Abstract The development of rehabilitation robots has long been an issue of increasing interest in a wide range of fields. An important aspect of the ongoing research field is applying flexible components to rehabilitation equipment to enhance human−machine interaction. Another major challenge is to accurately estimate the individual’s intention to achieve safe operation and efficient training. In this article, a robotic knee−ankle orthosis (KAO) with shape memory alloy (SMA) actuators is developed, and the estimation method is proposed to determine the joint torque. First, based on the analysis of human lower limb structure and walking patterns, the mechanical design of the KAO that can achieve various rehabilitation training modes is detailed. Next, the dynamic model of the hybrid-driven KAO is established using the thermodynamic constitutive equation and Lagrange formalism. In addition, the joint torque estimation is realized by the nonlinear Kalman filter method. Finally, the prototype and human subject experiments are conducted, and the experimental results demonstrate that the KAO can assist lower limb movements. In the three experimental scenarios, reductions of 59.1%, 16.5%, and 73% of the torque estimation error during the knee joint movement are observed, respectively.
{"title":"Development and dynamic state estimation for robotic knee-ankle orthosis with Shape memory alloy actuators","authors":"Zhi Sun, Yuan Li, Bin Zi, Bing Chen","doi":"10.1115/1.4063565","DOIUrl":"https://doi.org/10.1115/1.4063565","url":null,"abstract":"Abstract The development of rehabilitation robots has long been an issue of increasing interest in a wide range of fields. An important aspect of the ongoing research field is applying flexible components to rehabilitation equipment to enhance human−machine interaction. Another major challenge is to accurately estimate the individual’s intention to achieve safe operation and efficient training. In this article, a robotic knee−ankle orthosis (KAO) with shape memory alloy (SMA) actuators is developed, and the estimation method is proposed to determine the joint torque. First, based on the analysis of human lower limb structure and walking patterns, the mechanical design of the KAO that can achieve various rehabilitation training modes is detailed. Next, the dynamic model of the hybrid-driven KAO is established using the thermodynamic constitutive equation and Lagrange formalism. In addition, the joint torque estimation is realized by the nonlinear Kalman filter method. Finally, the prototype and human subject experiments are conducted, and the experimental results demonstrate that the KAO can assist lower limb movements. In the three experimental scenarios, reductions of 59.1%, 16.5%, and 73% of the torque estimation error during the knee joint movement are observed, respectively.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"8 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135514273","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}
Abstract Additive manufacturing (AM) is a rapidly growing technology within the industry and education sectors. Despite this, there lacks a comprehensive tool to guide AM novices in evaluating the suitability of a given design for fabrication by the range of AM processes. Existing design for additive manufacturing (DfAM) evaluation tools tend to focus on only certain key process-dependent DfAM considerations. By contrast, the purpose of this research is to propose a tool that guides a user to comprehensively evaluate their chosen design and educates the user on an appropriate DfAM strategy. The tool incorporates both opportunistic and restrictive elements, integrates the seven major AM processes, outputs an evaluative score, and recommends processes and improvements for the input design. This paper presents a thorough framework for this evaluation tool and details the inclusion of features such as dual-DfAM consideration, process recommendations, and a weighting system for restrictive DfAM. The result is a detailed recommendation output that helps users to determine not only “Can you print your design?” but also “Should you print your design?” by combining several key research studies to build a comprehensive user design tool. This research also demonstrates the potential of the framework through a series of user-based studies, in which the opportunistic side of the tool was found to have significantly improved novice designers’ ability to evaluate designs. The preliminary framework presented in this paper establishes a foundation for future studies to refine the tool’s accuracy using more data and expert analysis.
