Jackie Ayoub, Feng Zhou, Qianli Xu, Jessie X. Yang
It is necessary to analyze customer needs of a product ecosystem in order to increase customer satisfaction and user experience, which will, in turn, enhance its business strategy and profits. However, it is often time-consuming and challenging to identify and analyze customer needs of product ecosystems using traditional methods due to numerous products and services as well as their interdependence within the product ecosystem. In this paper, we analyzed customer needs of a product ecosystem by capitalizing on online product reviews of multiple products and services of the Amazon product ecosystem with machine learning techniques. First, we filtered the noise involved in the reviews using a fastText method to categorize the reviews into informative and uninformative regarding customer needs. Second, we extracted various customer needs related topics using a latent Dirichlet allocation technique. Third, we conducted sentiment analysis using a valence aware dictionary and sentiment reasoner method, which not only predicted the sentiment of the reviews, but also its intensity. Based on the first three steps, we classified customer needs using an analytical Kano model dynamically. The case study of Amazon product ecosystem showed the potential of the proposed method.
{"title":"Analyzing Customer Needs of Product Ecosystems Using Online Product Reviews","authors":"Jackie Ayoub, Feng Zhou, Qianli Xu, Jessie X. Yang","doi":"10.1115/detc2019-97642","DOIUrl":"https://doi.org/10.1115/detc2019-97642","url":null,"abstract":"\u0000 It is necessary to analyze customer needs of a product ecosystem in order to increase customer satisfaction and user experience, which will, in turn, enhance its business strategy and profits. However, it is often time-consuming and challenging to identify and analyze customer needs of product ecosystems using traditional methods due to numerous products and services as well as their interdependence within the product ecosystem. In this paper, we analyzed customer needs of a product ecosystem by capitalizing on online product reviews of multiple products and services of the Amazon product ecosystem with machine learning techniques. First, we filtered the noise involved in the reviews using a fastText method to categorize the reviews into informative and uninformative regarding customer needs. Second, we extracted various customer needs related topics using a latent Dirichlet allocation technique. Third, we conducted sentiment analysis using a valence aware dictionary and sentiment reasoner method, which not only predicted the sentiment of the reviews, but also its intensity. Based on the first three steps, we classified customer needs using an analytical Kano model dynamically. The case study of Amazon product ecosystem showed the potential of the proposed method.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"36 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":"125195743","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 is a developing technology that enhances design freedom at multiple length scales, from the macroscale, or bulk geometry, to the mesoscale, such as lattice structures, and even down to tailored microstructure. At the mesoscale, lattice structures are often used to replace solid sections of material and are typically patterned after generic topologies. The mechanical properties and performance of generic unit cell topologies are being explored by many researchers but there is a lack of development of custom lattice structures, optimized for their application, with considerations for design for additive manufacturing. This work proposes a ground structure topology optimization method for systematic unit cell optimization. Two case studies are presented to demonstrate the approach. Case Study 1 results in a range of unit cell designs that transition from maximum thermal conductivity to minimization of compliance. Case Study 2 shows the opportunity for constitutive matching of the bulk lattice properties to a target constitutive matrix. Future work will include validation of unit cell modeling, testing of optimized solutions, and further development of the approach through expansion to 3D and refinement of objective, penalty, and constraint functions.
