Recent years have witnessed a tremendous growth of interest in multi-robot system which can execute more complex tasks compared to single robot. To improve the operational life of multi-robot system and address challenges in long-duration mission, the solar-powered multi-robot system has been demonstrated to be an effective solution. To ensure efficient operation of solar-powered multi-robot system, we propose a multi-criteria mixed integer programming model for multi-robot mission planning to minimize three objectives including traveling distance, traveling time, and net energy consumption. Our proposed model is an extension of multiple vehicle routing problem considering time window, flexible speed, and energy sharing where a set of flexible speeds are proposed to explore the influence of robot’s velocity on energy consumption and solar energy harvesting. Three sets of case studies are designed to investigate the tradeoffs among the three objectives. The results demonstrate that heterogeneous multi-robot system: 1) can more efficiently utilize solar energy and 2) need a multi-criteria model to balance the three objectives.
{"title":"Multi-Criteria Mission Planning for a Solar-Powered Multi-Robot System","authors":"Di Wang, Mengqi Hu, Yang Gao","doi":"10.1115/DETC2018-85683","DOIUrl":"https://doi.org/10.1115/DETC2018-85683","url":null,"abstract":"Recent years have witnessed a tremendous growth of interest in multi-robot system which can execute more complex tasks compared to single robot. To improve the operational life of multi-robot system and address challenges in long-duration mission, the solar-powered multi-robot system has been demonstrated to be an effective solution. To ensure efficient operation of solar-powered multi-robot system, we propose a multi-criteria mixed integer programming model for multi-robot mission planning to minimize three objectives including traveling distance, traveling time, and net energy consumption. Our proposed model is an extension of multiple vehicle routing problem considering time window, flexible speed, and energy sharing where a set of flexible speeds are proposed to explore the influence of robot’s velocity on energy consumption and solar energy harvesting. Three sets of case studies are designed to investigate the tradeoffs among the three objectives. The results demonstrate that heterogeneous multi-robot system: 1) can more efficiently utilize solar energy and 2) need a multi-criteria model to balance the three objectives.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128964214","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}
Alexandre Bekhradi, B. Yannou, François Cluzel, M. Kokkolaras
Experimentation and validation tests conducted by or for technology startups are often costly, time-consuming, and, above all, not well organized. A review of the literature shows that existing tools and methods are either oriented towards lean iterative tests or strongly focused on technology improvement. There is therefore a gap to bridge by providing tangible decision-making supports involving both market and technology aspects. This paper introduces a new quantitative methodology called RITHM (Roadmapping Investments in TecHnology and Marketing), which is a structured process that enables startups to systematically experiment and reach, with relatively small effort, adequate maturity level for the most promising markets. The objective of this methodology is to model and optimize tests in the front end of innovation to progressively reduce uncertainties and risks before the launch of the product. A case study of a shape shifting technology is presented in this paper to illustrate the application of RITHM.
{"title":"Decision Support for R&D Activities of Innovative Technologies","authors":"Alexandre Bekhradi, B. Yannou, François Cluzel, M. Kokkolaras","doi":"10.1115/DETC2018-85657","DOIUrl":"https://doi.org/10.1115/DETC2018-85657","url":null,"abstract":"Experimentation and validation tests conducted by or for technology startups are often costly, time-consuming, and, above all, not well organized. A review of the literature shows that existing tools and methods are either oriented towards lean iterative tests or strongly focused on technology improvement. There is therefore a gap to bridge by providing tangible decision-making supports involving both market and technology aspects. This paper introduces a new quantitative methodology called RITHM (Roadmapping Investments in TecHnology and Marketing), which is a structured process that enables startups to systematically experiment and reach, with relatively small effort, adequate maturity level for the most promising markets. The objective of this methodology is to model and optimize tests in the front end of innovation to progressively reduce uncertainties and risks before the launch of the product. A case study of a shape shifting technology is presented in this paper to illustrate the application of RITHM.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121122263","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}
Testing of components at higher-than-nominal stress level provides an effective way of reducing the required testing effort for system reliability assessment. Due to various reasons, not all components are directly testable in practice. The missing information of untestable components poses significant challenges to the accurate evaluation of system reliability. This paper proposes a sequential accelerated life testing (SALT) design framework for system reliability assessment of systems with untestable components. In the proposed framework, system-level tests are employed in conjunction with component-level tests to effectively reduce the uncertainty in the system reliability evaluation. To minimize the number of system-level tests which are much more expensive than the component-level tests, the accelerated life testing design is performed sequentially. In each design cycle, testing resources are allocated to component-level or system-level tests according to the uncertainty analysis from system reliability evaluation. The component-level or system-level testing information obtained from the optimized testing plans are then aggregated to obtain the overall system reliability estimate using Bayesian methods. The aggregation of component-level and system-level testing information allows for an effective uncertainty reduction in the system reliability evaluation. Results of two numerical examples demonstrate the effectiveness of the proposed method.
