{"title":"用IFM-GA同时优化碳纤维的分配和定向","authors":"Kenta Fukui, Ryota Nonami","doi":"10.1016/j.cjmeam.2023.100078","DOIUrl":null,"url":null,"abstract":"<div><p>This paper proposes an individual fitness method genetic algorithm (IFM-GA) for carbon fiber-reinforced plastic (CFRP). The strength of CFRP depends on the carbon fiber allocation and orientation. Waste carbon fiber is generated if this design is inappropriate. Consequently, CFRPs are less cost-effective. It is necessary to optimize the allocation and orientation as design variables to solve this problem. The problem involves combinatorial optimization. The genetic algorithm (GA) is suitable for combinatorial optimization. However, it is difficult to obtain an optimal solution using the GA owing to the large number of combinations. Hence, the IFM-GA is developed in this study. It is a GA-based method with a different fitness calculation. The GA calculates the fitness of each design, whereas the IFM-GA calculates the fitness of each design element. As a result, the IFM-GA yields a higher-stiffness design than the GA. To conclude, the IFM-GA can enable optimum fiber allocation and orientation, whereas the GA cannot.</p></div>","PeriodicalId":100243,"journal":{"name":"Chinese Journal of Mechanical Engineering: Additive Manufacturing Frontiers","volume":"2 2","pages":"Article 100078"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Simultaneous Optimization of Carbon Fiber Allocation and Orientation by IFM-GA\",\"authors\":\"Kenta Fukui, Ryota Nonami\",\"doi\":\"10.1016/j.cjmeam.2023.100078\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper proposes an individual fitness method genetic algorithm (IFM-GA) for carbon fiber-reinforced plastic (CFRP). The strength of CFRP depends on the carbon fiber allocation and orientation. Waste carbon fiber is generated if this design is inappropriate. Consequently, CFRPs are less cost-effective. It is necessary to optimize the allocation and orientation as design variables to solve this problem. The problem involves combinatorial optimization. The genetic algorithm (GA) is suitable for combinatorial optimization. However, it is difficult to obtain an optimal solution using the GA owing to the large number of combinations. Hence, the IFM-GA is developed in this study. It is a GA-based method with a different fitness calculation. The GA calculates the fitness of each design, whereas the IFM-GA calculates the fitness of each design element. As a result, the IFM-GA yields a higher-stiffness design than the GA. To conclude, the IFM-GA can enable optimum fiber allocation and orientation, whereas the GA cannot.</p></div>\",\"PeriodicalId\":100243,\"journal\":{\"name\":\"Chinese Journal of Mechanical Engineering: Additive Manufacturing Frontiers\",\"volume\":\"2 2\",\"pages\":\"Article 100078\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chinese Journal of Mechanical Engineering: Additive Manufacturing Frontiers\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S277266572300017X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chinese Journal of Mechanical Engineering: Additive Manufacturing Frontiers","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S277266572300017X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Simultaneous Optimization of Carbon Fiber Allocation and Orientation by IFM-GA
This paper proposes an individual fitness method genetic algorithm (IFM-GA) for carbon fiber-reinforced plastic (CFRP). The strength of CFRP depends on the carbon fiber allocation and orientation. Waste carbon fiber is generated if this design is inappropriate. Consequently, CFRPs are less cost-effective. It is necessary to optimize the allocation and orientation as design variables to solve this problem. The problem involves combinatorial optimization. The genetic algorithm (GA) is suitable for combinatorial optimization. However, it is difficult to obtain an optimal solution using the GA owing to the large number of combinations. Hence, the IFM-GA is developed in this study. It is a GA-based method with a different fitness calculation. The GA calculates the fitness of each design, whereas the IFM-GA calculates the fitness of each design element. As a result, the IFM-GA yields a higher-stiffness design than the GA. To conclude, the IFM-GA can enable optimum fiber allocation and orientation, whereas the GA cannot.