Emulation plays an indispensable role in engineering design. However, the majority of emulation methods are formulated for interpolation purposes and their performance significantly deteriorates in extrapolation. In this paper, we develop a method for extrapolation by integrating Gaussian processes (GPs) and evolutionary programming (EP). Our underlying assumption is that there is a set of free-form parametric bases that can model the data source reasonably well. Consequently, if we can find these bases via some training data over a region, we can do predictions outside of that region. To systematically and efficiently find these bases, we start by learning a GP without any parametric mean function. Then, a rich dataset is generated by this GP and subsequently used in EP to find some parametric bases. Afterwards, we retrain the GP while using the bases found by EP. This retraining essentially allows to validate and/or correct the discovered bases via maximum likelihood estimation. By iterating between GP and EP we robustly and efficiently find the underlying bases that can be used for extrapolation. We validate our approach with a host of analytical problems in the absence or presence of noise. We also study an engineering example on finding the constitutive law of a composite microstructure.
{"title":"Extrapolation With Gaussian Random Processes and Evolutionary Programming","authors":"R. Planas, Nicholas Oune, R. Bostanabad","doi":"10.1115/detc2020-22381","DOIUrl":"https://doi.org/10.1115/detc2020-22381","url":null,"abstract":"\u0000 Emulation plays an indispensable role in engineering design. However, the majority of emulation methods are formulated for interpolation purposes and their performance significantly deteriorates in extrapolation. In this paper, we develop a method for extrapolation by integrating Gaussian processes (GPs) and evolutionary programming (EP). Our underlying assumption is that there is a set of free-form parametric bases that can model the data source reasonably well. Consequently, if we can find these bases via some training data over a region, we can do predictions outside of that region. To systematically and efficiently find these bases, we start by learning a GP without any parametric mean function. Then, a rich dataset is generated by this GP and subsequently used in EP to find some parametric bases. Afterwards, we retrain the GP while using the bases found by EP. This retraining essentially allows to validate and/or correct the discovered bases via maximum likelihood estimation. By iterating between GP and EP we robustly and efficiently find the underlying bases that can be used for extrapolation. We validate our approach with a host of analytical problems in the absence or presence of noise. We also study an engineering example on finding the constitutive law of a composite microstructure.","PeriodicalId":415040,"journal":{"name":"Volume 11A: 46th Design Automation Conference (DAC)","volume":"58 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114059068","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 study and application of computational design is gaining importance in biomedical engineering as medical devices are becoming more complex, especially with the emergence of 3D printed scaffold structures. Scaffolds are medical devices that act as temporary mechanical support and facilitate biological interactions to regenerate damaged tissues. Past computational design studies have investigated the influence of geometric design in lattice structured scaffolds to investigate mechanical and biological behavior. However, these studies often focus on symmetric cubic structures leaving an opportunity for investigating a larger portion of the design space to find favorable scaffold configurations beyond these constraints. Here, tissue growth behavior is investigated for tetragonal Bravais lattice structured scaffolds by implementing a computational approach that combines a voxel-based design generation method, curvature-based tissue growth modeling, and a design mapping technique for selecting scaffold designs. Results show that tetragonal unit cells achieve higher specific tissue growth than cubic unit cells when investigated for a constant beam width, thus demonstrating the merits in investigating a larger portion of the design space. It is seen that cubic structures achieve around 50% specific growth, while tetragonal structures achieve more than 60% specific growth for the design space investigated. These findings demonstrate the need for continued adaption and use of computational design methodologies for biomedical applications, where the discovery of favorable solutions may significantly improve medical outcomes.
