The Sit-to-Stand (STS) motion is a crucial activity in the daily lives of individuals, and its impairment can significantly impact independence and mobility, particularly among disabled individuals. Addressing this challenge necessitates the design of mobility assist devices that can simultaneously satisfy multiple conflicting constraints. The effective design of such devices often involves the generation of numerous conceptual mechanism designs. This paper introduces an innovative single degree-of-freedom (DOF) mechanism synthesis process for developing a highly customizable Sit-to-Stand (STS) mechanical device by integrating rigid body kinematics with machine learning. Unlike traditional mechanism synthesis approaches that primarily focus on limited functional requirements, such as path or motion generation, our proposed design pipeline efficiently generates a large number of one-DOF mechanism geometries and their corresponding motion paths, known as coupler curves. Leveraging a generative Deep Neural Network (DNN), we establish a probabilistic distribution of coupler curves and their mapping to mechanism parameters. Additionally, we introduce novel metrics for quantitatively evaluating and prioritizing design concepts. The methodology yields a diverse set of viable conceptual design solutions that adhere to the specified constraints. We showcase various single-degree-of-freedom six-bar linkage mechanisms designed for STS motion, presenting them in a ranked order based on established criteria. While the primary focus is on the integration of STS motion into a versatile mobility assist device, the proposed approach holds broad applicability for addressing design challenges in various applications.
{"title":"Deep Learning Conceptual Design of Sit-to-Stand Parallel Motion Six-Bar Mechanisms","authors":"Z. Lyu, A. Purwar","doi":"10.1115/1.4066036","DOIUrl":"https://doi.org/10.1115/1.4066036","url":null,"abstract":"\u0000 The Sit-to-Stand (STS) motion is a crucial activity in the daily lives of individuals, and its impairment can significantly impact independence and mobility, particularly among disabled individuals. Addressing this challenge necessitates the design of mobility assist devices that can simultaneously satisfy multiple conflicting constraints. The effective design of such devices often involves the generation of numerous conceptual mechanism designs. This paper introduces an innovative single degree-of-freedom (DOF) mechanism synthesis process for developing a highly customizable Sit-to-Stand (STS) mechanical device by integrating rigid body kinematics with machine learning. Unlike traditional mechanism synthesis approaches that primarily focus on limited functional requirements, such as path or motion generation, our proposed design pipeline efficiently generates a large number of one-DOF mechanism geometries and their corresponding motion paths, known as coupler curves. Leveraging a generative Deep Neural Network (DNN), we establish a probabilistic distribution of coupler curves and their mapping to mechanism parameters. Additionally, we introduce novel metrics for quantitatively evaluating and prioritizing design concepts. The methodology yields a diverse set of viable conceptual design solutions that adhere to the specified constraints. We showcase various single-degree-of-freedom six-bar linkage mechanisms designed for STS motion, presenting them in a ranked order based on established criteria. While the primary focus is on the integration of STS motion into a versatile mobility assist device, the proposed approach holds broad applicability for addressing design challenges in various applications.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141823350","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}
In this study, the so-called quasi-spiral motion pattern of a particular 3R (revolute) chain, which could track a quasi-spiral curve, is discovered for application in envelope gripping. Naturally, a novel perspective on the synthesis of 1-DOF (degree of freedom) single-loop 6R and 7R mechanisms is presented to implement the quasi-spiral motion pattern. In these processes, the method of factorization of the motion polynomial is used to synthesize a series of single-loop mechanisms with the motion to generate quasi-spiral curves. By employing the proposed method, numerous innovative 6R and 7R mechanisms have been developed to construct dual layers of grippers based on specific network regulations. Taking a novel 6R mechanism as an example, the forward solution of the mechanism is determined, and the relationship between different rotation angles is obtained. Then, displaying the changes in each angle during the grasping process. Finally, the novel 6R and 7R mechanisms are employed as a unit to determine the networking method and assemble new types of grippers. Grippers formed by the aforementioned 1-DOF mechanisms exhibit the characteristics of envelope grasping.
