Engineering design under uncertainty is an established field. Attempts to extricate the human decision maker from the process generally do not succeed. Surprisingly, even the determination of system parameters and their admissible values needs as many interventional steps from human designers and operators, as the selection of final attributes of the system that the human end user is expected to only interact and be concerned with. In this light, it becomes important to consider the mathematical models that would explain and model the decision making behavior of human beings. Concerningly, this behavior has been seen to violate common sense probability axioms. In this paper, we propose an earnest look at the mathematics of quantum mechanical theory in modeling and manipulating the uncertainties involved in engineering systems. We propose that the state of a system be modeled as a point in an abstract complex vector space as in quantum mechanics. Additionally, at a given point in time it can be interpreted as a superposition of multiple pure states. This change in perspective allows explanation of many commonly observed behaviors, least of which is the inconsistencies in defining what constitutes the failure of a system. We present our approach in the context of reliability engineering as it sees some of the most prevalent use of uncertainty modeling and propagation techniques. However, the implications on design and design theory are also evident. Some motivating examples are provided and directions for future work are identified.
{"title":"Quantum Mechanical Perspectives in Reliability Engineering and System Design","authors":"Vijitashwa Pandey","doi":"10.1115/detc2019-98028","DOIUrl":"https://doi.org/10.1115/detc2019-98028","url":null,"abstract":"\u0000 Engineering design under uncertainty is an established field. Attempts to extricate the human decision maker from the process generally do not succeed. Surprisingly, even the determination of system parameters and their admissible values needs as many interventional steps from human designers and operators, as the selection of final attributes of the system that the human end user is expected to only interact and be concerned with. In this light, it becomes important to consider the mathematical models that would explain and model the decision making behavior of human beings. Concerningly, this behavior has been seen to violate common sense probability axioms. In this paper, we propose an earnest look at the mathematics of quantum mechanical theory in modeling and manipulating the uncertainties involved in engineering systems. We propose that the state of a system be modeled as a point in an abstract complex vector space as in quantum mechanics. Additionally, at a given point in time it can be interpreted as a superposition of multiple pure states. This change in perspective allows explanation of many commonly observed behaviors, least of which is the inconsistencies in defining what constitutes the failure of a system. We present our approach in the context of reliability engineering as it sees some of the most prevalent use of uncertainty modeling and propagation techniques. However, the implications on design and design theory are also evident. Some motivating examples are provided and directions for future work are identified.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129933525","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}
We investigate the effect of competitor product representation on optimal design results in profit-maximization studies. Specifically, we study the implications of replacing a large set of product alternatives available in the marketplace with a reduced set of selected competitors or with composite alternatives, as is common in the literature. We derive first-order optimality conditions and show that optimal design (but not price) is independent of competitors under the logit and nested logit models (where preference coefficients are homogeneous), but optimal design results may depend on competitor representation in latent class and mixed logit models (where preference coefficients are heterogeneous). In a case study of automotive powertrain design using mixed logit demand, we find some change in the optimal acceleration performance value when competitors are modeled using a small set of alternatives rather than the larger set. The magnitude of this change depends on the specific form and parameters of the cost and demand functions assumed, ranging from 0% to 3% in our case study. We find that the magnitude of the change in optimal design variables induced by competitor representation in our case study increases with the heterogeneity of preference coefficients across consumers and changes with the curvature of the cost function.
