Yilong Zhang, Bifa Chen, Yuxuan Du, Ye Qiao, Cunfu Wang
Advances in additive manufacturing enable fabrication of architected materials composed of microstructures with extreme mechanical properties. In the design of such architected materials, the parameterization of microstructures determines not just the computational cost, but also connectivity between adjacent microstructures. In this paper, we propose a periodic composite function(PCF)-based approach for designing microstructures. The shape of the microstructures is characterized by the value of the periodic composite functions. The proposed method can program microstructures with both positive and negative Poisson's ratios by a small number of parameters. Furthermore, due to its implicit representation, the proposed method allows for continuously tiling of microstructures with different mechanical properties. Explicit geometric features of the PCF-based microstructures are extracted, and the condition to maintain connectivity between adjacent microstructures is derived. Based on the proposed approach, multiple groups of 2D and 3D microstructures with Poisson's ratios ranging from negative to positive are presented. Combining with a deep neural network(DNN) based surrogate model to predict macroscopic material properties of the microstructures, the proposed method is applied to the design of architected materials for elastic deformation control. Numerical examples on both microstructure representation and architected materials design are presented to demonstrate the efficacy of the proposed approach.
{"title":"Periodic composite function-based approach for designing architected materials with programmable Poisson's ratios","authors":"Yilong Zhang, Bifa Chen, Yuxuan Du, Ye Qiao, Cunfu Wang","doi":"10.1115/1.4064634","DOIUrl":"https://doi.org/10.1115/1.4064634","url":null,"abstract":"\u0000 Advances in additive manufacturing enable fabrication of architected materials composed of microstructures with extreme mechanical properties. In the design of such architected materials, the parameterization of microstructures determines not just the computational cost, but also connectivity between adjacent microstructures. In this paper, we propose a periodic composite function(PCF)-based approach for designing microstructures. The shape of the microstructures is characterized by the value of the periodic composite functions. The proposed method can program microstructures with both positive and negative Poisson's ratios by a small number of parameters. Furthermore, due to its implicit representation, the proposed method allows for continuously tiling of microstructures with different mechanical properties. Explicit geometric features of the PCF-based microstructures are extracted, and the condition to maintain connectivity between adjacent microstructures is derived. Based on the proposed approach, multiple groups of 2D and 3D microstructures with Poisson's ratios ranging from negative to positive are presented. Combining with a deep neural network(DNN) based surrogate model to predict macroscopic material properties of the microstructures, the proposed method is applied to the design of architected materials for elastic deformation control. Numerical examples on both microstructure representation and architected materials design are presented to demonstrate the efficacy of the proposed approach.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140471120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The parallel mechanisms (PMs) with configurable platform have advantages of flexibility, high speed and extra operability over PMs with common platform. This paper proposes a systematic approach for synthesizing 3 degrees of freedom (DOF) translational parallel mechanisms with configurable platforms of one translation DOF (TPMs-T) based on the finite screw. The motion relationship between configurable platform and limbs is discussed to achieve the motion requirement of the TPMs-T limbs. The equivalence principle of kinematic joints is further pinpointed, and a series of lower mobility limbs have been developed. At last, the geometric relationship of assembly conditions is derived which can contribute to quickly solving the intersection of limb motions, a series of TPMs-T are constructed to verify the assembly conditions and the fully-controlled condition is discussed.
{"title":"Structure synthesis of 3-DOF translational parallel mechanisms with configurable platform of translation","authors":"Jingyao Zhang, Jiantao Yao, Hongyu Zhang, Jiawei Guo, Shuai Zhang","doi":"10.1115/1.4064631","DOIUrl":"https://doi.org/10.1115/1.4064631","url":null,"abstract":"\u0000 The parallel mechanisms (PMs) with configurable platform have advantages of flexibility, high speed and extra operability over PMs with common platform. This paper proposes a systematic approach for synthesizing 3 degrees of freedom (DOF) translational parallel mechanisms with configurable platforms of one translation DOF (TPMs-T) based on the finite screw. The motion relationship between configurable platform and limbs is discussed to achieve the motion requirement of the TPMs-T limbs. The equivalence principle of kinematic joints is further pinpointed, and a series of lower mobility limbs have been developed. At last, the geometric relationship of assembly conditions is derived which can contribute to quickly solving the intersection of limb motions, a series of TPMs-T are constructed to verify the assembly conditions and the fully-controlled condition is discussed.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140472830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wei Li, Yuzhen Niu, Haihong Huang, A. Garg, Liang Gao
Robust design optimization (RDO) is a potent methodology that ensures stable performance in designed products during their operational phase. However, there remains a scarcity of robust design optimization methods that account for the intricacies of multidisciplinary coupling. In this paper we propose a multidisciplinary robust design optimization (MRDO) framework for physical systems under sparse samples containing the extreme scenario. The collaboration model is used to select samples that comply with multidisciplinary feasibility, avoiding time-consuming multidisciplinary decoupling analyses. To assess the robustness of sparse samples containing the extreme scenario, linear moment estimation is employed as the evaluation metric. The comparative analysis of MRDO results is conducted across various sample sizes, with and without the presence of the extreme scenario. The effectiveness and reliability of the proposed method are demonstrated through a mathematical case, a conceptual aircraft sizing design, and an energy efficiency optimization of a hobbing machine tool.