{"title":"Structural–parametric synthesis of single-loop 6R and 7R mechanisms using factorization of motion polynomials and its application in grippers","authors":"Meng Li, Kai Liu, Jingfang Liu, Hu Ding","doi":"10.1115/1.4065724","DOIUrl":"https://doi.org/10.1115/1.4065724","url":null,"abstract":"\u0000 In this study, the so-called quasi-spiral motion pattern of a particular 3R (revolute) chain, which could track a quasi-spiral curve, is discovered for application in envelope gripping. Naturally, a novel perspective on the synthesis of 1-DOF (degree of freedom) single-loop 6R and 7R mechanisms is presented to implement the quasi-spiral motion pattern. In these processes, the method of factorization of the motion polynomial is used to synthesize a series of single-loop mechanisms with the motion to generate quasi-spiral curves. By employing the proposed method, numerous innovative 6R and 7R mechanisms have been developed to construct dual layers of grippers based on specific network regulations. Taking a novel 6R mechanism as an example, the forward solution of the mechanism is determined, and the relationship between different rotation angles is obtained. Then, displaying the changes in each angle during the grasping process. Finally, the novel 6R and 7R mechanisms are employed as a unit to determine the networking method and assemble new types of grippers. Grippers formed by the aforementioned 1-DOF mechanisms exhibit the characteristics of envelope grasping.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141352916","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}
Facing the challenges posed by increasingly complex, dynamic, and unpredictable requirements, the design process is grappling with the critical issue of ensuring sustained product satisfaction amid changing demands. This paper introduces an approach for evaluating design adaptability, considering potential future requirements. Entropy serves as a crucial indicator to quantify design effort and the Markov process is employed to simulate potential requirement changes. The information contents of design requirements and design solutions are defined based on information entropy theory, and the design adaptability of a design candidate is evaluated by calculating the extra design effort for satisfying the design requirements, which is the difference in information content between the design candidate and design requirements. Moreover, a simulation method for requirement evolution is proposed, which integrates information entropy theory and the Markov process to accommodate potential future requirements. The general design adaptability of design solutions is then calculated based on conditional entropy, taking into account the evolving design requirements. Finally, the effectiveness of the proposed approach is validated through a case study involving the design and evaluation of a hybrid additive manufacturing (HAM) device.
{"title":"General Adaptable Design and Evaluation Using Markov Processes","authors":"Zhilin Sun, Kaifeng Wang, Peihua Gu","doi":"10.1115/1.4065723","DOIUrl":"https://doi.org/10.1115/1.4065723","url":null,"abstract":"\u0000 Facing the challenges posed by increasingly complex, dynamic, and unpredictable requirements, the design process is grappling with the critical issue of ensuring sustained product satisfaction amid changing demands. This paper introduces an approach for evaluating design adaptability, considering potential future requirements. Entropy serves as a crucial indicator to quantify design effort and the Markov process is employed to simulate potential requirement changes. The information contents of design requirements and design solutions are defined based on information entropy theory, and the design adaptability of a design candidate is evaluated by calculating the extra design effort for satisfying the design requirements, which is the difference in information content between the design candidate and design requirements. Moreover, a simulation method for requirement evolution is proposed, which integrates information entropy theory and the Markov process to accommodate potential future requirements. The general design adaptability of design solutions is then calculated based on conditional entropy, taking into account the evolving design requirements. Finally, the effectiveness of the proposed approach is validated through a case study involving the design and evaluation of a hybrid additive manufacturing (HAM) device.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141355105","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}
Artificial Intelligence (AI) has the potential to revolutionize product design, and designers need to know how to best leverage its capabilities. Based on the concept–knowledge (C-K) theory, a set of inspirational stimuli (IS) for the design of AI-powered products (ISfAI) has been developed to contribute to the conceptual design stage. We extracted 40 ISs from 1,755 granted AI patents using a five-step process and validated their feasibility through a controlled experiment using three design aids: brainstorming, ISfAI Sheet, and ISfAI Cards. Results suggest that the ISfAI Cards can serve as a creative tool to enabling practitioners to generate a greater range of high-quality AI-powered ideas, particularly in terms of Novelty, Creativity, Elaboration and Flexibility. This study has practical implications for developing AI-powered products and services.