{"title":"Lattice Structure Design for Additive Manufacturing: Unit Cell Topology Optimization","authors":"Bradley Hanks, M. Frecker","doi":"10.1115/detc2019-97863","DOIUrl":"https://doi.org/10.1115/detc2019-97863","url":null,"abstract":"\u0000 Additive manufacturing is a developing technology that enhances design freedom at multiple length scales, from the macroscale, or bulk geometry, to the mesoscale, such as lattice structures, and even down to tailored microstructure. At the mesoscale, lattice structures are often used to replace solid sections of material and are typically patterned after generic topologies. The mechanical properties and performance of generic unit cell topologies are being explored by many researchers but there is a lack of development of custom lattice structures, optimized for their application, with considerations for design for additive manufacturing. This work proposes a ground structure topology optimization method for systematic unit cell optimization. Two case studies are presented to demonstrate the approach. Case Study 1 results in a range of unit cell designs that transition from maximum thermal conductivity to minimization of compliance. Case Study 2 shows the opportunity for constitutive matching of the bulk lattice properties to a target constitutive matrix. Future work will include validation of unit cell modeling, testing of optimized solutions, and further development of the approach through expansion to 3D and refinement of objective, penalty, and constraint functions.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"29 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":"127259695","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}
As globalization continues, manufacturing enterprises need to do mass customization with a short lead-time, to satisfy evolving market demands in different regions. One challenge of mass customization is to fulfill orders swiftly at an acceptable cost, meanwhile maintaining the service quality. To do this, the customer order decoupling point – CODP, where the value-adding activities take place, should be designed and adapted to the changing market demands. In this paper, we propose a Formulation-Exploration method to make decisions on CODP positioning and improve the supply chain to support mass customization. A test problem of auto parts manufacturing is used to establish the efficacy of our method. The Formulation-Exploration method can be used to design supply chains to manage mass customization of products, especially when information is incomplete and inaccurate, goals conflict and multiple types of uncertainty add complexity. In this paper, we focus on the method rather than the results per se.
{"title":"Designing the Customer Order Decoupling Point to Facilitate Mass Customization","authors":"Lin Guo, Suhao Chen, J. Allen, F. Mistree","doi":"10.1115/detc2019-97379","DOIUrl":"https://doi.org/10.1115/detc2019-97379","url":null,"abstract":"\u0000 As globalization continues, manufacturing enterprises need to do mass customization with a short lead-time, to satisfy evolving market demands in different regions. One challenge of mass customization is to fulfill orders swiftly at an acceptable cost, meanwhile maintaining the service quality. To do this, the customer order decoupling point – CODP, where the value-adding activities take place, should be designed and adapted to the changing market demands.\u0000 In this paper, we propose a Formulation-Exploration method to make decisions on CODP positioning and improve the supply chain to support mass customization. A test problem of auto parts manufacturing is used to establish the efficacy of our method. The Formulation-Exploration method can be used to design supply chains to manage mass customization of products, especially when information is incomplete and inaccurate, goals conflict and multiple types of uncertainty add complexity. In this paper, we focus on the method rather than the results per se.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"135 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":"127583294","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 substantial role of additive manufacturing (AM) in fabricating unique geometries is undeniable in the domain of design and manufacturing. However, the successful implementation of AM technologies requires a consistency between the geometric specifications of a component and AM manufacturability capabilities and constraints. Otherwise, AM could result in failed prints and a wasteful use of resources. The goal of this research is to provide geometrically feasible designs for AM processes by rectifying the potentially infeasible geometries. To this end, a novel design modification system is presented that addresses the problematic areas of an AM-infeasible component using appropriate redesign solutions. This system also includes a geometric assessment algorithm which identifies the potential problematic part features using a comprehensive evaluation. Based on the obtained manufacturability feedback, the detected problematic features are then modified through a holistic design modification system. The functionality of the presented system is illustrated using a case study, and the effectiveness of the implemented modification approaches is also demonstrated through an experiment.