{"title":"Multidisciplinary Robust Design Optimization Incorporating Extreme Scenario in Sparse Samples","authors":"Wei Li, Yuzhen Niu, Haihong Huang, A. Garg, Liang Gao","doi":"10.1115/1.4064632","DOIUrl":"https://doi.org/10.1115/1.4064632","url":null,"abstract":"\u0000 Robust design optimization (RDO) is a potent methodology that ensures stable performance in designed products during their operational phase. However, there remains a scarcity of robust design optimization methods that account for the intricacies of multidisciplinary coupling. In this paper we propose a multidisciplinary robust design optimization (MRDO) framework for physical systems under sparse samples containing the extreme scenario. The collaboration model is used to select samples that comply with multidisciplinary feasibility, avoiding time-consuming multidisciplinary decoupling analyses. To assess the robustness of sparse samples containing the extreme scenario, linear moment estimation is employed as the evaluation metric. The comparative analysis of MRDO results is conducted across various sample sizes, with and without the presence of the extreme scenario. The effectiveness and reliability of the proposed method are demonstrated through a mathematical case, a conceptual aircraft sizing design, and an energy efficiency optimization of a hobbing machine tool.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140474405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alex Barnaby-Brown, Molly Goldstein, John Clay, Onan Demirel, Xingang Li, Zhenghui Sha
Computer-aided design (CAD) is a standard design tool used in engineering practice and by students. CAD has become increasingly analytic and inventive in incorporating AI approaches to design with generative design to help expand designers' divergent thinking. However, because generative design technologies are new, we know very little about generative design thinking in students. The purpose of this research is threefold: explore how students engage in the design process when using generative design software, understand the relationship between students' divergent and convergent thinking abilities, and investigate in what ways students' divergent and convergent abilities are related to their generative design understanding. This study was set in an introductory graphics and design course where student designers used Fusion 360. Data collected included a generative design CAD module and both divergent and convergent psychological tests. The results suggest that students approach generative design decision-making similarly to how beginning designers approach standard decision-making and that students' divergent and convergent thinking is not related to their generative design thinking. This study shows that new computational tools might present the same challenges to beginning designers as conventional tools. Instructors should be aware of informed design practices, should continue to encourage students to grow into informed designers by educating them on design practices without technology and, by introducing them to new technology such as AI-driven generative design.
{"title":"A Study on Generative Design Reasoning and Students' Divergent and Convergent Thinking","authors":"Alex Barnaby-Brown, Molly Goldstein, John Clay, Onan Demirel, Xingang Li, Zhenghui Sha","doi":"10.1115/1.4064564","DOIUrl":"https://doi.org/10.1115/1.4064564","url":null,"abstract":"\u0000 Computer-aided design (CAD) is a standard design tool used in engineering practice and by students. CAD has become increasingly analytic and inventive in incorporating AI approaches to design with generative design to help expand designers' divergent thinking. However, because generative design technologies are new, we know very little about generative design thinking in students. The purpose of this research is threefold: explore how students engage in the design process when using generative design software, understand the relationship between students' divergent and convergent thinking abilities, and investigate in what ways students' divergent and convergent abilities are related to their generative design understanding. This study was set in an introductory graphics and design course where student designers used Fusion 360. Data collected included a generative design CAD module and both divergent and convergent psychological tests. The results suggest that students approach generative design decision-making similarly to how beginning designers approach standard decision-making and that students' divergent and convergent thinking is not related to their generative design thinking. This study shows that new computational tools might present the same challenges to beginning designers as conventional tools. Instructors should be aware of informed design practices, should continue to encourage students to grow into informed designers by educating them on design practices without technology and, by introducing them to new technology such as AI-driven generative design.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139601313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As artificial intelligence (AI) systems become increasingly capable of performing design tasks, they are expected to be deployed to assist human designers' decision-making in a greater variety of ways. For complex design problems such as those with multiple objectives, one AI may not always perform its expected accuracy due to the complexity of decision-making, and therefore multiples of AIs may be implemented to provide design suggestions. For such assistance to be productive, human designers must develop appropriate confidence in each AI and in themselves and accept or reject AI inputs accordingly. This work conducts a human subjects experiment to examine the development of a human designer's confidence in each AI and self-confidence throughout decision-making assisted by two AIs and how these confidences influence the decision to accept AI inputs. Major findings demonstrate that certain performance combinations of the two AIs and feedback lead to severe decreases in a human designer's confidences. Additionally, a human designer's decision to accept AI suggestions depends on their self-confidence and confidence in one of the two AIs. Finally, an additional AI does not increase a human designer's likelihood of conforming to AI suggestions. Therefore, in comparison to a scenario with one AI, the results in this work caution the implementation of an additional AI to AI-assisted decision-making scenarios. The insights also inform the design and management of human-AI teams to improve the outcome of AI-assisted decision-making.