{"title":"Inspirational Stimuli to Support Creative Ideation for the Design of AI-powered Products","authors":"Xiaoneng Jin, Hua Dong, Mark Evans, Anqi Yao","doi":"10.1115/1.4065696","DOIUrl":"https://doi.org/10.1115/1.4065696","url":null,"abstract":"\u0000 Artificial Intelligence (AI) has the potential to revolutionize product design, and designers need to know how to best leverage its capabilities. Based on the concept–knowledge (C-K) theory, a set of inspirational stimuli (IS) for the design of AI-powered products (ISfAI) has been developed to contribute to the conceptual design stage. We extracted 40 ISs from 1,755 granted AI patents using a five-step process and validated their feasibility through a controlled experiment using three design aids: brainstorming, ISfAI Sheet, and ISfAI Cards. Results suggest that the ISfAI Cards can serve as a creative tool to enabling practitioners to generate a greater range of high-quality AI-powered ideas, particularly in terms of Novelty, Creativity, Elaboration and Flexibility. This study has practical implications for developing AI-powered products and services.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141361043","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 agile processes are increasingly adopted for product design and as consumer preferences are rapidly evolving with increasing information available from digital media, there is a need for a demand model that can accommodate the dynamics of product development. However, existing models of demand estimation, such as the discrete-choice models, do not capture the dynamics of product development and decision-making processes and thus are unable to effectively capture the effect of product updates and the release of information. To address this gap, we present a dynamic demand model and demonstrate how it can be used to determine the optimal time to release product updates. The demand model is based on decision field theory (DFT), which enables the modeling of the dynamic behavior of human decision makers. The contributions of this paper are as follows. First, we formulate a computational model for demand modeling built on DFT and demonstrate the viability of using the model to determine product release strategies. Second, we provide analytical approximations of the demand model and compare the accuracy of the approximated demand against the demand predicted by the dynamics model. Third, we show an example of a game played by competitors trying to optimize demand for their products by choosing the optimal update time relative to each other. Finally, we demonstrate the feasibility of parameter estimation using only the demand data.
{"title":"A Cognitive Approach for Modeling Customer Demand Dynamics for Optimal Product Release Strategies","authors":"Ian Walter, Philip E. Paré, Jitesh H. Panchal","doi":"10.1115/1.4065486","DOIUrl":"https://doi.org/10.1115/1.4065486","url":null,"abstract":"\u0000 As agile processes are increasingly adopted for product design and as consumer preferences are rapidly evolving with increasing information available from digital media, there is a need for a demand model that can accommodate the dynamics of product development. However, existing models of demand estimation, such as the discrete-choice models, do not capture the dynamics of product development and decision-making processes and thus are unable to effectively capture the effect of product updates and the release of information. To address this gap, we present a dynamic demand model and demonstrate how it can be used to determine the optimal time to release product updates. The demand model is based on decision field theory (DFT), which enables the modeling of the dynamic behavior of human decision makers. The contributions of this paper are as follows. First, we formulate a computational model for demand modeling built on DFT and demonstrate the viability of using the model to determine product release strategies. Second, we provide analytical approximations of the demand model and compare the accuracy of the approximated demand against the demand predicted by the dynamics model. Third, we show an example of a game played by competitors trying to optimize demand for their products by choosing the optimal update time relative to each other. Finally, we demonstrate the feasibility of parameter estimation using only the demand data.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141000604","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}
Structural design synthesis considering discrete elements can be formulated as a sequential decision process solved using deep reinforcement learning (DRL), shown in prior work to naturally accommodate discrete actions and efficiently provide adept solutions to a variety of problems. By modeling structural design synthesis as a Markov decision process (MDP), the states correspond to specific structural designs, the actions correspond to specific design alterations, and the rewards are related to the improvement in the altered design's performance with respect to the design objective and specified constraints. Here, the MDP action definition is extended by integrating parametric design grammars that further enable the design agent to not only alter a given structural design's topology, but also its element parameters. In considering topological and parametric actions, both the dimensionality of the state and action space and the diversity of the action types available to the agent in each state significantly increase, making the overall MDP learning task more challenging. Hence, this paper also addresses discrete design synthesis problems with large state and action spaces by significantly extending the framework. A hierarchical-inspired deep neural network architecture is developed to equip the agent to learn the type of action, topological or parametric, to apply, thus reducing the complexity of possible action choices in a given state. This extended framework is applied to the design synthesis of planar structures considering both discrete elements and cross-sectional areas, and it is observed to adeptly learn policies that synthesize high performing design solutions.
