{"title":"协调臂的刚柔耦合动力学分析及新型定向子区间不确定性分析方法的应用","authors":"Xuan Gao, Longmiao Chen, Jingsong Tang","doi":"10.3390/aerospace11060419","DOIUrl":null,"url":null,"abstract":"Cartridge delivery systems are commonly employed in aerospace engineering for the transportation of cylindrical projectiles. The coordination mechanism plays a pivotal role in ensuring reliable cartridge conveying, with its positioning accuracy being of utmost importance. However, accurately depicting the nonlinear relationship between input parameters and output response is challenging due to the involvement of numerous complex, uncertain factors during the movement process of the coordination mechanism. To address this issue, this study proposes a dynamics model that incorporates hinged gaps to represent rigid–flexible coupling within the coordination mechanism. Experimental validation confirms its effectiveness, while computational efficiency is enhanced through the utilization of a deep learning neural network surrogate model. Furthermore, an improved method for the uncertainty analysis of directional subintervals is introduced and applied to analyze uncertainty in coordination mechanisms, yielding results that demonstrate superior efficiency compared to other approaches.","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Rigid–Flexible Coupling Dynamics Analysis of Coordination Arm and Application of a New Directional Subinterval Uncertainty Analysis Method\",\"authors\":\"Xuan Gao, Longmiao Chen, Jingsong Tang\",\"doi\":\"10.3390/aerospace11060419\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cartridge delivery systems are commonly employed in aerospace engineering for the transportation of cylindrical projectiles. The coordination mechanism plays a pivotal role in ensuring reliable cartridge conveying, with its positioning accuracy being of utmost importance. However, accurately depicting the nonlinear relationship between input parameters and output response is challenging due to the involvement of numerous complex, uncertain factors during the movement process of the coordination mechanism. To address this issue, this study proposes a dynamics model that incorporates hinged gaps to represent rigid–flexible coupling within the coordination mechanism. Experimental validation confirms its effectiveness, while computational efficiency is enhanced through the utilization of a deep learning neural network surrogate model. Furthermore, an improved method for the uncertainty analysis of directional subintervals is introduced and applied to analyze uncertainty in coordination mechanisms, yielding results that demonstrate superior efficiency compared to other approaches.\",\"PeriodicalId\":2,\"journal\":{\"name\":\"ACS Applied Bio Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Bio Materials\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.3390/aerospace11060419\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/aerospace11060419","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
Rigid–Flexible Coupling Dynamics Analysis of Coordination Arm and Application of a New Directional Subinterval Uncertainty Analysis Method
Cartridge delivery systems are commonly employed in aerospace engineering for the transportation of cylindrical projectiles. The coordination mechanism plays a pivotal role in ensuring reliable cartridge conveying, with its positioning accuracy being of utmost importance. However, accurately depicting the nonlinear relationship between input parameters and output response is challenging due to the involvement of numerous complex, uncertain factors during the movement process of the coordination mechanism. To address this issue, this study proposes a dynamics model that incorporates hinged gaps to represent rigid–flexible coupling within the coordination mechanism. Experimental validation confirms its effectiveness, while computational efficiency is enhanced through the utilization of a deep learning neural network surrogate model. Furthermore, an improved method for the uncertainty analysis of directional subintervals is introduced and applied to analyze uncertainty in coordination mechanisms, yielding results that demonstrate superior efficiency compared to other approaches.