{"title":"A Design Modification System for Additive Manufacturing: Towards Feasible Geometry Development","authors":"S. E. Ghiasian, Prakhar Jaiswal, R. Rai, K. Lewis","doi":"10.1115/detc2019-97840","DOIUrl":"https://doi.org/10.1115/detc2019-97840","url":null,"abstract":"\u0000 The substantial role of additive manufacturing (AM) in fabricating unique geometries is undeniable in the domain of design and manufacturing. However, the successful implementation of AM technologies requires a consistency between the geometric specifications of a component and AM manufacturability capabilities and constraints. Otherwise, AM could result in failed prints and a wasteful use of resources. The goal of this research is to provide geometrically feasible designs for AM processes by rectifying the potentially infeasible geometries. To this end, a novel design modification system is presented that addresses the problematic areas of an AM-infeasible component using appropriate redesign solutions. This system also includes a geometric assessment algorithm which identifies the potential problematic part features using a comprehensive evaluation. Based on the obtained manufacturability feedback, the detected problematic features are then modified through a holistic design modification system. The functionality of the presented system is illustrated using a case study, and the effectiveness of the implemented modification approaches is also demonstrated through an experiment.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"17 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":"116991719","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 presence of various uncertainty sources in metal-based additive manufacturing (AM) process prevents producing AM products with consistently high quality. Using electron beam melting (EBM) of Ti-6A1-4V as an example, this paper presents a data-driven framework for process parameters optimization using physics-informed computer simulation models. The goal is to identify a robust manufacturing condition that allows us to constantly obtain equiaxed materials microstructures under uncertainty. To overcome the computational challenge in the robust design optimization under uncertainty, a two-level data-driven surrogate model is constructed based on the simulation data of a validated high-fidelity multi-physics AM simulation model. The robust design result, indicating a combination of low preheating temperature, low beam power and intermediate scanning speed, was acquired enabling the repetitive production of equiaxed-structure products as demonstrated by physics-based simulations. Global sensitivity analysis at the optimal design point indicates that among the studied six noise factors, specific heat capacity and grain growth activation energy have largest impact on the microstructure variation.
{"title":"Simulation-Based Process Optimization of Metallic Additive Manufacturing Under Uncertainty","authors":"Zhuo Wang, Pengwei Liu, Zhen Hu, Lei Chen","doi":"10.1115/detc2019-97492","DOIUrl":"https://doi.org/10.1115/detc2019-97492","url":null,"abstract":"\u0000 The presence of various uncertainty sources in metal-based additive manufacturing (AM) process prevents producing AM products with consistently high quality. Using electron beam melting (EBM) of Ti-6A1-4V as an example, this paper presents a data-driven framework for process parameters optimization using physics-informed computer simulation models. The goal is to identify a robust manufacturing condition that allows us to constantly obtain equiaxed materials microstructures under uncertainty. To overcome the computational challenge in the robust design optimization under uncertainty, a two-level data-driven surrogate model is constructed based on the simulation data of a validated high-fidelity multi-physics AM simulation model. The robust design result, indicating a combination of low preheating temperature, low beam power and intermediate scanning speed, was acquired enabling the repetitive production of equiaxed-structure products as demonstrated by physics-based simulations. Global sensitivity analysis at the optimal design point indicates that among the studied six noise factors, specific heat capacity and grain growth activation energy have largest impact on the microstructure variation.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"97 10 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":"127996129","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}
Lattice structures are promising for a wide range of applications. The development of additive manufacturing (AM) technology has made it possible to manufacture complex structures. However, designing the optimal lattices of complex solid models efficiently and automatically remains a challenge. Thus, we propose a novel stress-field-guided lattice design method to improve the mechanical properties of a lattice structure. Stress field is used to make the boundary struts of each cell of a lattice structure aligning to the principal stress direction while remaining conformal. Hierarchical cell templates are designed to reduce the computational burden of the cell optimization of a lattice structure. The proposed method is verified experimentally, and the experimental results prove the efficiency and validity of the proposed method.