{"title":"Discrete structural design synthesis: a hierarchical-inspired deep reinforcement learning approach considering topological and parametric actions","authors":"Maximilian E. Ororbia, G. Warn","doi":"10.1115/1.4065488","DOIUrl":"https://doi.org/10.1115/1.4065488","url":null,"abstract":"\u0000 Structural design synthesis considering discrete elements can be formulated as a sequential decision process solved using deep reinforcement learning (DRL), shown in prior work to naturally accommodate discrete actions and efficiently provide adept solutions to a variety of problems. By modeling structural design synthesis as a Markov decision process (MDP), the states correspond to specific structural designs, the actions correspond to specific design alterations, and the rewards are related to the improvement in the altered design's performance with respect to the design objective and specified constraints. Here, the MDP action definition is extended by integrating parametric design grammars that further enable the design agent to not only alter a given structural design's topology, but also its element parameters. In considering topological and parametric actions, both the dimensionality of the state and action space and the diversity of the action types available to the agent in each state significantly increase, making the overall MDP learning task more challenging. Hence, this paper also addresses discrete design synthesis problems with large state and action spaces by significantly extending the framework. A hierarchical-inspired deep neural network architecture is developed to equip the agent to learn the type of action, topological or parametric, to apply, thus reducing the complexity of possible action choices in a given state. This extended framework is applied to the design synthesis of planar structures considering both discrete elements and cross-sectional areas, and it is observed to adeptly learn policies that synthesize high performing design solutions.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141000031","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}
Conceptual design is a pivotal phase of product design and development, encompassing user requirement exploration and informed solution generation. Recent generative models with their powerful content generation capabilities have been applied to conceptual design to support designers' ideation. However, the lack of transparency in their generation process and the shallow nature of their generated solutions constrain their performance in complex conceptual design tasks. In this study, we first introduce a conceptual design generation approach that combines generative models with classic design theory. This approach decomposes the conceptual design task based on design process and design attributes, and uses the Who, What, Where, When, Why, How (5W1H) method, Function-Behavior-Structure (FBS) model, and Kansei Engineering to guide generative models to generate conceptual design solutions through multi-step reasoning. Then we present an interactive system using a mind-map layout to visualize multi-step reasoning, called DesignFusion. This empowers designers to track the generation process and control inputs/outputs at each reasoning step. Two user studies show that our approach significantly enhances the quality of generated design solutions and enriches designer experience in human-AI co-creation.
{"title":"DesignFusion: Integrating Generative Models for Conceptual Design Enrichment","authors":"Liuqing Chen, Qianzhi Jing, Yixin Tsang, Qianyi Wang, Lingyun Sun, Jianxi Luo","doi":"10.1115/1.4065487","DOIUrl":"https://doi.org/10.1115/1.4065487","url":null,"abstract":"\u0000 Conceptual design is a pivotal phase of product design and development, encompassing user requirement exploration and informed solution generation. Recent generative models with their powerful content generation capabilities have been applied to conceptual design to support designers' ideation. However, the lack of transparency in their generation process and the shallow nature of their generated solutions constrain their performance in complex conceptual design tasks. In this study, we first introduce a conceptual design generation approach that combines generative models with classic design theory. This approach decomposes the conceptual design task based on design process and design attributes, and uses the Who, What, Where, When, Why, How (5W1H) method, Function-Behavior-Structure (FBS) model, and Kansei Engineering to guide generative models to generate conceptual design solutions through multi-step reasoning. Then we present an interactive system using a mind-map layout to visualize multi-step reasoning, called DesignFusion. This empowers designers to track the generation process and control inputs/outputs at each reasoning step. Two user studies show that our approach significantly enhances the quality of generated design solutions and enriches designer experience in human-AI co-creation.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140999356","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}
Waleed Gowharji, Jeremy J. Michalek, Kate Whitefoot
Consumer choice models used in optimal product design typically ignore potential context effects by assuming the utility of each product is independent of the attributes of other products in the choice set. We characterize implications of context effects for profit-optimal designs by deriving the first-order conditions of the design problem under alternative utility formulations, and we propose a utility function that incorporates context effects and has well-defined optimal design solutions for all products in the choice set. We then conduct a discrete choice survey experiment of automobile options and find statistically significant context-effect parameters and superior out-of-sample prediction when context-effect parameters are used in both logit and mixed logit models. These results suggest that context effects can be important in engineering design contexts and have the potential to affect optimal design differentiation.
