Zhuocheng Wang, Cuimei Bo, Zheng Sun, Jun Li, F. Gao
{"title":"基于梯度增强Kriging模型的注射成型质量缺陷分析","authors":"Zhuocheng Wang, Cuimei Bo, Zheng Sun, Jun Li, F. Gao","doi":"10.1109/IAI55780.2022.9976740","DOIUrl":null,"url":null,"abstract":"In plastic injection molding (PIM), the process parameters affect the quality and productivity of molded parts. In this paper, we use orthogonal experiment design, numerical simulation, and metamodeling method to analyze the quality defect of process. The orthogonal experiment is to generate sampling points from the design space at different parameter levels and to determine key factors that affect product quality. For the sampling points, the numerical simulation is implemented to calculate the objective responses. Based on the sampling points and their corresponding response, a gradient enhanced Kriging (GEK) surrogate model strategy is applied to construct the response predictors to calculate the objective responses in the global design space. Last, we can analyze the surrogate model to look for available process parameters to improve product quality and production efficiency.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quality defect analysis of injection molding based on gradient enhanced Kriging model\",\"authors\":\"Zhuocheng Wang, Cuimei Bo, Zheng Sun, Jun Li, F. Gao\",\"doi\":\"10.1109/IAI55780.2022.9976740\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In plastic injection molding (PIM), the process parameters affect the quality and productivity of molded parts. In this paper, we use orthogonal experiment design, numerical simulation, and metamodeling method to analyze the quality defect of process. The orthogonal experiment is to generate sampling points from the design space at different parameter levels and to determine key factors that affect product quality. For the sampling points, the numerical simulation is implemented to calculate the objective responses. Based on the sampling points and their corresponding response, a gradient enhanced Kriging (GEK) surrogate model strategy is applied to construct the response predictors to calculate the objective responses in the global design space. Last, we can analyze the surrogate model to look for available process parameters to improve product quality and production efficiency.\",\"PeriodicalId\":138951,\"journal\":{\"name\":\"2022 4th International Conference on Industrial Artificial Intelligence (IAI)\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 4th International Conference on Industrial Artificial Intelligence (IAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IAI55780.2022.9976740\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IAI55780.2022.9976740","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Quality defect analysis of injection molding based on gradient enhanced Kriging model
In plastic injection molding (PIM), the process parameters affect the quality and productivity of molded parts. In this paper, we use orthogonal experiment design, numerical simulation, and metamodeling method to analyze the quality defect of process. The orthogonal experiment is to generate sampling points from the design space at different parameter levels and to determine key factors that affect product quality. For the sampling points, the numerical simulation is implemented to calculate the objective responses. Based on the sampling points and their corresponding response, a gradient enhanced Kriging (GEK) surrogate model strategy is applied to construct the response predictors to calculate the objective responses in the global design space. Last, we can analyze the surrogate model to look for available process parameters to improve product quality and production efficiency.