{"title":"Computational Design Generation and Evaluation of Beam-Based Tetragonal Bravais Lattice Structures for Tissue Engineering","authors":"Amit M. E. Arefin, P. Egan","doi":"10.1115/detc2020-22450","DOIUrl":"https://doi.org/10.1115/detc2020-22450","url":null,"abstract":"\u0000 The study and application of computational design is gaining importance in biomedical engineering as medical devices are becoming more complex, especially with the emergence of 3D printed scaffold structures. Scaffolds are medical devices that act as temporary mechanical support and facilitate biological interactions to regenerate damaged tissues. Past computational design studies have investigated the influence of geometric design in lattice structured scaffolds to investigate mechanical and biological behavior. However, these studies often focus on symmetric cubic structures leaving an opportunity for investigating a larger portion of the design space to find favorable scaffold configurations beyond these constraints. Here, tissue growth behavior is investigated for tetragonal Bravais lattice structured scaffolds by implementing a computational approach that combines a voxel-based design generation method, curvature-based tissue growth modeling, and a design mapping technique for selecting scaffold designs. Results show that tetragonal unit cells achieve higher specific tissue growth than cubic unit cells when investigated for a constant beam width, thus demonstrating the merits in investigating a larger portion of the design space. It is seen that cubic structures achieve around 50% specific growth, while tetragonal structures achieve more than 60% specific growth for the design space investigated. These findings demonstrate the need for continued adaption and use of computational design methodologies for biomedical applications, where the discovery of favorable solutions may significantly improve medical outcomes.","PeriodicalId":415040,"journal":{"name":"Volume 11A: 46th Design Automation Conference (DAC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126188369","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 slicing software process the 3D geometry into 2D slices and toolpaths for additive manufacturing processes. Most slicing software allows users to select from an array of infill patterns and to specify the overall infill volume fraction globally. However, the ability to locally control the volume fraction, and mechanical properties, is often limited. In this paper, we propose a novel toolpath enhancing algorithm to enable the local control on the volume fraction of various stock and custom infill patterns. In particular, the algorithm widens the infill pattern by directly modifying their toolpath with connected Fermat curves. By preserving the topology of the original toolpath, the connected Fermat curve not only produces predictable boosts in part performance but also minimized the printing time by eliminating extruder traversals without material deposition. The field that controls local volume fraction can be designed either manually or through optimization. The effectiveness of the proposed approach in toolpath generation is demonstrated through volume fraction fields designed by both approaches.
{"title":"Enhanced Toolpath Planning for Fused Filament Fabrication","authors":"Hongrui Chen, Xingchen Liu","doi":"10.1115/detc2020-22725","DOIUrl":"https://doi.org/10.1115/detc2020-22725","url":null,"abstract":"\u0000 The slicing software process the 3D geometry into 2D slices and toolpaths for additive manufacturing processes. Most slicing software allows users to select from an array of infill patterns and to specify the overall infill volume fraction globally. However, the ability to locally control the volume fraction, and mechanical properties, is often limited. In this paper, we propose a novel toolpath enhancing algorithm to enable the local control on the volume fraction of various stock and custom infill patterns. In particular, the algorithm widens the infill pattern by directly modifying their toolpath with connected Fermat curves. By preserving the topology of the original toolpath, the connected Fermat curve not only produces predictable boosts in part performance but also minimized the printing time by eliminating extruder traversals without material deposition. The field that controls local volume fraction can be designed either manually or through optimization. The effectiveness of the proposed approach in toolpath generation is demonstrated through volume fraction fields designed by both approaches.","PeriodicalId":415040,"journal":{"name":"Volume 11A: 46th Design Automation Conference (DAC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124515949","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}
Nathan K. Brown, M. Owen, J. DesJardins, Anthony P. Garland, G. Fadel
While using a prosthesis, transtibial amputees can experience pain and discomfort brought on by large pressure gradients, at the interface between the residual limb and prosthetic socket. Current prosthetic interface solutions attempt to alleviate these pressure gradients by using soft homogenous liners to reduce and distribute pressures. This research investigates an additively manufactured metamaterial inlay with adjustable mechanical response in order to reduce peak pressure gradients around the limb. The inlay uses a hyperelastic behaving metamaterial (US10244818) comprised of triangular pattern unit cells which can be 3D printed with walls of various thicknesses controlled by draft angles. The hyperelastic material properties are modeled using a third order representation based on Yeoh 3rd order coefficients. The 3rd order coefficients can be adjusted and optimized to represent a change in the unit cell wall thickness to create an inlay that can meet the unique offloading needs of an amputee. Finite element analyses evaluated the pressure gradient reduction from: 1) A common homogenous silicone liner, 2) A prosthetist’s inlay prescription that utilizes three variations of the metamaterial, and 3) A metamaterial solution with optimized Yeoh 3rd order coefficients. When compared to a traditional homogenous silicone liner for two unique limb loading scenarios, the prosthetist prescribed inlay and optimized material inlay can achieve equal or greater pressure gradient reduction capabilities. These results show the potential feasibility of implementing this metamaterial as a method of personalized medicine for transtibial amputees by creating customizable interface solution to the meet unique performance needs of an individual patient.