{"title":"Human Designers' Dynamic Confidence and Decision-Making When Working with More than One AI","authors":"L. Chong, K. Kotovsky, Jonathan Cagan","doi":"10.1115/1.4064565","DOIUrl":"https://doi.org/10.1115/1.4064565","url":null,"abstract":"\u0000 As artificial intelligence (AI) systems become increasingly capable of performing design tasks, they are expected to be deployed to assist human designers' decision-making in a greater variety of ways. For complex design problems such as those with multiple objectives, one AI may not always perform its expected accuracy due to the complexity of decision-making, and therefore multiples of AIs may be implemented to provide design suggestions. For such assistance to be productive, human designers must develop appropriate confidence in each AI and in themselves and accept or reject AI inputs accordingly. This work conducts a human subjects experiment to examine the development of a human designer's confidence in each AI and self-confidence throughout decision-making assisted by two AIs and how these confidences influence the decision to accept AI inputs. Major findings demonstrate that certain performance combinations of the two AIs and feedback lead to severe decreases in a human designer's confidences. Additionally, a human designer's decision to accept AI suggestions depends on their self-confidence and confidence in one of the two AIs. Finally, an additional AI does not increase a human designer's likelihood of conforming to AI suggestions. Therefore, in comparison to a scenario with one AI, the results in this work caution the implementation of an additional AI to AI-assisted decision-making scenarios. The insights also inform the design and management of human-AI teams to improve the outcome of AI-assisted decision-making.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139602518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reliability-based control co-design (RBCCD) formulations have been developed for the design of stochastic dynamic systems. To address the limitations of their current formulations, and to enable higher-fidelity solutions for complex problems, a novel reliability-based multidisciplinary feasible (MDF) formulation of multidisciplinary dynamic system design optimization (RB-MDF-MDSDO) and a new reliability analysis method using generalized polynomial chaos (gPC) expansion for RBCCD were developed in previous work. Although the gPC expansion method was initially selected for the reliability analysis of simulation-based RBCCD, its performance against state-of-the-art, the most-probable-point (MPP) method, has not been established yet. Therefore, in this work, the first-ever MPP-based formulations of RB-MDF-MDSDO are developed, and using two engineering test problems, the new formulations' solution efficiency and accuracy are compared to those from the gPC-based formulation. Numerical results reveal that the gPC expansion method is marginally more accurate than the MPP algorithms, and therefore, it is more suitable for accuracy-sensitive applications. Conversely, the MPP algorithms are much more efficient, and thus, are more attractive for problems where solution efficiency is the priority.