{"title":"Sequential Accelerated Life Testing Design for System Reliability Analysis With Untestable Components","authors":"Zhen Hu, Z. Mourelatos","doi":"10.1115/DETC2018-85373","DOIUrl":"https://doi.org/10.1115/DETC2018-85373","url":null,"abstract":"Testing of components at higher-than-nominal stress level provides an effective way of reducing the required testing effort for system reliability assessment. Due to various reasons, not all components are directly testable in practice. The missing information of untestable components poses significant challenges to the accurate evaluation of system reliability. This paper proposes a sequential accelerated life testing (SALT) design framework for system reliability assessment of systems with untestable components. In the proposed framework, system-level tests are employed in conjunction with component-level tests to effectively reduce the uncertainty in the system reliability evaluation. To minimize the number of system-level tests which are much more expensive than the component-level tests, the accelerated life testing design is performed sequentially. In each design cycle, testing resources are allocated to component-level or system-level tests according to the uncertainty analysis from system reliability evaluation. The component-level or system-level testing information obtained from the optimized testing plans are then aggregated to obtain the overall system reliability estimate using Bayesian methods. The aggregation of component-level and system-level testing information allows for an effective uncertainty reduction in the system reliability evaluation. Results of two numerical examples demonstrate the effectiveness of the proposed method.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124577920","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}
Functionally graded materials (FGMs) are heterogeneous materials engineered to vary material composition across the volume of an object. Controlled mixture and deposition of each material through a manufactured part can ultimately allow for specific material properties defined in different regions of a structure. While such structures are traditionally difficult to manufacture, additive manufacturing processes, such as directed energy deposition, material jetting, and material extrusion, have recently increased the manufacturability of FGMs. However, the existing digital design workflow lacks the ability to accurately mix and assign multiple materials to a given volume, especially in the case of toolpath dependent deposition processes like filament-based material extrusion. In this paper, we will address this limitation by using a voxel-based representation approach, where material values are assigned across a pixel grid on each geometry slice before converting to toolpath information for manufacturing. This approach allows for creation of structures with increased material complexity decoupled from the external geometry of the design space, an approach not yet demonstrated in the existing literature. By using a dual-feed, single melt-pool extrusion nozzle system, this research demonstrates the ability to accurately recreate mathematically derived gradients while establishing a digital workflow capable of integrating with the material extrusion AM process.
{"title":"A Voxel-Based Design Approach for Creating Functionally Graded Structures via Material Extrusion Additive Manufacturing","authors":"Daniel R. Spillane, N. Meisel","doi":"10.1115/DETC2018-85618","DOIUrl":"https://doi.org/10.1115/DETC2018-85618","url":null,"abstract":"Functionally graded materials (FGMs) are heterogeneous materials engineered to vary material composition across the volume of an object. Controlled mixture and deposition of each material through a manufactured part can ultimately allow for specific material properties defined in different regions of a structure. While such structures are traditionally difficult to manufacture, additive manufacturing processes, such as directed energy deposition, material jetting, and material extrusion, have recently increased the manufacturability of FGMs. However, the existing digital design workflow lacks the ability to accurately mix and assign multiple materials to a given volume, especially in the case of toolpath dependent deposition processes like filament-based material extrusion. In this paper, we will address this limitation by using a voxel-based representation approach, where material values are assigned across a pixel grid on each geometry slice before converting to toolpath information for manufacturing. This approach allows for creation of structures with increased material complexity decoupled from the external geometry of the design space, an approach not yet demonstrated in the existing literature. By using a dual-feed, single melt-pool extrusion nozzle system, this research demonstrates the ability to accurately recreate mathematically derived gradients while establishing a digital workflow capable of integrating with the material extrusion AM process.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132618889","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 work leverages the current state of the art in reinforcement learning for continuous control, the Deep Deterministic Policy Gradient (DDPG) algorithm, towards the optimal 24-hour dispatch of shared energy assets within building clusters. The modeled DDPG agent interacts with a battery environment, designed to emulate a shared battery system. The aim here is to not only learn an efficient charged/discharged policy, but to also address the continuous domain question of how much energy should be charged or discharged. Experimentally, we examine the impact of the learned dispatch strategy towards minimizing demand peaks within the building cluster. Our results show that across the variety of building cluster combinations studied, the algorithm is able to learn and exploit energy arbitrage, tailoring it into battery dispatch strategies for peak demand shifting.