{"title":"Metamaterial Design for Targeted Limb-Socket Interface Pressure Offloading in Transtibial Amputees","authors":"Nathan K. Brown, M. Owen, J. DesJardins, Anthony P. Garland, G. Fadel","doi":"10.1115/detc2020-22044","DOIUrl":"https://doi.org/10.1115/detc2020-22044","url":null,"abstract":"\u0000 While using a prosthesis, transtibial amputees can experience pain and discomfort brought on by large pressure gradients, at the interface between the residual limb and prosthetic socket. Current prosthetic interface solutions attempt to alleviate these pressure gradients by using soft homogenous liners to reduce and distribute pressures. This research investigates an additively manufactured metamaterial inlay with adjustable mechanical response in order to reduce peak pressure gradients around the limb. The inlay uses a hyperelastic behaving metamaterial (US10244818) comprised of triangular pattern unit cells which can be 3D printed with walls of various thicknesses controlled by draft angles. The hyperelastic material properties are modeled using a third order representation based on Yeoh 3rd order coefficients. The 3rd order coefficients can be adjusted and optimized to represent a change in the unit cell wall thickness to create an inlay that can meet the unique offloading needs of an amputee. Finite element analyses evaluated the pressure gradient reduction from: 1) A common homogenous silicone liner, 2) A prosthetist’s inlay prescription that utilizes three variations of the metamaterial, and 3) A metamaterial solution with optimized Yeoh 3rd order coefficients. When compared to a traditional homogenous silicone liner for two unique limb loading scenarios, the prosthetist prescribed inlay and optimized material inlay can achieve equal or greater pressure gradient reduction capabilities. These results show the potential feasibility of implementing this metamaterial as a method of personalized medicine for transtibial amputees by creating customizable interface solution to the meet unique performance needs of an individual patient.","PeriodicalId":415040,"journal":{"name":"Volume 11A: 46th Design Automation Conference (DAC)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122420764","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 examine optimal-profit product design platforming problems for products sold across multiple markets. Firms have an incentive to platform to take advantage of cost reductions that are possible with increased production quantity, such as learning-by-doing. However, platforming may decrease sales compared to if the designs were customized for each market. The problem can be represented as a Nash equilibrium between multiple competing firms, each with a nonconvex mixed-integer nonlinear programing (MINLP) problem for maximizing their individual profits. We derive the Karush-Kuhn-Tucker (KKT) conditions for the problem and compare results from two algorithmic approaches: (1) an iterative MINLP approach that uses the BARON algorithm to solve each firm’s design and platforming problem and iterates until convergence to an equilibrium, and (2) an approach that solves the KKT conditions directly holding platforming decisions fixed, and compares profits for these platforming decisions to find an equilibrium. Results are presented for a case study of plug-in hybrid electric vehicles (PHEVs) where firms choose whether or not to platform the battery pack across the U.S. and China, and set the optimal battery capacity. We vary the learning rate and the difference in consumer willingness to pay for all-electric range between China and the U.S. Both algorithms agree on the same equilibrium solution in 98.4% of the cases. Results show that the optimum for each firm is to platform when learning rates are low, or the difference between optimal battery capacity in each market is relatively small.