{"title":"Creation and Assessment of a Novel Design Evaluation Tool for Additive Manufacturing","authors":"Alexander Cayley, Jayant Mathur, Nicholas Meisel","doi":"10.1115/1.4063566","DOIUrl":"https://doi.org/10.1115/1.4063566","url":null,"abstract":"Abstract Additive manufacturing (AM) is a rapidly growing technology within the industry and education sectors. Despite this, there lacks a comprehensive tool to guide AM novices in evaluating the suitability of a given design for fabrication by the range of AM processes. Existing design for additive manufacturing (DfAM) evaluation tools tend to focus on only certain key process-dependent DfAM considerations. By contrast, the purpose of this research is to propose a tool that guides a user to comprehensively evaluate their chosen design and educates the user on an appropriate DfAM strategy. The tool incorporates both opportunistic and restrictive elements, integrates the seven major AM processes, outputs an evaluative score, and recommends processes and improvements for the input design. This paper presents a thorough framework for this evaluation tool and details the inclusion of features such as dual-DfAM consideration, process recommendations, and a weighting system for restrictive DfAM. The result is a detailed recommendation output that helps users to determine not only “Can you print your design?” but also “Should you print your design?” by combining several key research studies to build a comprehensive user design tool. This research also demonstrates the potential of the framework through a series of user-based studies, in which the opportunistic side of the tool was found to have significantly improved novice designers’ ability to evaluate designs. The preliminary framework presented in this paper establishes a foundation for future studies to refine the tool’s accuracy using more data and expert analysis.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"76 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135514414","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}
Abstract The modularity of a product architecture can be measured by the characteristics of commonality and combinability. Positive and negative effects of a more communal or more combinable structure are summarized and visualized life phase by life phase in an impact model, in order to support companies in implementing a modular product architecture and to guide them in defining the modularization target. However, due to the megatrend of personalization, the solution space of a modular product architecture needs to be extended to include personalizable modules. What remains unclear is how personalization impacts the different life phases. Therefore, this article derives an impact model considering product personalization/ product individualization. First, the modularity property of personalizability is derived, in order to then specifically investigate the effects occurring in the different life phases. Therefore, a literature review is conducted. New effects are found and the existing effects of commonality and combinability are examined for their validity for personalizability. The findings are then combined with the known effects of commonality and combinability to create a holistic impact model of modular product families. This new model takes personalizable modules into account and can support companies in defining the goals and focus of a modularization project.
{"title":"Effects of Product Personalization - Considering <i>Personalizability</i> in the Product Architecture of Modular Product Families","authors":"Juliane Vogt, Lea-Nadine Woeller, Dieter Krause","doi":"10.1115/1.4063825","DOIUrl":"https://doi.org/10.1115/1.4063825","url":null,"abstract":"Abstract The modularity of a product architecture can be measured by the characteristics of commonality and combinability. Positive and negative effects of a more communal or more combinable structure are summarized and visualized life phase by life phase in an impact model, in order to support companies in implementing a modular product architecture and to guide them in defining the modularization target. However, due to the megatrend of personalization, the solution space of a modular product architecture needs to be extended to include personalizable modules. What remains unclear is how personalization impacts the different life phases. Therefore, this article derives an impact model considering product personalization/ product individualization. First, the modularity property of personalizability is derived, in order to then specifically investigate the effects occurring in the different life phases. Therefore, a literature review is conducted. New effects are found and the existing effects of commonality and combinability are examined for their validity for personalizability. The findings are then combined with the known effects of commonality and combinability to create a holistic impact model of modular product families. This new model takes personalizable modules into account and can support companies in defining the goals and focus of a modularization project.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135823945","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}
Abstract Bridging the gaps among various categories of stochastic microstructures remains a challenge in the design representation of microstructural materials. Each microstructure category requires certain unique mathematical and statistical methods to define the design space (design representation). The design representation methods are usually incompatible between two different categories of stochastic microstructures. The common practice of pre-selecting the microstructure category and the associated design representation method before conducting rigorous computational design restricts the design freedom and hinders the discovery of innovative microstructure designs. To overcome this issue, this paper proposes and compares two novel methods, the deep generative modeling-based method and the curvature functional-based method, to understand their pros and cons in designing mixed-category stochastic microstructures for desired properties. For the deep generative modeling-based method, the Variational Autoencoder is employed to generate an unstructured latent space as the design space. For the curvature functional-based method, the microstructure geometry is represented by curvature functionals, of which the functional parameters are employed as the microstructure design variables. Regressors of the microstructure design variables-property relationship are trained for microstructure design optimization. A comparative study is conducted to understand the relative merits of these two methods in terms of computational cost, continuous transition, design scalability, design diversity, dimensionality of the design space, interpretability of the statistical equivalency, and design performance.
