances for new products are sometimes assigned without specific knowledge of the cost or feasibility of manufacturing them to the assigned tolerances, which can significantly drive up production costs and lead to delays and design revisions. We present an interactive tool that quickly estimates the manufacturability of assigned tolerances for additive manufacturing and a compact visualization to present this information to the designer. The designer can use the system to explore feasible build orientations and then adjust specified tolerance limits if all tolerances are not simultaneously achievable at a single orientation. After the designer is satisfied that the range of feasible orientations has been fully explored, a physical programming approach is used to identify a single orientation to best satisfy the designer’s preferences. The calculation and visualization of the results is done in real-time, enabling quick iteration. A test case is presented to illustrate the use of the tool.
{"title":"An Interactive Manufacturability Analysis and Tolerance Allocation Tool for Additive Manufacturing","authors":"Hannah D. Budinoff, Sara McMains, A. Rinaldi","doi":"10.1115/DETC2018-86344","DOIUrl":"https://doi.org/10.1115/DETC2018-86344","url":null,"abstract":"Geometric tolerances for new products are sometimes assigned without specific knowledge of the cost or feasibility of manufacturing them to the assigned tolerances, which can significantly drive up production costs and lead to delays and design revisions. We present an interactive tool that quickly estimates the manufacturability of assigned tolerances for additive manufacturing and a compact visualization to present this information to the designer. The designer can use the system to explore feasible build orientations and then adjust specified tolerance limits if all tolerances are not simultaneously achievable at a single orientation. After the designer is satisfied that the range of feasible orientations has been fully explored, a physical programming approach is used to identify a single orientation to best satisfy the designer’s preferences. The calculation and visualization of the results is done in real-time, enabling quick iteration. A test case is presented to illustrate the use of the tool.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134184766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Customer preferences are found to evolve over time and correlate with geographical locations. Studying spatiotemporal heterogeneity of customer preferences is crucial to engineering design as it provides a dynamic perspective for a thorough understanding of preference trend. However, existing analytical models for demand modeling do not take the spatiotemporal heterogeneity of customer preferences into consideration. To fill this research gap, a spatial panel modeling approach is developed in this study to investigate the spatiotemporal heterogeneity of customer preferences by introducing engineering attributes explicitly as model inputs in support of demand forecasting in engineering design. In addition, a step-by-step procedure is proposed to aid the implementation of the approach. To demonstrate this approach, a case study is conducted on small SUV in China’s automotive market. Our results show that small SUVs with lower prices, higher power, and lower fuel consumption tend to have a positive impact on their sales in each region. In understanding the spatial patterns of China’s small SUV market, we found that each province has a unique spatial specific effect influencing the small SUV demand, which suggests that even if changing the design attributes of a product to the same extent, the resulting effects on product demand might be different across different regions. In understanding the underlying social-economic factors that drive the regional differences, it is found that Gross Domestic Product (GDP) per capita, length of paved roads per capita and household consumption expenditure have significantly positive influence on small SUV sales. These results demonstrate the potential capability of our approach in handling spatial variations of customers for product design and marketing strategy development. The main contribution of this research is the development of an analytical approach integrating spatiotemporal heterogeneity into demand modeling to support engineering design.
{"title":"Modeling Spatiotemporal Heterogeneity of Customer Preferences in Engineering Design","authors":"Youyi Bi, Jian Xie, Zhenghui Sha, Mingxian Wang, Yan Fu, Wei Chen","doi":"10.1115/DETC2018-86245","DOIUrl":"https://doi.org/10.1115/DETC2018-86245","url":null,"abstract":"Customer preferences are found to evolve over time and correlate with geographical locations. Studying spatiotemporal heterogeneity of customer preferences is crucial to engineering design as it provides a dynamic perspective for a thorough understanding of preference trend. However, existing analytical models for demand modeling do not take the spatiotemporal heterogeneity of customer preferences into consideration. To fill this research gap, a spatial panel modeling approach is developed in this study to investigate the spatiotemporal heterogeneity of customer preferences by introducing engineering attributes explicitly as model inputs in support of demand forecasting in engineering design. In addition, a step-by-step procedure is proposed to aid the implementation of the approach. To demonstrate this approach, a case study is conducted on small SUV in China’s automotive market. Our results show that small SUVs with lower prices, higher power, and lower fuel consumption tend to have a positive impact on their sales in each region. In understanding the spatial patterns of China’s small SUV market, we found that each province has a unique spatial specific effect influencing the small SUV demand, which suggests that even if changing the design attributes of a product to the same extent, the resulting effects on product demand might be different across different regions. In understanding the underlying social-economic factors that drive the regional differences, it is found that Gross Domestic Product (GDP) per capita, length of paved roads per capita and household consumption expenditure have significantly positive influence on small SUV sales. These results demonstrate the potential capability of our approach in handling spatial variations of customers for product design and marketing strategy development. The main contribution of this research is the development of an analytical approach integrating spatiotemporal heterogeneity into demand modeling to support engineering design.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124023353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research presents a convergence analysis for an iterative framework for optimizing plant and controller parameters for active systems. The optimization strategy fuses expensive yet valuable experiments with less accurate yet cheaper simulations. The numerical model is improved at each iteration through a cumulative correction law, using an optimally designed set of experiments. The iterative framework reduces the feasible design space between iterations, ultimately yielding convergence to a small design space that contains the optimum. This paper presents the derivation of an asymptotic upper bound on the difference between the corrected numerical model and true system response. Furthermore, convergence of the numerical model to the true system response and convergence of the design space are demonstrated on an airborne wind energy (AWE) application.