{"title":"Control of Shared Energy Storage Assets Within Building Clusters Using Reinforcement Learning","authors":"Philip Odonkor, K. Lewis","doi":"10.1115/DETC2018-86094","DOIUrl":"https://doi.org/10.1115/DETC2018-86094","url":null,"abstract":"This work leverages the current state of the art in reinforcement learning for continuous control, the Deep Deterministic Policy Gradient (DDPG) algorithm, towards the optimal 24-hour dispatch of shared energy assets within building clusters. The modeled DDPG agent interacts with a battery environment, designed to emulate a shared battery system. The aim here is to not only learn an efficient charged/discharged policy, but to also address the continuous domain question of how much energy should be charged or discharged. Experimentally, we examine the impact of the learned dispatch strategy towards minimizing demand peaks within the building cluster. Our results show that across the variety of building cluster combinations studied, the algorithm is able to learn and exploit energy arbitrage, tailoring it into battery dispatch strategies for peak demand shifting.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131562399","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}
Due to the limitations of currently available artificial spinal discs stemming from anatomical unfit and unnatural motion, patient-specific elastomeric artificial spinal discs are conceived as a promising solution to improve clinical results. Multimaterial Additive Manufacturing (AM) has the potential to facilitate the production of an elastomeric composite artificial disc with complex personalized geometry and controlled material distribution. Motivated by the potential combined advantages of personalized artificial spinal discs and multi-material AM, a biomimetic multi-material elastomeric artificial disc design with several matrix sections and a crisscross fiber network is proposed in this study. To determine the optimized material distribution of each component for natural motion restoration, a computational method is proposed. The method consists of automatic generation of a patient-specific disc Finite Element (FE) model followed by material property optimization. Biologically inspired heuristics are incorporated into the optimization process to reduce the number of design variables in order to facilitate convergence. The general applicability of the method is verified by designing both lumbar and cervical artificial discs with varying geometries, natural rotational motion ranges, and rotational stiffness requirements. The results show that the proposed method is capable of producing a patient-specific artificial spinal disc design with customized geometry and optimized material distribution to achieve natural spinal rotational motions. Future work focuses on extending the method to also include implant strength and shock absorption behavior into the optimization as well as identifying a suitable AM process for manufacturing.
{"title":"Computational Design of a Personalized Artificial Spinal Disc for Additive Manufacturing With Physiological Rotational Motions","authors":"Zhiyang Yu, T. Stanković, K. Shea","doi":"10.1115/DETC2018-85921","DOIUrl":"https://doi.org/10.1115/DETC2018-85921","url":null,"abstract":"Due to the limitations of currently available artificial spinal discs stemming from anatomical unfit and unnatural motion, patient-specific elastomeric artificial spinal discs are conceived as a promising solution to improve clinical results. Multimaterial Additive Manufacturing (AM) has the potential to facilitate the production of an elastomeric composite artificial disc with complex personalized geometry and controlled material distribution. Motivated by the potential combined advantages of personalized artificial spinal discs and multi-material AM, a biomimetic multi-material elastomeric artificial disc design with several matrix sections and a crisscross fiber network is proposed in this study. To determine the optimized material distribution of each component for natural motion restoration, a computational method is proposed. The method consists of automatic generation of a patient-specific disc Finite Element (FE) model followed by material property optimization. Biologically inspired heuristics are incorporated into the optimization process to reduce the number of design variables in order to facilitate convergence. The general applicability of the method is verified by designing both lumbar and cervical artificial discs with varying geometries, natural rotational motion ranges, and rotational stiffness requirements. The results show that the proposed method is capable of producing a patient-specific artificial spinal disc design with customized geometry and optimized material distribution to achieve natural spinal rotational motions. Future work focuses on extending the method to also include implant strength and shock absorption behavior into the optimization as well as identifying a suitable AM process for manufacturing.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116812228","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}
Ashish M. Chaudhari, Ilias Bilionis, Jitesh H. Panchal
Designers make process-level decisions to (i) select designs for performance evaluation, (ii) select information source, and (iii) decide whether to stop design exploration. These decisions are influenced by problem-related factors, such as costs and uncertainty in information sources, and budget constraints for design evaluations. The objective of this paper is to analyze individuals’ strategies for making process-level decisions under the availability of noisy information sources of different cost and uncertainty, and limited budget. Our approach involves a) conducting a behavioral experiment with an engineering optimization task to collect data on subjects’ decision strategies, b) eliciting their decision strategies using a survey, and c) performing a descriptive analysis to compare elicited strategies and observations from the data. We observe that subjects use specific criteria such as fixed values of attributes, highest prediction of performance, highest uncertainty in performance, and attribute thresholds when making decisions of interest. When subjects have higher budget, they are less likely to evaluate points having highest prediction of performance, and more likely to evaluate points having highest uncertainty in performance. Further, subjects conduct expensive evaluations even when their decisions have not sufficiently converged to the region of maximum performance in the design space and improvements from additional cheap evaluations are large. The implications of the results in identifying deviations from optimal strategies and structuring decisions for further model development are discussed.