{"title":"Designing Mixed-Category Stochastic Microstructures by Deep Generative Model-based and Curvature Functional-based Methods","authors":"Leidong Xu, Kiarash Naghavi Khanghah, Hongyi Xu","doi":"10.1115/1.4063824","DOIUrl":"https://doi.org/10.1115/1.4063824","url":null,"abstract":"Abstract Bridging the gaps among various categories of stochastic microstructures remains a challenge in the design representation of microstructural materials. Each microstructure category requires certain unique mathematical and statistical methods to define the design space (design representation). The design representation methods are usually incompatible between two different categories of stochastic microstructures. The common practice of pre-selecting the microstructure category and the associated design representation method before conducting rigorous computational design restricts the design freedom and hinders the discovery of innovative microstructure designs. To overcome this issue, this paper proposes and compares two novel methods, the deep generative modeling-based method and the curvature functional-based method, to understand their pros and cons in designing mixed-category stochastic microstructures for desired properties. For the deep generative modeling-based method, the Variational Autoencoder is employed to generate an unstructured latent space as the design space. For the curvature functional-based method, the microstructure geometry is represented by curvature functionals, of which the functional parameters are employed as the microstructure design variables. Regressors of the microstructure design variables-property relationship are trained for microstructure design optimization. A comparative study is conducted to understand the relative merits of these two methods in terms of computational cost, continuous transition, design scalability, design diversity, dimensionality of the design space, interpretability of the statistical equivalency, and design performance.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135994800","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}
Abstract Incorporating style-related objectives into shape design has been centrally important to maximize product appeal. However, algorithmic style capture and reuse have not fully benefited from automated data-driven methodologies due to the challenging nature of design describability. This paper proposes an AI-driven method to fully automate the discovery of brand-related features. First, to tackle the scarcity of vectorized product images, this research proposes two data acquisition workflows: parametric modeling from small curve-based datasets, and vectorization from large pixel-based datasets. Secondly, this study constructs BIGNet, a two-tier Brand Identification Graph Neural Network to learn from both scalar vector graphics' curve-level and chunk-level parameters. In the first case study, BIGNet not only classifies phone brands but also captures brand-related features across multiple scales, such as lens' location, as confirmed by AI evaluation. In the second study, this paper showcases the generalizability of BIGNet learning from a vectorized car image dataset and validates the consistency and robustness of its predictions given four scenarios. The results match the difference commonly observed in luxury vs. economy brands in the automobile market. Finally, this paper also visualizes the activation maps generated from a convolutional neural network and shows BIGNet's advantage of being a more explainable style-capturing agent.
{"title":"BIGNet: A Deep Learning Architecture for Brand Recognition with Geometry-based Explainability","authors":"Yu-hsuan Chen, Levent Burak Kara, Jonathan Cagan","doi":"10.1115/1.4063760","DOIUrl":"https://doi.org/10.1115/1.4063760","url":null,"abstract":"Abstract Incorporating style-related objectives into shape design has been centrally important to maximize product appeal. However, algorithmic style capture and reuse have not fully benefited from automated data-driven methodologies due to the challenging nature of design describability. This paper proposes an AI-driven method to fully automate the discovery of brand-related features. First, to tackle the scarcity of vectorized product images, this research proposes two data acquisition workflows: parametric modeling from small curve-based datasets, and vectorization from large pixel-based datasets. Secondly, this study constructs BIGNet, a two-tier Brand Identification Graph Neural Network to learn from both scalar vector graphics' curve-level and chunk-level parameters. In the first case study, BIGNet not only classifies phone brands but also captures brand-related features across multiple scales, such as lens' location, as confirmed by AI evaluation. In the second study, this paper showcases the generalizability of BIGNet learning from a vectorized car image dataset and validates the consistency and robustness of its predictions given four scenarios. The results match the difference commonly observed in luxury vs. economy brands in the automobile market. Finally, this paper also visualizes the activation maps generated from a convolutional neural network and shows BIGNet's advantage of being a more explainable style-capturing agent.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135969939","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}
Abstract This paper aims at presenting a detailed and practical comparison between three designs of robotic soft fingers for industrial grippers. While the soft finger based on the Fin Ray Effect (FRE) has been proposed for quite some time, few works in the literature have studied its reliance on the presence of the crossbeams or its precision grasp performance compared to its power grasp. Aiming at addressing these gaps, two novel designs are proposed and compared to the classic FRE fingers in this paper. First, the three designs are presented and one of the fingers, PacomeFlex, embeds changeable grasping modes by relying on two sets of kinematic structures of a single bistable stopper design. Then, finite element analyses are conducted to simulate their power and precision grasps followed by the estimation of the overall grasp forces they produce. These finite element analyses will then be used to train neural networks capable of predicting the grasp forces produced by the fingers. Finally, the grasp strength and the pullout resistance of the fingers are experimentally measured and experimental results are shown to be in good accordance with the FEA and neural network models. As will also be shown, the PacomeFlex finger introduced in this work provides a noticeably higher performance level than Festo's commercial product with respect to typical metrics in soft grasping.
