{"title":"Review of path planning of welding robot based on spline curve","authors":"Guan He, H. H. Teo, L. K. Moey","doi":"10.15282/jmes.18.1.2024.10.0785","DOIUrl":null,"url":null,"abstract":"This paper assesses the efficacy of intelligent path planning for welding robots utilizing splines. Traditional path planning methods can result in inefficient and inaccurate welding operations. The study reviews current research and case studies to appraise the practical application of spline-based path planning across diverse industrial scenarios. It underscores the benefits of discovering the shortest path and reducing cycle time while acknowledging challenges such as calibration accuracy and sensitivity to sensor data noise. The introduction of artificial intelligence algorithms in automobile welding path planning enables a more precise replication of the body's design curve, ensuring the continuity and smoothness of the welding process. This, in turn, fosters further automation and optimization of the automotive welding manufacturing process. The current research concentrates on integrating intelligent optimization algorithms and spline curves to provide an efficient and intelligent method for welding path planning. Intelligent path planning based on spline curves demonstrates significant potential in enhancing welding efficiency, determining the shortest path, and holds promising applications in the broader research field of welding path planning.","PeriodicalId":16166,"journal":{"name":"Journal of Mechanical Engineering and Sciences","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Mechanical Engineering and Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15282/jmes.18.1.2024.10.0785","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
Abstract
This paper assesses the efficacy of intelligent path planning for welding robots utilizing splines. Traditional path planning methods can result in inefficient and inaccurate welding operations. The study reviews current research and case studies to appraise the practical application of spline-based path planning across diverse industrial scenarios. It underscores the benefits of discovering the shortest path and reducing cycle time while acknowledging challenges such as calibration accuracy and sensitivity to sensor data noise. The introduction of artificial intelligence algorithms in automobile welding path planning enables a more precise replication of the body's design curve, ensuring the continuity and smoothness of the welding process. This, in turn, fosters further automation and optimization of the automotive welding manufacturing process. The current research concentrates on integrating intelligent optimization algorithms and spline curves to provide an efficient and intelligent method for welding path planning. Intelligent path planning based on spline curves demonstrates significant potential in enhancing welding efficiency, determining the shortest path, and holds promising applications in the broader research field of welding path planning.
期刊介绍:
The Journal of Mechanical Engineering & Sciences "JMES" (ISSN (Print): 2289-4659; e-ISSN: 2231-8380) is an open access peer-review journal (Indexed by Emerging Source Citation Index (ESCI), WOS; SCOPUS Index (Elsevier); EBSCOhost; Index Copernicus; Ulrichsweb, DOAJ, Google Scholar) which publishes original and review articles that advance the understanding of both the fundamentals of engineering science and its application to the solution of challenges and problems in mechanical engineering systems, machines and components. It is particularly concerned with the demonstration of engineering science solutions to specific industrial problems. Original contributions providing insight into the use of analytical, computational modeling, structural mechanics, metal forming, behavior and application of advanced materials, impact mechanics, strain localization and other effects of nonlinearity, fluid mechanics, robotics, tribology, thermodynamics, and materials processing generally from the core of the journal contents are encouraged. Only original, innovative and novel papers will be considered for publication in the JMES. The authors are required to confirm that their paper has not been submitted to any other journal in English or any other language. The JMES welcome contributions from all who wishes to report on new developments and latest findings in mechanical engineering.