{"title":"Implications of Competitor Representation on Optimal Design","authors":"Arthur Yip, Jeremy J. Michalek, Kate S. Whitefoot","doi":"10.1115/detc2019-98114","DOIUrl":"https://doi.org/10.1115/detc2019-98114","url":null,"abstract":"\u0000 We investigate the effect of competitor product representation on optimal design results in profit-maximization studies. Specifically, we study the implications of replacing a large set of product alternatives available in the marketplace with a reduced set of selected competitors or with composite alternatives, as is common in the literature. We derive first-order optimality conditions and show that optimal design (but not price) is independent of competitors under the logit and nested logit models (where preference coefficients are homogeneous), but optimal design results may depend on competitor representation in latent class and mixed logit models (where preference coefficients are heterogeneous). In a case study of automotive powertrain design using mixed logit demand, we find some change in the optimal acceleration performance value when competitors are modeled using a small set of alternatives rather than the larger set. The magnitude of this change depends on the specific form and parameters of the cost and demand functions assumed, ranging from 0% to 3% in our case study. We find that the magnitude of the change in optimal design variables induced by competitor representation in our case study increases with the heterogeneity of preference coefficients across consumers and changes with the curvature of the cost function.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123103556","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}
Topology optimization has been proved to be an automatic, efficient and powerful tool for structural designs. In recent years, the focus of structural topology optimization has evolved from mono-scale, single material structural designs to hierarchical multimaterial structural designs. In this research, the multi-material structural design is carried out in a concurrent parametric level set framework so that the structural topologies in the macroscale and the corresponding material properties in mesoscale can be optimized simultaneously. The constructed cardinal basis function (CBF) is utilized to parameterize the level set function. With CBF, the upper and lower bounds of the design variables can be identified explicitly, compared with the trial and error approach when the radial basis function (RBF) is used. In the macroscale, the ‘color’ level set is employed to model the multiple material phases, where different materials are represented using combined level set functions like mixing colors from primary colors. At the end of this optimization, the optimal material properties for different constructing materials will be identified. By using those optimal values as targets, a second structural topology optimization is carried out to determine the exact mesoscale metamaterial structural layout. In both the macroscale and the mesoscale structural topology optimization, an energy functional is utilized to regularize the level set function to be a distance-regularized level set function, where the level set function is maintained as a signed distance function along the design boundary and kept flat elsewhere. The signed distance slopes can ensure a steady and accurate material property interpolation from the level set model to the physical model. The flat surfaces can make it easier for the level set function to penetrate its zero level to create new holes. After obtaining both the macroscale structural layouts and the mesoscale metamaterial layouts, the hierarchical multimaterial structure is finalized via a local-shape-preserving conformal mapping to preserve the designed material properties. Unlike the conventional conformal mapping using the Ricci flow method where only four control points are utilized, in this research, a multi-control-point conformal mapping is utilized to be more flexible and adaptive in handling complex geometries. The conformally mapped multi-material hierarchical structure models can be directly used for additive manufacturing, concluding the entire process of designing, mapping, and manufacturing.
{"title":"Generative Design of Multi-Material Hierarchical Structures via Concurrent Topology Optimization and Conformal Geometry Method","authors":"Long Jiang, Shikui Chen, X. Gu","doi":"10.1115/detc2019-97617","DOIUrl":"https://doi.org/10.1115/detc2019-97617","url":null,"abstract":"\u0000 Topology optimization has been proved to be an automatic, efficient and powerful tool for structural designs. In recent years, the focus of structural topology optimization has evolved from mono-scale, single material structural designs to hierarchical multimaterial structural designs. In this research, the multi-material structural design is carried out in a concurrent parametric level set framework so that the structural topologies in the macroscale and the corresponding material properties in mesoscale can be optimized simultaneously. The constructed cardinal basis function (CBF) is utilized to parameterize the level set function. With CBF, the upper and lower bounds of the design variables can be identified explicitly, compared with the trial and error approach when the radial basis function (RBF) is used. In the macroscale, the ‘color’ level set is employed to model the multiple material phases, where different materials are represented using combined level set functions like mixing colors from primary colors. At the end of this optimization, the optimal material properties for different constructing materials will be identified. By using those optimal values as targets, a second structural topology optimization is carried out to determine the exact mesoscale metamaterial structural layout. In both the macroscale and the mesoscale structural topology optimization, an energy functional is utilized to regularize the level set function to be a distance-regularized level set function, where the level set function is maintained as a signed distance function along the design boundary and kept flat elsewhere. The signed distance slopes can ensure a steady and accurate material property interpolation from the level set model to the physical model. The flat surfaces can make it easier for the level set function to penetrate its zero level to create new holes. After obtaining both the macroscale structural layouts and the mesoscale metamaterial layouts, the hierarchical multimaterial structure is finalized via a local-shape-preserving conformal mapping to preserve the designed material properties. Unlike the conventional conformal mapping using the Ricci flow method where only four control points are utilized, in this research, a multi-control-point conformal mapping is utilized to be more flexible and adaptive in handling complex geometries. The conformally mapped multi-material hierarchical structure models can be directly used for additive manufacturing, concluding the entire process of designing, mapping, and manufacturing.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114996472","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}
Movement in compliant mechanisms is achieved, at least in part, via deformable flexible members, rather than using articulating joints. These flexible members are traditionally modeled using Finite Element Models (FEMs). In this article, an alternative strategy for modeling compliant cantilever beams is developed with the objectives of reducing computational expense, and providing accuracy with respect to design optimization solutions. The method involves approximating the response of a flexible beam with an n-link/m-joint Pseudo-Rigid Body Dynamic Model (PRBDM). Traditionally, PRBDM models have shown an approximation of compliant elements using 2 or 3 revolute joints (2R/3R-PRBDM). In this study, a more general nR-PRBDM model is developed. The first n resonant frequencies of the PRBDM are matched to exact or FEM solutions to approximate the response of the compliant system. These models can be used for co-design studies of flexible structural members, and are capable of modeling higher deflection of compliant elements.