{"title":"Stress Field Guided Lattice Structure Design Based on Hexahedral Mesh","authors":"Lin Liu, Yizhou Liao, Shuming Gao","doi":"10.1115/detc2019-97248","DOIUrl":"https://doi.org/10.1115/detc2019-97248","url":null,"abstract":"\u0000 Lattice structures are promising for a wide range of applications. The development of additive manufacturing (AM) technology has made it possible to manufacture complex structures. However, designing the optimal lattices of complex solid models efficiently and automatically remains a challenge. Thus, we propose a novel stress-field-guided lattice design method to improve the mechanical properties of a lattice structure. Stress field is used to make the boundary struts of each cell of a lattice structure aligning to the principal stress direction while remaining conformal. Hierarchical cell templates are designed to reduce the computational burden of the cell optimization of a lattice structure. The proposed method is verified experimentally, and the experimental results prove the efficiency and validity of the proposed method.","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":"130872536","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}
Hannah S. Walsh, Mohammad Hejase, Daniel E. Hulse, G. Brat, I. Tumer
There is a major push in safety-critical systems to consider system risk early in the design process in order to avoid costly redesign later on. However, existing techniques, which may be labor-intensive and be subject to many sources of uncertainty, rely on failure mode and failure rate data, which can only be estimated in the early design phase. This paper proposes a network-based technique for assessing the consequential importance of a particular component to enable designers to consider hazards in the design of the system architecture without the use of estimated failure rates. Structural consequence analysis represents connectivity between components with a network and provides an explicit representation of risk prevention and mitigation techniques, such as redundancy. The network is augmented with a measure of the consequence of the failure of the “end” components, or sinks, which can be backpropagated through the network to compute the consequence associated with the failure of all components. Based on this consequence, designers can consider mitigation strategies, such as redundancy or increased component reliability. The approach is demonstrated in the design of an electric system to control an aileron of an unmanned aircraft system (UAS). It is found that structural consequence analysis can identify potentially important components without failure rate data, allowing designers to proactively design for risk earlier in the design process.
{"title":"Structural Consequence Analysis: Towards the Quantification of Component Consequential Importance in System Architecture Design","authors":"Hannah S. Walsh, Mohammad Hejase, Daniel E. Hulse, G. Brat, I. Tumer","doi":"10.1115/detc2019-98393","DOIUrl":"https://doi.org/10.1115/detc2019-98393","url":null,"abstract":"\u0000 There is a major push in safety-critical systems to consider system risk early in the design process in order to avoid costly redesign later on. However, existing techniques, which may be labor-intensive and be subject to many sources of uncertainty, rely on failure mode and failure rate data, which can only be estimated in the early design phase. This paper proposes a network-based technique for assessing the consequential importance of a particular component to enable designers to consider hazards in the design of the system architecture without the use of estimated failure rates. Structural consequence analysis represents connectivity between components with a network and provides an explicit representation of risk prevention and mitigation techniques, such as redundancy. The network is augmented with a measure of the consequence of the failure of the “end” components, or sinks, which can be backpropagated through the network to compute the consequence associated with the failure of all components. Based on this consequence, designers can consider mitigation strategies, such as redundancy or increased component reliability. The approach is demonstrated in the design of an electric system to control an aileron of an unmanned aircraft system (UAS). It is found that structural consequence analysis can identify potentially important components without failure rate data, allowing designers to proactively design for risk earlier in the design process.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"9 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":"116767902","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}
Complex engineering design tasks require teams of engineers with different skills and unique knowledge sets to work together to develop a solution. In these contexts, team communication is critical to successful design outcomes. Previous research has identified effective management of communication frequency as an important dimension of team communication leading to improved design outcomes. Organization research literature has demonstrated a curvilinear relationship in which both frequent and infrequent communication may hamper organizational performance. In contrast, recent work in engineering design research has found an inverse relationship between frequency and technical system performance for simple design tasks. This paper extends this work quantifying the impact of communication frequency on technical system performance by examining multi-disciplinary problems. Results from a multi-agent simulation on a six discipline parameter design task for minimizing the weight of a geostationary satellite are presented. Simulation results suggest that the form of relationship between frequency and performance changes significantly depending on the communication pattern. The evidence suggests that for the same design task a planned periodic communication pattern results in a curvilinear relationship, whereas for a stochastic communication pattern a less pronounced monotonic inverse relationship is found.