{"title":"Experimentally-Infused Active System Optimization Framework: Theoretical Convergence Analysis and Airborne Wind Energy Case Study","authors":"N. Deodhar, C. Vermillion","doi":"10.1115/DETC2018-85305","DOIUrl":"https://doi.org/10.1115/DETC2018-85305","url":null,"abstract":"This research presents a convergence analysis for an iterative framework for optimizing plant and controller parameters for active systems. The optimization strategy fuses expensive yet valuable experiments with less accurate yet cheaper simulations. The numerical model is improved at each iteration through a cumulative correction law, using an optimally designed set of experiments. The iterative framework reduces the feasible design space between iterations, ultimately yielding convergence to a small design space that contains the optimum. This paper presents the derivation of an asymptotic upper bound on the difference between the corrected numerical model and true system response. Furthermore, convergence of the numerical model to the true system response and convergence of the design space are demonstrated on an airborne wind energy (AWE) application.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129587282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents an explorative-based computational methodology to aid the analogical retrieval process in design-by-analogy practice. The computational methodology, driven by Non-negative Matrix Factorization (NMF), iteratively builds a hierarchical repositories of design solutions within which clusters of design analogies can be explored by designers. In the work, the methodology has been applied on a large repository of mechanical design related patents, processed to contain only component-, behavior-, or material-based content, to demonstrate that unique and valuable attribute-based analogical inspiration can be discovered from different representations of patent data. For explorative purposes, the hierarchical repositories have been visualized with a three-dimensional hierarchical structure and two-dimensional bar graph structure, which can be used interchangeably for retrieving analogies. This paper demonstrates that the explorative-based computational methodology provides designers an enhanced control over design repositories, empowering them to retrieve analogical inspiration for design-by-analogy practice.
{"title":"Approaches for Supporting Exploration for Analogical Inspiration With Behavior, Material and Component Based Structural Representations of Patent Databases","authors":"H. Song, Katherine K. Fu","doi":"10.1115/DETC2018-85591","DOIUrl":"https://doi.org/10.1115/DETC2018-85591","url":null,"abstract":"This paper presents an explorative-based computational methodology to aid the analogical retrieval process in design-by-analogy practice. The computational methodology, driven by Non-negative Matrix Factorization (NMF), iteratively builds a hierarchical repositories of design solutions within which clusters of design analogies can be explored by designers. In the work, the methodology has been applied on a large repository of mechanical design related patents, processed to contain only component-, behavior-, or material-based content, to demonstrate that unique and valuable attribute-based analogical inspiration can be discovered from different representations of patent data. For explorative purposes, the hierarchical repositories have been visualized with a three-dimensional hierarchical structure and two-dimensional bar graph structure, which can be used interchangeably for retrieving analogies. This paper demonstrates that the explorative-based computational methodology provides designers an enhanced control over design repositories, empowering them to retrieve analogical inspiration for design-by-analogy practice.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"435 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123198466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sheng Shen, Mohammadkazem Sadoughi, Xiangyi Chen, Mingyi Hong, Chao Hu
Over the past two decades, safety and reliability of lithium-ion (Li-ion) rechargeable batteries have been receiving a considerable amount of attention from both industry and academia. To guarantee safe and reliable operation of a Li-ion battery pack and build failure resilience in the pack, battery management systems (BMSs) should possess the capability to monitor, in real time, the state of health (SOH) of the individual cells in the pack. This paper presents a deep learning method, named deep convolutional neural networks, for cell-level SOH assessment based on the capacity, voltage, and current measurements during a charge cycle. The unique features of deep convolutional neural networks include the local connectivity and shared weights, which enable the model to estimate battery capacity accurately using the measurements during charge. To our knowledge, this is the first attempt to apply deep learning to online SOH assessment of Li-ion battery. 10-year daily cycling data from implantable Li-ion cells are used to verify the performance of the proposed method. Compared with traditional machine learning methods such as relevance vector machine and shallow neural networks, the proposed method is demonstrated to produce higher accuracy and robustness in capacity estimation.