{"title":"Bistable Stopper Design and Force Prediction for Precision and Power Grasps of Soft Robotic Fingers for Industrial Manipulation","authors":"Xiaowei Shan, Lionel Birglen","doi":"10.1115/1.4063763","DOIUrl":"https://doi.org/10.1115/1.4063763","url":null,"abstract":"Abstract This paper aims at presenting a detailed and practical comparison between three designs of robotic soft fingers for industrial grippers. While the soft finger based on the Fin Ray Effect (FRE) has been proposed for quite some time, few works in the literature have studied its reliance on the presence of the crossbeams or its precision grasp performance compared to its power grasp. Aiming at addressing these gaps, two novel designs are proposed and compared to the classic FRE fingers in this paper. First, the three designs are presented and one of the fingers, PacomeFlex, embeds changeable grasping modes by relying on two sets of kinematic structures of a single bistable stopper design. Then, finite element analyses are conducted to simulate their power and precision grasps followed by the estimation of the overall grasp forces they produce. These finite element analyses will then be used to train neural networks capable of predicting the grasp forces produced by the fingers. Finally, the grasp strength and the pullout resistance of the fingers are experimentally measured and experimental results are shown to be in good accordance with the FEA and neural network models. As will also be shown, the PacomeFlex finger introduced in this work provides a noticeably higher performance level than Festo's commercial product with respect to typical metrics in soft grasping.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135969952","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}
Abstract Cloud-based multi-user computer-aided design (MUCAD) tools have the potential to revolutionize design team collaboration. Previous research focusing on parametric part modeling suggests that teams collaborating through MUCAD are more efficient at producing a CAD model than individual designers. While these studies are enlightening, there is a significant gap in understanding the impact of MUCAD on assembly modeling, despite its crucial role in the design process. Part and assembly models are both defined by parametric relationships, but assembly models lack hierarchical feature dependency; we propose that by modularizing tasks and executing them in parallel, teams can optimize the assembly process in ways not possible with part modelling. Our study aims to examine and compare CAD assembly performance between individuals and virtual collaborative teams using the same cloud MUCAD platform. Through analyzing team communication, workflow, task allocation, and collaboration challenges of teams comprising 1-4 members, we identify factors that contribute to or hinder the success of multi-user CAD teams. Our results show that teams can complete an assembly in less calendar time than a single user, but single users are more efficient on a per-person basis, due to communication and coordination overheads. Notably, pairs exhibit an assembly bonus effect. These findings provide initial insights into the realm of collaborative CAD assembly work, highlighting the potential of MUCAD to enhance the capabilities of modern product design teams.