{"title":"How Do Designers Choose Among Multiple Noisy Information Sources in Engineering Design Optimization? An Experimental Study","authors":"Ashish M. Chaudhari, Ilias Bilionis, Jitesh H. Panchal","doi":"10.1115/DETC2018-85460","DOIUrl":"https://doi.org/10.1115/DETC2018-85460","url":null,"abstract":"Designers make process-level decisions to (i) select designs for performance evaluation, (ii) select information source, and (iii) decide whether to stop design exploration. These decisions are influenced by problem-related factors, such as costs and uncertainty in information sources, and budget constraints for design evaluations. The objective of this paper is to analyze individuals’ strategies for making process-level decisions under the availability of noisy information sources of different cost and uncertainty, and limited budget. Our approach involves a) conducting a behavioral experiment with an engineering optimization task to collect data on subjects’ decision strategies, b) eliciting their decision strategies using a survey, and c) performing a descriptive analysis to compare elicited strategies and observations from the data. We observe that subjects use specific criteria such as fixed values of attributes, highest prediction of performance, highest uncertainty in performance, and attribute thresholds when making decisions of interest. When subjects have higher budget, they are less likely to evaluate points having highest prediction of performance, and more likely to evaluate points having highest uncertainty in performance. Further, subjects conduct expensive evaluations even when their decisions have not sufficiently converged to the region of maximum performance in the design space and improvements from additional cheap evaluations are large. The implications of the results in identifying deviations from optimal strategies and structuring decisions for further model development are discussed.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123722036","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}
Matthew L. Dering, James Cunningham, Raj Desai, M. Yukish, T. Simpson, Conrad S. Tucker
In this paper, we present a method that uses a physics-based virtual environment to evaluate the feasibility of neural network-based generated designs. Deep learning models rely on large training data sets that are used for training. These training data sets are typically validated by human designers that have a conceptual understanding of the problem being solved. However, the requirement of human training data severely constrains the size and availability of training data for computer generated models due to the manual process of either creating or labeling such data sets. Furthermore, there may be misclassification errors that result from human labeling. To mitigate these challenges, we present a physics-based simulation environment that helps users discover correlations between the form of a generated design and the physical constraints that relate to its function. We hypothesize that training data that includes machine validated designs from a physics-based virtual environment will increase the probability of generative models creating functionally-feasible design concepts. A case study involving a generative model that is trained on over 70,000 human 2D boat sketches is used to test the hypothesis. Knowledge gained from testing this hypothesis will provide human designers with insights into the importance of training data in the resulting design solutions generated by deep neural networks.