{"title":"Pseudo-Rigid Body Dynamic Modeling of Compliant Members for Design","authors":"Vedant","doi":"10.1115/detc2019-97881","DOIUrl":"https://doi.org/10.1115/detc2019-97881","url":null,"abstract":"\u0000 Movement in compliant mechanisms is achieved, at least in part, via deformable flexible members, rather than using articulating joints. These flexible members are traditionally modeled using Finite Element Models (FEMs). In this article, an alternative strategy for modeling compliant cantilever beams is developed with the objectives of reducing computational expense, and providing accuracy with respect to design optimization solutions. The method involves approximating the response of a flexible beam with an n-link/m-joint Pseudo-Rigid Body Dynamic Model (PRBDM). Traditionally, PRBDM models have shown an approximation of compliant elements using 2 or 3 revolute joints (2R/3R-PRBDM). In this study, a more general nR-PRBDM model is developed. The first n resonant frequencies of the PRBDM are matched to exact or FEM solutions to approximate the response of the compliant system. These models can be used for co-design studies of flexible structural members, and are capable of modeling higher deflection of compliant elements.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124403732","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 focuses on the idea of energy efficient cooperative collision avoidance between two quadcopters. Two strategies for reciprocal online collision-avoiding actions (i.e., coherent maneuvers without requiring any real-time consensus) are proposed. In the first strategy, UAVs change their speed, while in the second strategy they change their heading to avoid a collision. The avoidance actions are parameterized in terms of the time difference between detecting the collision and starting the maneuver and the amount of speed/heading change. These action parameters are used to generate intermediate way-points, subsequently translated into a minimum snap trajectory, to be executed by a PD controller. For realism, the relative pose of the other UAV, estimated by each UAV (at the point of detection), is considered to be uncertain — thereby presenting substantial challenges to undertaking reciprocal actions. Performing supervised learning based on optimization derived labels (as done in prior work) becomes computationally burden-some under these uncertainties. Instead, an (unsupervised) neuroevolution algorithm, called AGENT, is employed to learn a neural network (NN) model that takes the initial (uncertain) pose as state inputs and maps it to a robust optimal action. In neuroevolution, the NN topology and weights are simultaneously optimized using a special evolutionary process, where the fitness of candidate NNs are evaluated over a set of sample (in this case, various collision) scenarios. For further computational tractability, a surrogate model is used to estimate the energy consumption and a classifier is used to identify trajectories where the controller fails. The trained neural network shows encouraging performance for collision avoidance over a large variety of unseen scenarios.