{"title":"Analysis of Collaborative Assembly in Multi-User Computer-Aided Design","authors":"Kathy Cheng, Alison Olechowski","doi":"10.1115/1.4063759","DOIUrl":"https://doi.org/10.1115/1.4063759","url":null,"abstract":"Abstract Cloud-based multi-user computer-aided design (MUCAD) tools have the potential to revolutionize design team collaboration. Previous research focusing on parametric part modeling suggests that teams collaborating through MUCAD are more efficient at producing a CAD model than individual designers. While these studies are enlightening, there is a significant gap in understanding the impact of MUCAD on assembly modeling, despite its crucial role in the design process. Part and assembly models are both defined by parametric relationships, but assembly models lack hierarchical feature dependency; we propose that by modularizing tasks and executing them in parallel, teams can optimize the assembly process in ways not possible with part modelling. Our study aims to examine and compare CAD assembly performance between individuals and virtual collaborative teams using the same cloud MUCAD platform. Through analyzing team communication, workflow, task allocation, and collaboration challenges of teams comprising 1-4 members, we identify factors that contribute to or hinder the success of multi-user CAD teams. Our results show that teams can complete an assembly in less calendar time than a single user, but single users are more efficient on a per-person basis, due to communication and coordination overheads. Notably, pairs exhibit an assembly bonus effect. These findings provide initial insights into the realm of collaborative CAD assembly work, highlighting the potential of MUCAD to enhance the capabilities of modern product design teams.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135969941","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}
Abstract The concept of integrated sustainable product design has recently emerged, aiming to incorporate downstream lifecycle performance into the initial product design to enhance sustainability. Various sustainable product design tools based on life-cycle assessment or quality function deployment have been established while the impact of reliability on circular practices has received limited attention. Recognizing the critical role of product reliability in post-design performance, this paper develops a product design optimization model that considers the warranty performance and the effect of end-of-use options. The model takes into account the effect of uncertain operating conditions on product reliability. Two optimization goals including the minimization of expected unit lifecycle cost and environmental impact are achieved by the model. To demonstrate the benefits of the integrated approach, the model is applied to an electric motor design problem. The results highlight that integrating end-of-use options in the early design phase leads to adjustments in component selection and reliability design. Moreover, the circular utilization of used products enables cost savings throughout the product's lifecycle and contributes to environmental impact reduction. Lastly, the study analyzes the effects of operating conditions, warranty policies, and take-back prices for used products on design decisions, providing valuable insights for product designers.
{"title":"Integrated Sustainable Product Design with Warranty and End-of-use Considerations","authors":"Xinyang Liu, Pingfeng Wang","doi":"10.1115/1.4063762","DOIUrl":"https://doi.org/10.1115/1.4063762","url":null,"abstract":"Abstract The concept of integrated sustainable product design has recently emerged, aiming to incorporate downstream lifecycle performance into the initial product design to enhance sustainability. Various sustainable product design tools based on life-cycle assessment or quality function deployment have been established while the impact of reliability on circular practices has received limited attention. Recognizing the critical role of product reliability in post-design performance, this paper develops a product design optimization model that considers the warranty performance and the effect of end-of-use options. The model takes into account the effect of uncertain operating conditions on product reliability. Two optimization goals including the minimization of expected unit lifecycle cost and environmental impact are achieved by the model. To demonstrate the benefits of the integrated approach, the model is applied to an electric motor design problem. The results highlight that integrating end-of-use options in the early design phase leads to adjustments in component selection and reliability design. Moreover, the circular utilization of used products enables cost savings throughout the product's lifecycle and contributes to environmental impact reduction. Lastly, the study analyzes the effects of operating conditions, warranty policies, and take-back prices for used products on design decisions, providing valuable insights for product designers.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135969943","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}
Abstract A multi-fidelity integration method is proposed to analyze the reliability of multiple performance indicators (MPI) for industrial robots. In order to high-fidelity mapping the performance of industrial robots, a unified multi-domain model (UMDM) is first established. The contribution-degree analysis is then used to classify the input random variables into interacting and non-interacting ones. Thus, the high-dimensional integration of reliability analysis is separated into a low-dimensional integration and multiple one-dimensional integrations in an additive form. Here, the low-dimensional integration consisting of the interacting variables is calculated using the high-precision mixed-degree cubature formula (MDCF), and the computational results are treated as high-fidelity data. The one-dimensional integration consisting of non-interacting variables is then computed by the highly efficient five-point Gaussian Hermite quadrature (FGHQ), and the computational results are named low-fidelity data. A multi-fidelity integration method is constructed by fusing the high-fidelity data and the low-fidelity data to obtain the statistical moments of the MPI. Subsequently, the probability density function and the failure probability of the MPI are estimated using the saddlepoint approximation method. Finally, some representative methods are performed to verify the superiority of the proposed method.