{"title":"Online Estimation of Lithium-Ion Battery Capacity Using Deep Convolutional Neural Networks","authors":"Sheng Shen, Mohammadkazem Sadoughi, Xiangyi Chen, Mingyi Hong, Chao Hu","doi":"10.1115/DETC2018-86347","DOIUrl":"https://doi.org/10.1115/DETC2018-86347","url":null,"abstract":"Over the past two decades, safety and reliability of lithium-ion (Li-ion) rechargeable batteries have been receiving a considerable amount of attention from both industry and academia. To guarantee safe and reliable operation of a Li-ion battery pack and build failure resilience in the pack, battery management systems (BMSs) should possess the capability to monitor, in real time, the state of health (SOH) of the individual cells in the pack. This paper presents a deep learning method, named deep convolutional neural networks, for cell-level SOH assessment based on the capacity, voltage, and current measurements during a charge cycle. The unique features of deep convolutional neural networks include the local connectivity and shared weights, which enable the model to estimate battery capacity accurately using the measurements during charge. To our knowledge, this is the first attempt to apply deep learning to online SOH assessment of Li-ion battery. 10-year daily cycling data from implantable Li-ion cells are used to verify the performance of the proposed method. Compared with traditional machine learning methods such as relevance vector machine and shallow neural networks, the proposed method is demonstrated to produce higher accuracy and robustness in capacity estimation.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122236339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The presented research demonstrates the synthesis of two-dimensional kinematic mechanisms using feature-based reinforcement learning. As a running example the classic challenge of designing a straight-line mechanism is adopted: a mechanism capable of tracing a straight line as part of its trajectory. This paper presents a basic framework, consisting of elements such as mechanism representations, kinematic simulations and learning algorithms, as well as some of the resulting mechanisms and a comparison to prior art. Series of successful mechanisms have been synthesized for path generation of a straight line and figure-eight.
{"title":"Kinematic Synthesis Using Reinforcement Learning","authors":"Kaz Vermeer, Reinier Kuppens, J. Herder","doi":"10.1115/DETC2018-85529","DOIUrl":"https://doi.org/10.1115/DETC2018-85529","url":null,"abstract":"The presented research demonstrates the synthesis of two-dimensional kinematic mechanisms using feature-based reinforcement learning. As a running example the classic challenge of designing a straight-line mechanism is adopted: a mechanism capable of tracing a straight line as part of its trajectory. This paper presents a basic framework, consisting of elements such as mechanism representations, kinematic simulations and learning algorithms, as well as some of the resulting mechanisms and a comparison to prior art. Series of successful mechanisms have been synthesized for path generation of a straight line and figure-eight.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"316 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134213046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Conner Sharpe, C. Seepersad, S. Watts, D. Tortorelli
Advances in additive manufacturing processes have made it possible to build mechanical metamaterials with bulk properties that exceed those of naturally occurring materials. One class of these metamaterials is structural lattices that can achieve high stiffness to weight ratios. Recent work on geometric projection approaches has introduced the possibility of optimizing these architected lattice designs in a drastically reduced parameter space. The reduced number of design variables enables application of a new class of methods for exploring the design space. This work investigates the use of Bayesian optimization, a technique for global optimization of expensive non-convex objective functions through surrogate modeling. We utilize formulations for implementing probabilistic constraints in Bayesian optimization to aid convergence in this highly constrained engineering problem, and demonstrate results with a variety of stiff lightweight lattice designs.