{"title":"Global Product Design Platforming: A Comparison of Two Methods to Find Equilibrium Solutions","authors":"Sarah S. Case, Kate S. Whitefoot","doi":"10.1115/detc2020-22658","DOIUrl":"https://doi.org/10.1115/detc2020-22658","url":null,"abstract":"\u0000 We examine optimal-profit product design platforming problems for products sold across multiple markets. Firms have an incentive to platform to take advantage of cost reductions that are possible with increased production quantity, such as learning-by-doing. However, platforming may decrease sales compared to if the designs were customized for each market. The problem can be represented as a Nash equilibrium between multiple competing firms, each with a nonconvex mixed-integer nonlinear programing (MINLP) problem for maximizing their individual profits. We derive the Karush-Kuhn-Tucker (KKT) conditions for the problem and compare results from two algorithmic approaches: (1) an iterative MINLP approach that uses the BARON algorithm to solve each firm’s design and platforming problem and iterates until convergence to an equilibrium, and (2) an approach that solves the KKT conditions directly holding platforming decisions fixed, and compares profits for these platforming decisions to find an equilibrium. Results are presented for a case study of plug-in hybrid electric vehicles (PHEVs) where firms choose whether or not to platform the battery pack across the U.S. and China, and set the optimal battery capacity. We vary the learning rate and the difference in consumer willingness to pay for all-electric range between China and the U.S. Both algorithms agree on the same equilibrium solution in 98.4% of the cases. Results show that the optimum for each firm is to platform when learning rates are low, or the difference between optimal battery capacity in each market is relatively small.","PeriodicalId":415040,"journal":{"name":"Volume 11A: 46th Design Automation Conference (DAC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122537905","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}
To appropriately leverage the benefits of Additive Manufacturing (AM), it would be advantageous if a printing could be guaranteed before allocating the necessary resources. Further, when considering AM for an inventory of existing components traditionally fabricated through traditional means, such a guarantee could result in significant technical and economic advantages. To realize such advantages, this paper presents a platform that allows for a successful and efficient transition of part-inventories to AM. This is accomplished using a novel design recommender system supported by machine learning, capable of making suggestions towards effective design modifications. This system uses an automatic AM-feasibility analysis of existing parts and a clustering of the parts based on similarities in their AM-feasibilities to develop a set of recommendations for those part clusters whose current designs are deemed as infeasible and/or inefficient for AM. The design modifications leverage a re-design algorithm to address not only problematic geometric issues but also potential infeasibilities associated with resource consumption. The utility of the presented modification algorithm is demonstrated using a number of case studies.
{"title":"A Machine Learning-Based Design Recommender System for Additive Manufacturing","authors":"S. E. Ghiasian, K. Lewis","doi":"10.1115/detc2020-22182","DOIUrl":"https://doi.org/10.1115/detc2020-22182","url":null,"abstract":"\u0000 To appropriately leverage the benefits of Additive Manufacturing (AM), it would be advantageous if a printing could be guaranteed before allocating the necessary resources. Further, when considering AM for an inventory of existing components traditionally fabricated through traditional means, such a guarantee could result in significant technical and economic advantages. To realize such advantages, this paper presents a platform that allows for a successful and efficient transition of part-inventories to AM. This is accomplished using a novel design recommender system supported by machine learning, capable of making suggestions towards effective design modifications. This system uses an automatic AM-feasibility analysis of existing parts and a clustering of the parts based on similarities in their AM-feasibilities to develop a set of recommendations for those part clusters whose current designs are deemed as infeasible and/or inefficient for AM. The design modifications leverage a re-design algorithm to address not only problematic geometric issues but also potential infeasibilities associated with resource consumption. The utility of the presented modification algorithm is demonstrated using a number of case studies.","PeriodicalId":415040,"journal":{"name":"Volume 11A: 46th Design Automation Conference (DAC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123837163","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}
Importance-performance analysis (IPA) is a technique used to understand customer satisfaction and improve the quality of product attributes. This study proposes an explainable deep-neural-network-based method to carry out IPA of product attributes from online reviews for product design. Previous works used shallow neural network (SNN)-based methods to estimate importance values, but it was unclear whether the SNN is an optimal neural network architecture. The estimated importance has high variability by a single neural network from a training set that is randomly selected. However, the proposed method provides importance values with a lower variance by improving the importance estimation of each product attribute in the IPA. The proposed method first identifies the product attributes and estimates their performance. Then, it infers the importance values by combining explanations of the input features from multiple optimal neural networks. A case study on smartphones is used herein to demonstrate the proposed method.