{"title":"A multi-fidelity integration method for reliability analysis of industrial robots","authors":"Jinhui Wu, Pengpeng Tian, Shunyu Wang, yourui Tao","doi":"10.1115/1.4063404","DOIUrl":"https://doi.org/10.1115/1.4063404","url":null,"abstract":"Abstract A multi-fidelity integration method is proposed to analyze the reliability of multiple performance indicators (MPI) for industrial robots. In order to high-fidelity mapping the performance of industrial robots, a unified multi-domain model (UMDM) is first established. The contribution-degree analysis is then used to classify the input random variables into interacting and non-interacting ones. Thus, the high-dimensional integration of reliability analysis is separated into a low-dimensional integration and multiple one-dimensional integrations in an additive form. Here, the low-dimensional integration consisting of the interacting variables is calculated using the high-precision mixed-degree cubature formula (MDCF), and the computational results are treated as high-fidelity data. The one-dimensional integration consisting of non-interacting variables is then computed by the highly efficient five-point Gaussian Hermite quadrature (FGHQ), and the computational results are named low-fidelity data. A multi-fidelity integration method is constructed by fusing the high-fidelity data and the low-fidelity data to obtain the statistical moments of the MPI. Subsequently, the probability density function and the failure probability of the MPI are estimated using the saddlepoint approximation method. Finally, some representative methods are performed to verify the superiority of the proposed method.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136057773","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}
Christopher Mattson, Thomas Geilman, Joshua Cook-Wright, Christopher Mabey, Eric Dahlin, John Salmon
Abstract This article introduces 55 prompt questions that can be used by design teams to consider the social impacts of the engineered products they develop. These 55 questions were developed by a team of engineers and social scientists to help design teams consider the wide range of social impacts that can result from their design decisions. After their development, these 55 questions were tested in a controlled experiment involving 12 design teams. Given a 1-h period of time, 6 control teams were asked to identify many social impacts within each of the 11 social impact categories identified by Rainock et al. (2018, The Social Impacts of Products: A Review, Impact Assess. Project Appraisal, 36, pp. 230241), while 6 treatment groups were asked to do the same while using the 55 questions as prompts to the ideation session. Considering all 1079 social impacts identified by the teams combined and using 99% confidence intervals, the analysis of the data shows that the 55 questions cause teams to more evenly identify high-quality, high-variety, high-novelty impacts across all 11 social impact categories during an ideation session, as opposed to focusing too heavily on a subset of impact categories. The questions (treatment) do this without reducing the quantity, quality, or novelty of impacts identified, compared to the control group. In addition, using a 90% confidence interval, the 55 questions cause teams to more evenly identify impacts when low quality, low variety, and low novelty are not filtered out. As a point of interest, the case where low quality and low variety impacts are removed – but low novelty impacts are not – the treatment draws the same conclusion but with only 85% confidence.
{"title":"Fifty-Five Prompt Questions for Identifying Social Impacts of Engineered Products","authors":"Christopher Mattson, Thomas Geilman, Joshua Cook-Wright, Christopher Mabey, Eric Dahlin, John Salmon","doi":"10.1115/1.4063453","DOIUrl":"https://doi.org/10.1115/1.4063453","url":null,"abstract":"Abstract This article introduces 55 prompt questions that can be used by design teams to consider the social impacts of the engineered products they develop. These 55 questions were developed by a team of engineers and social scientists to help design teams consider the wide range of social impacts that can result from their design decisions. After their development, these 55 questions were tested in a controlled experiment involving 12 design teams. Given a 1-h period of time, 6 control teams were asked to identify many social impacts within each of the 11 social impact categories identified by Rainock et al. (2018, The Social Impacts of Products: A Review, Impact Assess. Project Appraisal, 36, pp. 230241), while 6 treatment groups were asked to do the same while using the 55 questions as prompts to the ideation session. Considering all 1079 social impacts identified by the teams combined and using 99% confidence intervals, the analysis of the data shows that the 55 questions cause teams to more evenly identify high-quality, high-variety, high-novelty impacts across all 11 social impact categories during an ideation session, as opposed to focusing too heavily on a subset of impact categories. The questions (treatment) do this without reducing the quantity, quality, or novelty of impacts identified, compared to the control group. In addition, using a 90% confidence interval, the 55 questions cause teams to more evenly identify impacts when low quality, low variety, and low novelty are not filtered out. As a point of interest, the case where low quality and low variety impacts are removed – but low novelty impacts are not – the treatment draws the same conclusion but with only 85% confidence.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135043474","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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