{"title":"A Neuroevolution-Based Learning of Reciprocal Maneuver for Collision Avoidance in Quadcopters Under Pose Uncertainties","authors":"A. Behjat, Krushang Gabani, Souma Chowdhury","doi":"10.1115/detc2019-97975","DOIUrl":"https://doi.org/10.1115/detc2019-97975","url":null,"abstract":"\u0000 This paper focuses on the idea of energy efficient cooperative collision avoidance between two quadcopters. Two strategies for reciprocal online collision-avoiding actions (i.e., coherent maneuvers without requiring any real-time consensus) are proposed. In the first strategy, UAVs change their speed, while in the second strategy they change their heading to avoid a collision. The avoidance actions are parameterized in terms of the time difference between detecting the collision and starting the maneuver and the amount of speed/heading change. These action parameters are used to generate intermediate way-points, subsequently translated into a minimum snap trajectory, to be executed by a PD controller. For realism, the relative pose of the other UAV, estimated by each UAV (at the point of detection), is considered to be uncertain — thereby presenting substantial challenges to undertaking reciprocal actions. Performing supervised learning based on optimization derived labels (as done in prior work) becomes computationally burden-some under these uncertainties. Instead, an (unsupervised) neuroevolution algorithm, called AGENT, is employed to learn a neural network (NN) model that takes the initial (uncertain) pose as state inputs and maps it to a robust optimal action. In neuroevolution, the NN topology and weights are simultaneously optimized using a special evolutionary process, where the fitness of candidate NNs are evaluated over a set of sample (in this case, various collision) scenarios. For further computational tractability, a surrogate model is used to estimate the energy consumption and a classifier is used to identify trajectories where the controller fails. The trained neural network shows encouraging performance for collision avoidance over a large variety of unseen scenarios.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134211330","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}
Zhenguo Nie, Sangjin Jung, L. Kara, Kate S. Whitefoot
This research presents a method of evaluating and optimizing the consolidation of parts in an assembly using metal additive manufacturing (MAM). The method generates candidates for consolidation, filters them for feasibility and structural redundancy, finds the optimal build layout of the parts, and optimizes which parts to consolidate using a genetic algorithm. Optimal results are presented for both minimal production time and minimal production costs, respectively. The production time and cost model considers each step of the manufacturing process, including MAM build, post-processing steps such as support-structure removal, and assembly. It accounts for costs affected by parts consolidation, including machine costs, material, scrap, energy consumption, and labor requirements. We find that developing a closed-loop filter that excludes consolidation candidates with structural redundancy dramatically reduces the number of candidates to consider, thereby significantly reducing convergence time. Results show that, when increasing the number of parts that are consolidated, the production cost and time at first decrease due to reduced assembly steps, and then increase due to additional support structures needed to uphold the larger, consolidated parts. We present a rationale and evidence justifying that this is an inherent tradeoff of parts consolidation that generalizes to most types of assemblies. Subsystems that can be oriented with very little support structures, or have low material costs or fast deposition rates can have an optimum at full consolidation; otherwise, the optimum is likely to be less than 100%. The presented method offers a promising pathway to minimize production time and cost by consolidating parts using MAM. In our test-bed results on an aircraft fairing produced with powder-bed electron-beam melting, the solution for minimizing time is to consolidate 48 components into three discrete parts, which leads to a 33% reduction in unit production time. The solution for minimizing production costs is to consolidate the components into five discrete parts, leading to a 28% reduction in unit costs.
{"title":"Optimization of Parts Consolidation for Minimum Production Costs and Time Using Additive Manufacturing","authors":"Zhenguo Nie, Sangjin Jung, L. Kara, Kate S. Whitefoot","doi":"10.1115/detc2019-97649","DOIUrl":"https://doi.org/10.1115/detc2019-97649","url":null,"abstract":"\u0000 This research presents a method of evaluating and optimizing the consolidation of parts in an assembly using metal additive manufacturing (MAM). The method generates candidates for consolidation, filters them for feasibility and structural redundancy, finds the optimal build layout of the parts, and optimizes which parts to consolidate using a genetic algorithm. Optimal results are presented for both minimal production time and minimal production costs, respectively. The production time and cost model considers each step of the manufacturing process, including MAM build, post-processing steps such as support-structure removal, and assembly. It accounts for costs affected by parts consolidation, including machine costs, material, scrap, energy consumption, and labor requirements. We find that developing a closed-loop filter that excludes consolidation candidates with structural redundancy dramatically reduces the number of candidates to consider, thereby significantly reducing convergence time. Results show that, when increasing the number of parts that are consolidated, the production cost and time at first decrease due to reduced assembly steps, and then increase due to additional support structures needed to uphold the larger, consolidated parts. We present a rationale and evidence justifying that this is an inherent tradeoff of parts consolidation that generalizes to most types of assemblies. Subsystems that can be oriented with very little support structures, or have low material costs or fast deposition rates can have an optimum at full consolidation; otherwise, the optimum is likely to be less than 100%. The presented method offers a promising pathway to minimize production time and cost by consolidating parts using MAM. In our test-bed results on an aircraft fairing produced with powder-bed electron-beam melting, the solution for minimizing time is to consolidate 48 components into three discrete parts, which leads to a 33% reduction in unit production time. The solution for minimizing production costs is to consolidate the components into five discrete parts, leading to a 28% reduction in unit costs.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128332612","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}
Silicon-based anode is one of the promising candidates for the next generation lithium ion batteries (LIBs) to achieve high power/energy density. However, the major drawback limiting the practical application of Si anode is that Si experiences significant volume change during its lithiation/de-lithiation cycles, which induces high stress and causes degradation and pulverization of the anode. This study focuses on the crack initiation performances of Si anode during the de-lithiation process. A multi-physics based finite element (FE) model is built to simulate the electrochemical process and crack generation during de-lithiation. On top of that, a Gaussian Processes (GP) based surrogate model is developed to assist the exploration of the crack initiation performances within the anode design space. It is found that, the thickness of the Si coating layer TSi, the yield strength σFc of Si material, the cohesive strength between Si and substrate σFs, and the curvature of the substrate ρ have large impacts on the cracking behavior of Si. This coupled FE simulation-GP surrogate model framework is also applicable to other types of LIB electrodes.