{"title":"Importance-Performance Analysis of Product Attributes Using Explainable Deep Neural Network From Online Reviews","authors":"Junegak Joung, Harrison M. Kim","doi":"10.1115/detc2020-22382","DOIUrl":"https://doi.org/10.1115/detc2020-22382","url":null,"abstract":"\u0000 Importance-performance analysis (IPA) is a technique used to understand customer satisfaction and improve the quality of product attributes. This study proposes an explainable deep-neural-network-based method to carry out IPA of product attributes from online reviews for product design. Previous works used shallow neural network (SNN)-based methods to estimate importance values, but it was unclear whether the SNN is an optimal neural network architecture. The estimated importance has high variability by a single neural network from a training set that is randomly selected. However, the proposed method provides importance values with a lower variance by improving the importance estimation of each product attribute in the IPA. The proposed method first identifies the product attributes and estimates their performance. Then, it infers the importance values by combining explanations of the input features from multiple optimal neural networks. A case study on smartphones is used herein to demonstrate the proposed method.","PeriodicalId":415040,"journal":{"name":"Volume 11A: 46th Design Automation Conference (DAC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127901888","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}
Demand Response (DR) is the implementation of a specific strategy or set of strategies, with the goal of altering consumer energy demand, such that some system level objectives are improved. These strategies typically include dynamic pricing, direct load control, policy implementation, or other financial incentives. DR will become a crucial tool for managing growing global energy demand in conjunction with higher penetration rates of intermittent renewable energy resources. Effective implementation of a DR strategy requires a realistic understanding of how consumers will respond to that strategy and how they will be affected by it. Here, a product-based decision model for residential consumers, that links consumer decisions directly to product-use, is revisited and adapted from a continuous time formulation to discrete time. The relationship between financial incentives, consumer preferences, and demand flexibility at the population level is then quantified. The model is used for exploring the tradeoffs between typical objectives for a dynamic pricing residential DR program and evaluating the characteristics of well-performing pricing solutions.
{"title":"A Consumer Dissatisfaction Model Linking Dynamic Pricing With Shifted Product-Use in Residential Electricity Markets","authors":"Samuel Dunbar, S. Ferguson","doi":"10.1115/detc2020-22499","DOIUrl":"https://doi.org/10.1115/detc2020-22499","url":null,"abstract":"\u0000 Demand Response (DR) is the implementation of a specific strategy or set of strategies, with the goal of altering consumer energy demand, such that some system level objectives are improved. These strategies typically include dynamic pricing, direct load control, policy implementation, or other financial incentives. DR will become a crucial tool for managing growing global energy demand in conjunction with higher penetration rates of intermittent renewable energy resources. Effective implementation of a DR strategy requires a realistic understanding of how consumers will respond to that strategy and how they will be affected by it. Here, a product-based decision model for residential consumers, that links consumer decisions directly to product-use, is revisited and adapted from a continuous time formulation to discrete time. The relationship between financial incentives, consumer preferences, and demand flexibility at the population level is then quantified. The model is used for exploring the tradeoffs between typical objectives for a dynamic pricing residential DR program and evaluating the characteristics of well-performing pricing solutions.","PeriodicalId":415040,"journal":{"name":"Volume 11A: 46th Design Automation Conference (DAC)","volume":"13 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125760135","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}
Endoscopic stents are being used by surgeons in off-label uses to manage leaks and perforations in the gastrointestinal tract. Commercially available stents are primarily designed to open strictures in the esophagus through tissue compression. The stents incorporate a woven NiTi wire to produce a stiff and linear tubular shape that conforms to the esophagus. In off-label uses, where the stents are placed in non-esophageal locations the stents must bend, the stents show a high propensity to migrate from their initial location causing unwanted complications. In this paper, a new stent design incorporating functionally graded NiTi is presented and explored. First, a functionally graded NiTi stent design is proposed. Next, a mechanical model using finite element analysis is developed to predict the bending moment and stiffness of the functionally graded stent designs. Finally, the mechanical model is coupled with a genetic algorithm in MATLAB to identify optimal designs. For a 90° bending angle, the best design parameters of the newly proposed flexible stents are found for three different stent design families. The results of the functionally graded stents show how tailoring the material properties locally in a structure can lead to highly compliant behavior. The tailoring of the geometric and material design developed may be applied to design of highly flexible and optimized medical devices.
