{"title":"Detailed design and analysis for additive manufacturing of topologically optimised and generatively designed Ti-6Al-4V Hip Joint Implant","authors":"Abhishek Kishor, Ramesh Gupta Burela, Ankit Gupta","doi":"10.1615/intjmultcompeng.2023050152","DOIUrl":null,"url":null,"abstract":"In this paper, a comprehensive investigation for the design and analysis of Ti-6Al-4V hip joint implants using generative design and topology optimisation, along with Laser Powder Bed Fusion (LPBF) additive manufacturing, has been presented. The study employed the NSGA-II genetic algorithm for generative design, enabling the generation of diverse optimised designs and topology optimisation with the SIMP approach, efficiently reducing implant mass of the design space by up to 75% while maintaining structural integrity. Finite Element Analysis revealed comparable levels of von Misses stress and deformation between geometries obtained with generative design and topology optimisation. However, the combined approach exhibited superior performance, namely topology optimisation followed by generative design, with a 40% reduction in deformation and a 15% reduction in von Misses stress compared to conventional models. LPBF simulations demonstrated the superiority of the optimised geometries, with a 30% reduction in thermal stress and a 66% reduction in deformation compared to conventional designs. It is observed that design input for generative design has a significant impact on the output design. Also, geometry has a notable impact on the quality of the printed part.","PeriodicalId":50350,"journal":{"name":"International Journal for Multiscale Computational Engineering","volume":"5 1","pages":""},"PeriodicalIF":1.4000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal for Multiscale Computational Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1615/intjmultcompeng.2023050152","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, a comprehensive investigation for the design and analysis of Ti-6Al-4V hip joint implants using generative design and topology optimisation, along with Laser Powder Bed Fusion (LPBF) additive manufacturing, has been presented. The study employed the NSGA-II genetic algorithm for generative design, enabling the generation of diverse optimised designs and topology optimisation with the SIMP approach, efficiently reducing implant mass of the design space by up to 75% while maintaining structural integrity. Finite Element Analysis revealed comparable levels of von Misses stress and deformation between geometries obtained with generative design and topology optimisation. However, the combined approach exhibited superior performance, namely topology optimisation followed by generative design, with a 40% reduction in deformation and a 15% reduction in von Misses stress compared to conventional models. LPBF simulations demonstrated the superiority of the optimised geometries, with a 30% reduction in thermal stress and a 66% reduction in deformation compared to conventional designs. It is observed that design input for generative design has a significant impact on the output design. Also, geometry has a notable impact on the quality of the printed part.
期刊介绍:
The aim of the journal is to advance the research and practice in diverse areas of Multiscale Computational Science and Engineering. The journal will publish original papers and educational articles of general value to the field that will bridge the gap between modeling, simulation and design of products based on multiscale principles. The scope of the journal includes papers concerned with bridging of physical scales, ranging from the atomic level to full scale products and problems involving multiple physical processes interacting at multiple spatial and temporal scales. The emerging areas of computational nanotechnology and computational biotechnology and computational energy sciences are of particular interest to the journal. The journal is intended to be of interest and use to researchers and practitioners in academic, governmental and industrial communities.