{"title":"Implications of Context Effects in Consumer Utility Models for Optimal Product Design and Differentiation","authors":"Waleed Gowharji, Jeremy J. Michalek, Kate Whitefoot","doi":"10.1115/1.4065478","DOIUrl":"https://doi.org/10.1115/1.4065478","url":null,"abstract":"\u0000 Consumer choice models used in optimal product design typically ignore potential context effects by assuming the utility of each product is independent of the attributes of other products in the choice set. We characterize implications of context effects for profit-optimal designs by deriving the first-order conditions of the design problem under alternative utility formulations, and we propose a utility function that incorporates context effects and has well-defined optimal design solutions for all products in the choice set. We then conduct a discrete choice survey experiment of automobile options and find statistically significant context-effect parameters and superior out-of-sample prediction when context-effect parameters are used in both logit and mixed logit models. These results suggest that context effects can be important in engineering design contexts and have the potential to affect optimal design differentiation.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141003055","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}
Decoupled compliant parallel mechanisms with isotropic legs possess many excellent performances, including ease of actuation, control, manufacture and mathematical analysis, as well as effective error compensation. Despite the advent of numerous isotropic compliant parallel mechanisms, their synthesis process predominantly relies on the empirical knowledge of engineers, with an absence of dedicated synthesis methodologies. This paper proposes the Constraint Algebra Method, a novel synthesis method capable of autonomously exploring feasible constraint space for the synthesis. This method involves algebraic formulation of the constraints for the compliant modules, followed by solving constraint equations to find the feasible constraints and orientations, thereby facilitating the synthesis with intended performance characteristics. The multiplicity of solutions to the constraint equations enables the generation of diverse designs, including innovative configurations that are challenging to obtain via other methods and experience. Furthermore, by employing the constraint equations to define the positional space, the optimal configuration can be selected for simplified manufacture. A design case has been monolithically prototyped and experimentally tested. The proposed methodology holds promise for potential extension to the synthesis of other types of compliant mechanisms.
{"title":"A Synthesis Approach of XYZ Compliant Parallel Mechanisms towards Motion Decoupling with Isotropic Property and Simplified Manufacturing","authors":"Chuyang Leng, Guangbo Hao, Xiaoze Ren, Changsheng Wang, Yanming Li, Yuanzhao Zhang, Haiyang Li","doi":"10.1115/1.4065460","DOIUrl":"https://doi.org/10.1115/1.4065460","url":null,"abstract":"\u0000 Decoupled compliant parallel mechanisms with isotropic legs possess many excellent performances, including ease of actuation, control, manufacture and mathematical analysis, as well as effective error compensation. Despite the advent of numerous isotropic compliant parallel mechanisms, their synthesis process predominantly relies on the empirical knowledge of engineers, with an absence of dedicated synthesis methodologies. This paper proposes the Constraint Algebra Method, a novel synthesis method capable of autonomously exploring feasible constraint space for the synthesis. This method involves algebraic formulation of the constraints for the compliant modules, followed by solving constraint equations to find the feasible constraints and orientations, thereby facilitating the synthesis with intended performance characteristics. The multiplicity of solutions to the constraint equations enables the generation of diverse designs, including innovative configurations that are challenging to obtain via other methods and experience. Furthermore, by employing the constraint equations to define the positional space, the optimal configuration can be selected for simplified manufacture. A design case has been monolithically prototyped and experimentally tested. The proposed methodology holds promise for potential extension to the synthesis of other types of compliant mechanisms.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141017050","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 study presents a novel framework for the optimal design of compliant mechanisms, specifically addressing the structural drawbacks of conventional single-point or de-facto hinges. The hinges often lead to structural instability and stress concentration while deriving maximum motion. To overcome these issues, we introduce a new method that can design stable and elastic domains connected by either revolute or prismatic joints. The new method, called Sequential Analysis based on Reaction Force (SARF), can successfully eliminate weak hinge points while optimizing joint locations. The efficiency of developed methodology is validated through several numerical examples, yielding compliant mechanisms with suppressed hinges.
{"title":"Topology design of compliant mechanism design with multiple component modeling connected by various joints","authors":"Jun Hwan Kim, Gilho Yoon","doi":"10.1115/1.4065459","DOIUrl":"https://doi.org/10.1115/1.4065459","url":null,"abstract":"\u0000 This study presents a novel framework for the optimal design of compliant mechanisms, specifically addressing the structural drawbacks of conventional single-point or de-facto hinges. The hinges often lead to structural instability and stress concentration while deriving maximum motion. To overcome these issues, we introduce a new method that can design stable and elastic domains connected by either revolute or prismatic joints. The new method, called Sequential Analysis based on Reaction Force (SARF), can successfully eliminate weak hinge points while optimizing joint locations. The efficiency of developed methodology is validated through several numerical examples, yielding compliant mechanisms with suppressed hinges.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141017044","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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