{"title":"Design and Optimization of Functionally Graded Superelastic NiTi Stents","authors":"Jivtesh B. Khurana, M. Frecker, E. Pauli","doi":"10.1115/detc2020-22706","DOIUrl":"https://doi.org/10.1115/detc2020-22706","url":null,"abstract":"\u0000 Endoscopic stents are being used by surgeons in off-label uses to manage leaks and perforations in the gastrointestinal tract. Commercially available stents are primarily designed to open strictures in the esophagus through tissue compression. The stents incorporate a woven NiTi wire to produce a stiff and linear tubular shape that conforms to the esophagus. In off-label uses, where the stents are placed in non-esophageal locations the stents must bend, the stents show a high propensity to migrate from their initial location causing unwanted complications. In this paper, a new stent design incorporating functionally graded NiTi is presented and explored. First, a functionally graded NiTi stent design is proposed. Next, a mechanical model using finite element analysis is developed to predict the bending moment and stiffness of the functionally graded stent designs. Finally, the mechanical model is coupled with a genetic algorithm in MATLAB to identify optimal designs. For a 90° bending angle, the best design parameters of the newly proposed flexible stents are found for three different stent design families. The results of the functionally graded stents show how tailoring the material properties locally in a structure can lead to highly compliant behavior. The tailoring of the geometric and material design developed may be applied to design of highly flexible and optimized medical devices.","PeriodicalId":415040,"journal":{"name":"Volume 11A: 46th Design Automation Conference (DAC)","volume":"275 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115271132","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}
Non-stochastic lattice structures are patterned after the unit cell topology and are of interest to the research and design communities for improving stiffness to weight ratios and/or metamaterial design. While additive manufacturing (AM) increases design freedom, it remains difficult to design or select an appropriate unit cell topology. In this work, a ground structure topology optimization approach is developed for unit cell design. Using a multi-objective evolutionary algorithm, this framework incorporates a library of different objectives, constraints, and penalties. The Additive Lattice Topology Optimization (ALTO) approach generates novel lattice structures for AM from the selected design objectives. A key purpose of this framework is incorporating AM process considerations into the optimization through objectives, constraints, and penalty functions for improved manufacturability. Two case studies presented in this work demonstrate ALTO’s ability to generate novel lattice structures with specific functionality while accounting for AM process constraints for laser powder bed fusion. Case Study 1 is an example of generating a lattice structure for heat sink applications. Case Study 2 demonstrates creation of three novel lattices with different stiffness properties, each with the same volume fraction. Using ground structure topology optimization and incorporating AM process considerations, ALTO is a unique approach for improved lattice structure design.
{"title":"3D Additive Lattice Topology Optimization: A Unit Cell Design Approach","authors":"Bradley Hanks, M. Frecker","doi":"10.1115/detc2020-22386","DOIUrl":"https://doi.org/10.1115/detc2020-22386","url":null,"abstract":"\u0000 Non-stochastic lattice structures are patterned after the unit cell topology and are of interest to the research and design communities for improving stiffness to weight ratios and/or metamaterial design. While additive manufacturing (AM) increases design freedom, it remains difficult to design or select an appropriate unit cell topology. In this work, a ground structure topology optimization approach is developed for unit cell design. Using a multi-objective evolutionary algorithm, this framework incorporates a library of different objectives, constraints, and penalties. The Additive Lattice Topology Optimization (ALTO) approach generates novel lattice structures for AM from the selected design objectives. A key purpose of this framework is incorporating AM process considerations into the optimization through objectives, constraints, and penalty functions for improved manufacturability. Two case studies presented in this work demonstrate ALTO’s ability to generate novel lattice structures with specific functionality while accounting for AM process constraints for laser powder bed fusion. Case Study 1 is an example of generating a lattice structure for heat sink applications. Case Study 2 demonstrates creation of three novel lattices with different stiffness properties, each with the same volume fraction. Using ground structure topology optimization and incorporating AM process considerations, ALTO is a unique approach for improved lattice structure design.","PeriodicalId":415040,"journal":{"name":"Volume 11A: 46th Design Automation Conference (DAC)","volume":"23 23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128440731","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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