{"title":"Gaussian Process Based Crack Initiation Modeling for Design of Battery Anode Materials","authors":"Zhuoyuan Zheng, Yanwen Xu, Bo Chen, Pingfeng Wang","doi":"10.1115/detc2019-97547","DOIUrl":"https://doi.org/10.1115/detc2019-97547","url":null,"abstract":"\u0000 Silicon-based anode is one of the promising candidates for the next generation lithium ion batteries (LIBs) to achieve high power/energy density. However, the major drawback limiting the practical application of Si anode is that Si experiences significant volume change during its lithiation/de-lithiation cycles, which induces high stress and causes degradation and pulverization of the anode. This study focuses on the crack initiation performances of Si anode during the de-lithiation process. A multi-physics based finite element (FE) model is built to simulate the electrochemical process and crack generation during de-lithiation. On top of that, a Gaussian Processes (GP) based surrogate model is developed to assist the exploration of the crack initiation performances within the anode design space. It is found that, the thickness of the Si coating layer TSi, the yield strength σFc of Si material, the cohesive strength between Si and substrate σFs, and the curvature of the substrate ρ have large impacts on the cracking behavior of Si. This coupled FE simulation-GP surrogate model framework is also applicable to other types of LIB electrodes.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116572135","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) offers access to the entire volume of a printed artifact during the build operation. This makes it possible to embedding foreign components (e.g. sensors, motors, actuators) into AM parts, thus enabling multifunctional products directly from the build tray. However, the process of designing for embedding currently requires extensive designer expertise in AM. Current methods rely on a designer to select an orientation for the embedded component and design a cavity such that the component can be successfully embedded without compromising the print quality of the final part. For irregular geometries, additional design knowledge is required to prepare a shape converter: a secondary piece to ensure a flush deposition surface on top of the embedded component. This research aims to develop a tool to automate these different design decisions for in-situ embedding, thus reducing the need for expert design knowledge. A three-stage process is proposed to 1) find the optimum orientation based on cavity volume and cross-section area, 2) create the necessary cavity geometry to successfully insert the component, and 3) perform a Boolean operation to create the digital design for any requisite shape converter. Performance of the tool is demonstrated with four test cases with varying levels of geometric complexity. These test cases show that the proposed process successfully handles arbitrary embedded geometries, though several limitations are noted for future work.
{"title":"Digital Design Automation to Support In-Situ Embedding of Functional Components in Additive Manufacturing","authors":"Manoj Malviya, Swapnil Sinha, N. Meisel","doi":"10.1115/detc2019-97607","DOIUrl":"https://doi.org/10.1115/detc2019-97607","url":null,"abstract":"\u0000 Additive manufacturing (AM) offers access to the entire volume of a printed artifact during the build operation. This makes it possible to embedding foreign components (e.g. sensors, motors, actuators) into AM parts, thus enabling multifunctional products directly from the build tray. However, the process of designing for embedding currently requires extensive designer expertise in AM. Current methods rely on a designer to select an orientation for the embedded component and design a cavity such that the component can be successfully embedded without compromising the print quality of the final part. For irregular geometries, additional design knowledge is required to prepare a shape converter: a secondary piece to ensure a flush deposition surface on top of the embedded component. This research aims to develop a tool to automate these different design decisions for in-situ embedding, thus reducing the need for expert design knowledge. A three-stage process is proposed to 1) find the optimum orientation based on cavity volume and cross-section area, 2) create the necessary cavity geometry to successfully insert the component, and 3) perform a Boolean operation to create the digital design for any requisite shape converter. Performance of the tool is demonstrated with four test cases with varying levels of geometric complexity. These test cases show that the proposed process successfully handles arbitrary embedded geometries, though several limitations are noted for future work.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122030904","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}
Alexandra Blösch-Paidosh, S. Ahmed-Kristensen, K. Shea
Additive manufacturing (AM) affords those who wield it correctly the benefits of shape, material, hierarchical, and functional complexity. However, many engineers and designers lack the training and experience necessary to take full advantage of these benefits. They require training, tools, and methods to assist them in gaining the enhanced design freedom made possible by additive manufacturing. This work, which is an extension of the authors’ previous work, explores if design heuristics for AM, presented in a card-based format, are an effective mechanism for helping designers achieve the design freedoms enabled by AM. The effectiveness of these design heuristic cards is demonstrated in an experiment with 27 product design students, by showing that there is an increase in the number of unique capabilities of AM being utilized, an increase in the AM novelty, and an increase in the AM flexibility of the generated concepts, when given access to the cards. Additionally, similar to the previous work, an increase in the number of interpreted heuristics and AM modifications present in the participants’ designs when they are provided with the heuristic cards is shown. Comparisons are also made between 8-heuristic and 29-heuristic experiments, but no conclusive statements regarding these comparisons can be drawn. Further user studies are planned to confirm the efficacy of this format at enhancing the design freedoms achieved in group and team design scenarios.