{"title":"Investigating Optimal Communication Frequency in Multi-Disciplinary Engineering Teams Using Multi-Agent Simulation","authors":"Mojtaba Arezoomand, J. Austin-Breneman","doi":"10.1115/detc2019-97301","DOIUrl":"https://doi.org/10.1115/detc2019-97301","url":null,"abstract":"\u0000 Complex engineering design tasks require teams of engineers with different skills and unique knowledge sets to work together to develop a solution. In these contexts, team communication is critical to successful design outcomes. Previous research has identified effective management of communication frequency as an important dimension of team communication leading to improved design outcomes. Organization research literature has demonstrated a curvilinear relationship in which both frequent and infrequent communication may hamper organizational performance. In contrast, recent work in engineering design research has found an inverse relationship between frequency and technical system performance for simple design tasks. This paper extends this work quantifying the impact of communication frequency on technical system performance by examining multi-disciplinary problems. Results from a multi-agent simulation on a six discipline parameter design task for minimizing the weight of a geostationary satellite are presented. Simulation results suggest that the form of relationship between frequency and performance changes significantly depending on the communication pattern. The evidence suggests that for the same design task a planned periodic communication pattern results in a curvilinear relationship, whereas for a stochastic communication pattern a less pronounced monotonic inverse relationship is found.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"156 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":"131329621","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}
Decision Support Problems (DSPs) are used to model design decisions involving multiple trade-offs. In practice, such design decisions are also coupled, that is, these decisions must be modelled by identifying and addressing the influence they exert on one another. Hence, we need to classify coupled decision problems and to introduce methods for managing uncertainty for such problems. Classification of coupled decision problems allows for the development and execution of decision templates to effect design and to archive design-related knowledge on a computer. Incorporating robustness metrics allows for the exploration of robust design solutions for coupled decision problems by managing uncertainty. In this paper, we present a classification scheme for coupled decisions using DSPs, called the Decision Scenario Matrix and we illustrate its utility by solving a coupled problem using DSPs. The design of a beam to be used as a fender is used to illustrate the efficacy of the formulation of coupled problems. In the first example, we determine a robust design, that is, determine the dimensions of the fender and simultaneously design the material recognizing that the computational models are incomplete and inaccurate. In the second example, we determine robust design solutions when design decisions are coupled, that is, determine the dimensions of the fender and select the material concurrently. Our focus, in this paper, is on illustrating the efficacy of the method rather than on the results.
{"title":"Classification and Execution of Coupled Decision Problems in Engineering Design for Exploration of Robust Design Solutions","authors":"Gehendra Sharma, J. Allen, F. Mistree","doi":"10.1115/detc2019-97372","DOIUrl":"https://doi.org/10.1115/detc2019-97372","url":null,"abstract":"\u0000 Decision Support Problems (DSPs) are used to model design decisions involving multiple trade-offs. In practice, such design decisions are also coupled, that is, these decisions must be modelled by identifying and addressing the influence they exert on one another. Hence, we need to classify coupled decision problems and to introduce methods for managing uncertainty for such problems. Classification of coupled decision problems allows for the development and execution of decision templates to effect design and to archive design-related knowledge on a computer. Incorporating robustness metrics allows for the exploration of robust design solutions for coupled decision problems by managing uncertainty.\u0000 In this paper, we present a classification scheme for coupled decisions using DSPs, called the Decision Scenario Matrix and we illustrate its utility by solving a coupled problem using DSPs. The design of a beam to be used as a fender is used to illustrate the efficacy of the formulation of coupled problems. In the first example, we determine a robust design, that is, determine the dimensions of the fender and simultaneously design the material recognizing that the computational models are incomplete and inaccurate. In the second example, we determine robust design solutions when design decisions are coupled, that is, determine the dimensions of the fender and select the material concurrently. Our focus, in this paper, is on illustrating the efficacy of the method rather than on the results.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"14 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":"132456320","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}
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