{"title":"A Comparative Study Between the Generalized Polynomial Chaos Expansion- and First-Order Reliability Method-Based Formulations of Simulation-Based Control Co-Design","authors":"M. Behtash, Michael J. Alexander-Ramos","doi":"10.1115/1.4064567","DOIUrl":"https://doi.org/10.1115/1.4064567","url":null,"abstract":"\u0000 Reliability-based control co-design (RBCCD) formulations have been developed for the design of stochastic dynamic systems. To address the limitations of their current formulations, and to enable higher-fidelity solutions for complex problems, a novel reliability-based multidisciplinary feasible (MDF) formulation of multidisciplinary dynamic system design optimization (RB-MDF-MDSDO) and a new reliability analysis method using generalized polynomial chaos (gPC) expansion for RBCCD were developed in previous work. Although the gPC expansion method was initially selected for the reliability analysis of simulation-based RBCCD, its performance against state-of-the-art, the most-probable-point (MPP) method, has not been established yet. Therefore, in this work, the first-ever MPP-based formulations of RB-MDF-MDSDO are developed, and using two engineering test problems, the new formulations' solution efficiency and accuracy are compared to those from the gPC-based formulation. Numerical results reveal that the gPC expansion method is marginally more accurate than the MPP algorithms, and therefore, it is more suitable for accuracy-sensitive applications. Conversely, the MPP algorithms are much more efficient, and thus, are more attractive for problems where solution efficiency is the priority.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139599324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a general kinematic-based design method for optimizing the side-mounted leg mechanism of BJTUBOT, a novel multi-mission quadrupedal Earth rover. The focus issue lies in designing structural improvements that not only enhance its kinematic performance but also prevent singularity, all while meeting the demands for miniaturization and lightweight without deviating from the original leg concept. To address this issue, a novel 3-UPRU&PPRR mechanism is envisaged based on the original configuration. Around the unique structural features of this target mechanism, its inverse kinematic solution and Jacobian matrix are calculated, a coupled motion relation between a key limb and its moving platform is presented. In order to achieve singularity avoidance, typical singularity configurations based on line-geometry analysis are given. In accordance with this result, an initial configuration for multi-objective dimensional optimization is presented. To further enhance its kinematic performance, we introduce the use of the global conditional index performance at extreme positions as one of the optimization criteria based on the NSGAII algorithm, and directly measuring the crowding distance using the position vector of the universal joints on the moving platform. This optimized mechanism prototype is demonstrated in a single-leg Adams simulation, which exhibits good velocity mapping effects and displacement accuracy. Finally, a new BJTUBOT prototype is constructed based on the optimized leg, and its flexibility was tested with various classical forms of motions. The workflow in this paper significantly improves the leg performance under the current design needs.
{"title":"A Kinematics-based Optimization Design for the Leg Mechanism of a Novel Earth Rover","authors":"Yifan Wu, Sheng Guo, Lianzheng Niu, Xinhua Yang, Fuqun Zhao, Yufan He","doi":"10.1115/1.4064566","DOIUrl":"https://doi.org/10.1115/1.4064566","url":null,"abstract":"\u0000 This paper proposes a general kinematic-based design method for optimizing the side-mounted leg mechanism of BJTUBOT, a novel multi-mission quadrupedal Earth rover. The focus issue lies in designing structural improvements that not only enhance its kinematic performance but also prevent singularity, all while meeting the demands for miniaturization and lightweight without deviating from the original leg concept. To address this issue, a novel 3-UPRU&PPRR mechanism is envisaged based on the original configuration. Around the unique structural features of this target mechanism, its inverse kinematic solution and Jacobian matrix are calculated, a coupled motion relation between a key limb and its moving platform is presented. In order to achieve singularity avoidance, typical singularity configurations based on line-geometry analysis are given. In accordance with this result, an initial configuration for multi-objective dimensional optimization is presented. To further enhance its kinematic performance, we introduce the use of the global conditional index performance at extreme positions as one of the optimization criteria based on the NSGAII algorithm, and directly measuring the crowding distance using the position vector of the universal joints on the moving platform. This optimized mechanism prototype is demonstrated in a single-leg Adams simulation, which exhibits good velocity mapping effects and displacement accuracy. Finally, a new BJTUBOT prototype is constructed based on the optimized leg, and its flexibility was tested with various classical forms of motions. The workflow in this paper significantly improves the leg performance under the current design needs.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139600120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jessica Menold, Alison Olechowski, C. Lauff, Katherine Fu, Julie Linsey, Maria Yang, Nicolas F. Soria Zurita, Scarlett Miller
This special issue features articles that review the changing role of design artifacts in design practice, covering topics including generative AI, digital design tools, and cloud-based collaborative platforms.
{"title":"The Role of Design Artifacts in Design","authors":"Jessica Menold, Alison Olechowski, C. Lauff, Katherine Fu, Julie Linsey, Maria Yang, Nicolas F. Soria Zurita, Scarlett Miller","doi":"10.1115/1.4064543","DOIUrl":"https://doi.org/10.1115/1.4064543","url":null,"abstract":"\u0000 This special issue features articles that review the changing role of design artifacts in design practice, covering topics including generative AI, digital design tools, and cloud-based collaborative platforms.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139605337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Waheed B. Bello, Satya R. T. Peddada, Anurag Bhattacharyya, Lawrence Zeidner, James T. Allison, Kai A. James
This article presents a novel three-dimensional topology optimization framework developed for 3D spatial packaging of interconnected systems using a geometric projection method (GPM). The proposed gradient-based topology optimization method simultaneously optimizes the locations and orientations of system components (or devices) and lengths, diameters, and trajectories of interconnects to reduce the overall system volume within the prescribed 3D design domain. The optimization is subject to geometric and physics-based constraints dictated by various system specifications, suited for a wide range of transportation (aerospace or automotive), HVACR (heating, ventilation, air-conditioning, and refrigeration), and other complex system applications. The system components and interconnects are represented using 3D parametric shapes such as cubes, cuboids, and cylinders. These objects are then projected onto a three-dimensional finite element mesh using the geometric projection method. Sensitivities are calculated for the objective function (bounding box volume) with various geometric and physics-based (thermal and hydraulic) constraints. Several case studies were performed with different component counts, interconnection topologies, and system boundary conditions are presented to exhibit the capabilities of the proposed 3D multiphysics spatial packaging optimization framework.