{"title":"Design of Mechanical Metamaterials via Constrained Bayesian Optimization","authors":"Conner Sharpe, C. Seepersad, S. Watts, D. Tortorelli","doi":"10.1115/DETC2018-85270","DOIUrl":"https://doi.org/10.1115/DETC2018-85270","url":null,"abstract":"Advances in additive manufacturing processes have made it possible to build mechanical metamaterials with bulk properties that exceed those of naturally occurring materials. One class of these metamaterials is structural lattices that can achieve high stiffness to weight ratios. Recent work on geometric projection approaches has introduced the possibility of optimizing these architected lattice designs in a drastically reduced parameter space. The reduced number of design variables enables application of a new class of methods for exploring the design space. This work investigates the use of Bayesian optimization, a technique for global optimization of expensive non-convex objective functions through surrogate modeling. We utilize formulations for implementing probabilistic constraints in Bayesian optimization to aid convergence in this highly constrained engineering problem, and demonstrate results with a variety of stiff lightweight lattice designs.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130296250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Additive manufacturing (AM) has unique capabilities when compared to traditional manufacturing, such as shape, hierarchical, functional, and material complexity, a fact that has fascinated those in research, industry, and the media for the last decade. Consequently, designers would like to know how they can incorporate AM’s special capabilities into their designs, but are often at a loss as to how to do so. Design for Additive Manufacturing (DfAM) methods are currently in development but the vast majority of existing methods are not tailored to the needs and knowledge of designers in the early stages of the design a process. The authors have previously derived 29 design heuristics for AM. In this paper, the efficacy of these heuristics is tested in the context of a re-design scenario with novice designers. The preliminary results show that the heuristics positively influence the designs generated by the novice designers. Analysis of the use of specific heuristics by the participants and future research to validate the impact of the design heuristics for additive manufacturing with expert designers and in original design scenarios is planned.
{"title":"Preliminary User Study on Design Heuristics for Additive Manufacturing","authors":"Alexandra Blösch-Paidosh, K. Shea","doi":"10.1115/DETC2018-85908","DOIUrl":"https://doi.org/10.1115/DETC2018-85908","url":null,"abstract":"Additive manufacturing (AM) has unique capabilities when compared to traditional manufacturing, such as shape, hierarchical, functional, and material complexity, a fact that has fascinated those in research, industry, and the media for the last decade. Consequently, designers would like to know how they can incorporate AM’s special capabilities into their designs, but are often at a loss as to how to do so. Design for Additive Manufacturing (DfAM) methods are currently in development but the vast majority of existing methods are not tailored to the needs and knowledge of designers in the early stages of the design a process. The authors have previously derived 29 design heuristics for AM. In this paper, the efficacy of these heuristics is tested in the context of a re-design scenario with novice designers. The preliminary results show that the heuristics positively influence the designs generated by the novice designers. Analysis of the use of specific heuristics by the participants and future research to validate the impact of the design heuristics for additive manufacturing with expert designers and in original design scenarios is planned.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121948089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The use of additive manufacturing (AM) for fabricating industrial grade components has increased significantly in recent years. Numerous industrial entities are looking to leverage new AM techniques to enable fabrication of components that were typically manufactured previously using conventional manufacturing techniques such as subtractive manufacturing or casting. Therefore, it is becoming increasingly important to be able to rigorously evaluate the technical and economic feasibility of additively manufacturing a component relative to conventional alternatives. In order to support this evaluation, this paper presents a framework that investigates fabrication feasibility for AM from three perspectives: geometric evaluation, build orientation/support generation, and resources necessary (i.e., cost and time). The core functionality of the framework is enabled on voxelized model representation, discrete and binary formats of 3D continuous objects. AM fabrication feasibility analysis is applied to 34 various parts representing a wide range of manifolds and valves manufactured using conventional manufacturing techniques, components commonly found in the aerospace industry. Results obtained illustrate the capability and generalizability of the framework to analyze intricate geometries and provide a primary assessment for the feasibility of the AM process.
{"title":"From Conventional to Additive Manufacturing: Determining Component Fabrication Feasibility","authors":"S. E. Ghiasian, Prakhar Jaiswal, R. Rai, K. Lewis","doi":"10.1115/DETC2018-86238","DOIUrl":"https://doi.org/10.1115/DETC2018-86238","url":null,"abstract":"The use of additive manufacturing (AM) for fabricating industrial grade components has increased significantly in recent years. Numerous industrial entities are looking to leverage new AM techniques to enable fabrication of components that were typically manufactured previously using conventional manufacturing techniques such as subtractive manufacturing or casting. Therefore, it is becoming increasingly important to be able to rigorously evaluate the technical and economic feasibility of additively manufacturing a component relative to conventional alternatives. In order to support this evaluation, this paper presents a framework that investigates fabrication feasibility for AM from three perspectives: geometric evaluation, build orientation/support generation, and resources necessary (i.e., cost and time). The core functionality of the framework is enabled on voxelized model representation, discrete and binary formats of 3D continuous objects. AM fabrication feasibility analysis is applied to 34 various parts representing a wide range of manifolds and valves manufactured using conventional manufacturing techniques, components commonly found in the aerospace industry. Results obtained illustrate the capability and generalizability of the framework to analyze intricate geometries and provide a primary assessment for the feasibility of the AM process.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"269 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122135938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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