{"title":"A Physics-Based Virtual Environment for Enhancing the Quality of Deep Generative Designs","authors":"Matthew L. Dering, James Cunningham, Raj Desai, M. Yukish, T. Simpson, Conrad S. Tucker","doi":"10.1115/DETC2018-86333","DOIUrl":"https://doi.org/10.1115/DETC2018-86333","url":null,"abstract":"In this paper, we present a method that uses a physics-based virtual environment to evaluate the feasibility of neural network-based generated designs. Deep learning models rely on large training data sets that are used for training. These training data sets are typically validated by human designers that have a conceptual understanding of the problem being solved. However, the requirement of human training data severely constrains the size and availability of training data for computer generated models due to the manual process of either creating or labeling such data sets. Furthermore, there may be misclassification errors that result from human labeling. To mitigate these challenges, we present a physics-based simulation environment that helps users discover correlations between the form of a generated design and the physical constraints that relate to its function. We hypothesize that training data that includes machine validated designs from a physics-based virtual environment will increase the probability of generative models creating functionally-feasible design concepts. A case study involving a generative model that is trained on over 70,000 human 2D boat sketches is used to test the hypothesis. Knowledge gained from testing this hypothesis will provide human designers with insights into the importance of training data in the resulting design solutions generated by deep neural networks.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122874883","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 investigates the application of Superformula for structural synthesis. The focus is set on the lightweight design of parts that can be realized using discrete lattice structures. While the design domain will be obtained using the Superformula, a tetrahedral meshing technique will be applied to this domain to generate the topology of the lattice structure. The motivation for this investigation stems from the property of the Superformula to easily represent complex biological shapes, which opens a possibility to directly link a structural synthesis to a biomimetic design. Currently, numerous results are being reported showing the development of a wide range of design methods and tools that first study and then utilize the solutions and principles from the nature to solve technical problems. However, none of these methods and tools quantitatively utilizes these principles in the form of nature inspired shapes that can be controlled parametrically. The motivation for this work is also in part due to the mathematical formulation of the Superformula as a generalization of a superellipse, which, in contrast to the normal surface modeling offers a very compact and easy way to handle set of rich shape variants with promising applications in structural synthesis. The structural synthesis approach is organized as a volume minimization using Simulated Annealing (SA) to search over the topology and shape of the lattice structure. The fitness of each of candidate solutions generated by SA is determined based on the outcome of lattice member sizing for which an Interior Point based method is applied. The approach is validated with a case study involving inline skate wheel spokes.
{"title":"Topology, Shape, and Size Optimization of Additively Manufactured Lattice Structures Based on the Superformula","authors":"Andrea Nessi, T. Stanković","doi":"10.1115/DETC2018-86191","DOIUrl":"https://doi.org/10.1115/DETC2018-86191","url":null,"abstract":"This paper investigates the application of Superformula for structural synthesis. The focus is set on the lightweight design of parts that can be realized using discrete lattice structures. While the design domain will be obtained using the Superformula, a tetrahedral meshing technique will be applied to this domain to generate the topology of the lattice structure. The motivation for this investigation stems from the property of the Superformula to easily represent complex biological shapes, which opens a possibility to directly link a structural synthesis to a biomimetic design. Currently, numerous results are being reported showing the development of a wide range of design methods and tools that first study and then utilize the solutions and principles from the nature to solve technical problems. However, none of these methods and tools quantitatively utilizes these principles in the form of nature inspired shapes that can be controlled parametrically. The motivation for this work is also in part due to the mathematical formulation of the Superformula as a generalization of a superellipse, which, in contrast to the normal surface modeling offers a very compact and easy way to handle set of rich shape variants with promising applications in structural synthesis. The structural synthesis approach is organized as a volume minimization using Simulated Annealing (SA) to search over the topology and shape of the lattice structure. The fitness of each of candidate solutions generated by SA is determined based on the outcome of lattice member sizing for which an Interior Point based method is applied. The approach is validated with a case study involving inline skate wheel spokes.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128860686","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}
Porous materials / structures have wide applications in industry, since the sizes, shapes and positions of their pores can be adjusted on various demands. However, the precise control and performance oriented design of porous structures are still urgent and challenging, especially when the manufacturing technology is well developed due to 3D printing. In this study, the control and design of anisotropic porous structures are studied with more degrees of freedom than isotropic structures, and can achieve more complex mechanical goals. The proposed approach introduces Super Formula to represent the structural cells, maps the design problem to an optimal problem using PGD, and solves the optimal problem using MMA to obtain the structure with desired performance. The proposed approach is also tested on the performance of the expansion of design space, the capture of the physical orientation and so on.
{"title":"Performance Oriented Design of Semiregular Anisotropic Porous Structures","authors":"Chao Xu, L. Pan, Ming Li, Shuming Gao","doi":"10.1115/DETC2018-85928","DOIUrl":"https://doi.org/10.1115/DETC2018-85928","url":null,"abstract":"Porous materials / structures have wide applications in industry, since the sizes, shapes and positions of their pores can be adjusted on various demands. However, the precise control and performance oriented design of porous structures are still urgent and challenging, especially when the manufacturing technology is well developed due to 3D printing. In this study, the control and design of anisotropic porous structures are studied with more degrees of freedom than isotropic structures, and can achieve more complex mechanical goals. The proposed approach introduces Super Formula to represent the structural cells, maps the design problem to an optimal problem using PGD, and solves the optimal problem using MMA to obtain the structure with desired performance. The proposed approach is also tested on the performance of the expansion of design space, the capture of the physical orientation and so on.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"2005 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127333952","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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