{"title":"Multi-Physics 3D Component Placement and Routing Optimization Using Geometric Projection","authors":"Waheed B. Bello, Satya R. T. Peddada, Anurag Bhattacharyya, Lawrence Zeidner, James T. Allison, Kai A. James","doi":"10.1115/1.4064488","DOIUrl":"https://doi.org/10.1115/1.4064488","url":null,"abstract":"\u0000 This article presents a novel three-dimensional topology optimization framework developed for 3D spatial packaging of interconnected systems using a geometric projection method (GPM). The proposed gradient-based topology optimization method simultaneously optimizes the locations and orientations of system components (or devices) and lengths, diameters, and trajectories of interconnects to reduce the overall system volume within the prescribed 3D design domain. The optimization is subject to geometric and physics-based constraints dictated by various system specifications, suited for a wide range of transportation (aerospace or automotive), HVACR (heating, ventilation, air-conditioning, and refrigeration), and other complex system applications. The system components and interconnects are represented using 3D parametric shapes such as cubes, cuboids, and cylinders. These objects are then projected onto a three-dimensional finite element mesh using the geometric projection method. Sensitivities are calculated for the objective function (bounding box volume) with various geometric and physics-based (thermal and hydraulic) constraints. Several case studies were performed with different component counts, interconnection topologies, and system boundary conditions are presented to exhibit the capabilities of the proposed 3D multiphysics spatial packaging optimization framework.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139624178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zeda Xu, C. Hong, Nicolas F. Soria Zurita, J. Gyory, Gary Stump, H. Nolte, Jonathan Cagan, Christopher McComb
Exploring the opportunities for incorporating Artificial Intelligence (AI) to support team problem solving has been the focus of intensive ongoing research. However, while the incorporation of such AI tools into human team problem solving can improve team performance, it is still unclear what modality of AI integration will lead to a genuine human-AI partnership capable of mimicking the dynamic adaptability of humans. This work unites human designers with AI Partners as fellow team members who can both reactively and proactively collaborate in real-time towards solving a complex and evolving engineering problem. Team performance and problem-solving behaviors are examined using the HyForm collaborative research platform. The problem constraints are unexpectedly changed midway through problem solving to simulate the nature of dynamically evolving engineering problems. This work shows that after the shock is introduced, human-AI hybrid teams perform similarly to human teams, demonstrating the capability of AI Partners to adapt to unexpected events. Nonetheless, hybrid teams do struggle more with coordination and communication after the shock is introduced. Overall, this work demonstrates that these AI design Partners can participate as active partners within human teams during a large, complex task, showing promise for future integration in practice.
{"title":"Adaptation through Communication: Assessing Human-AI Partnership for the Design of Complex Engineering Systems","authors":"Zeda Xu, C. Hong, Nicolas F. Soria Zurita, J. Gyory, Gary Stump, H. Nolte, Jonathan Cagan, Christopher McComb","doi":"10.1115/1.4064490","DOIUrl":"https://doi.org/10.1115/1.4064490","url":null,"abstract":"\u0000 Exploring the opportunities for incorporating Artificial Intelligence (AI) to support team problem solving has been the focus of intensive ongoing research. However, while the incorporation of such AI tools into human team problem solving can improve team performance, it is still unclear what modality of AI integration will lead to a genuine human-AI partnership capable of mimicking the dynamic adaptability of humans. This work unites human designers with AI Partners as fellow team members who can both reactively and proactively collaborate in real-time towards solving a complex and evolving engineering problem. Team performance and problem-solving behaviors are examined using the HyForm collaborative research platform. The problem constraints are unexpectedly changed midway through problem solving to simulate the nature of dynamically evolving engineering problems. This work shows that after the shock is introduced, human-AI hybrid teams perform similarly to human teams, demonstrating the capability of AI Partners to adapt to unexpected events. Nonetheless, hybrid teams do struggle more with coordination and communication after the shock is introduced. Overall, this work demonstrates that these AI design Partners can participate as active partners within human teams during a large, complex task, showing promise for future integration in practice.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139625428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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