{"title":"Evaluating the Potential of Design for Additive Manufacturing Heuristic Cards to Stimulate Novel Product Redesigns","authors":"Alexandra Blösch-Paidosh, S. Ahmed-Kristensen, K. Shea","doi":"10.1115/detc2019-97865","DOIUrl":"https://doi.org/10.1115/detc2019-97865","url":null,"abstract":"\u0000 Additive manufacturing (AM) affords those who wield it correctly the benefits of shape, material, hierarchical, and functional complexity. However, many engineers and designers lack the training and experience necessary to take full advantage of these benefits. They require training, tools, and methods to assist them in gaining the enhanced design freedom made possible by additive manufacturing. This work, which is an extension of the authors’ previous work, explores if design heuristics for AM, presented in a card-based format, are an effective mechanism for helping designers achieve the design freedoms enabled by AM. The effectiveness of these design heuristic cards is demonstrated in an experiment with 27 product design students, by showing that there is an increase in the number of unique capabilities of AM being utilized, an increase in the AM novelty, and an increase in the AM flexibility of the generated concepts, when given access to the cards. Additionally, similar to the previous work, an increase in the number of interpreted heuristics and AM modifications present in the participants’ designs when they are provided with the heuristic cards is shown. Comparisons are also made between 8-heuristic and 29-heuristic experiments, but no conclusive statements regarding these comparisons can be drawn. Further user studies are planned to confirm the efficacy of this format at enhancing the design freedoms achieved in group and team design scenarios.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130432202","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 purpose of this study is to optimize production planning decisions in additive manufacturing for mass customization (AMMC) systems in which customer demands are highly variable. The main research question is to find the optimal quantity of products for scheduling, the economic scheduling quantity (ESQ). If the scheduling quantity is too large, the time to collect customer orders increases and a penalty cost occurs due to the delay in responding to consumer demands. On the other hand, if the scheduling quantity is too small, the number of parts per jobs decreases and parts are not efficiently packed within a workspace and consequently the build process cost increases. An experiment is provided for the case of stereolithography (SLA) and 2D packing to demonstrate how the build time per part increases as the scheduling quantity decreases. In addition, a mathematical framework based on ESQ is provided to evaluate the production capacity in satisfying the market demand.
{"title":"An Optimal Quantity of Scheduling Model for Mass Customization-Based Additive Manufacturing","authors":"Yosep Oh, S. Behdad","doi":"10.1115/detc2019-97913","DOIUrl":"https://doi.org/10.1115/detc2019-97913","url":null,"abstract":"\u0000 The purpose of this study is to optimize production planning decisions in additive manufacturing for mass customization (AMMC) systems in which customer demands are highly variable. The main research question is to find the optimal quantity of products for scheduling, the economic scheduling quantity (ESQ). If the scheduling quantity is too large, the time to collect customer orders increases and a penalty cost occurs due to the delay in responding to consumer demands. On the other hand, if the scheduling quantity is too small, the number of parts per jobs decreases and parts are not efficiently packed within a workspace and consequently the build process cost increases. An experiment is provided for the case of stereolithography (SLA) and 2D packing to demonstrate how the build time per part increases as the scheduling quantity decreases. In addition, a mathematical framework based on ESQ is provided to evaluate the production capacity in satisfying the market demand.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127336979","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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