I. Escamilla, P. Perez, L. Torres, Patricia Zambrano
{"title":"1018和4140钢表面粗糙度智能预测优化系统的开发与应用","authors":"I. Escamilla, P. Perez, L. Torres, Patricia Zambrano","doi":"10.1109/CERMA.2008.68","DOIUrl":null,"url":null,"abstract":"The aim of this research is to present a new methodology for predicting and optimizing the surface roughness during machining of 1018 and 4140 Steel. There is particular interest in finding the best machining value parameters that should be used to achieve good surface roughness. These parameter values can be found by this neural intelligent approach. This methodology analyzes and identifies the parameters involved in the machining process; with this information the model is able to predict the surface roughness value in different conditions and then optimize the results with different intelligent heuristics. The experimental results show that we may conclude that this intelligent system is a suitable methodology for predicting and optimizing surface roughness during the machining of 1018 and 4140 Steel.","PeriodicalId":126172,"journal":{"name":"2008 Electronics, Robotics and Automotive Mechanics Conference (CERMA '08)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Development and Application of an Intelligent System to Predict and Optimize the Surface Roughness of 1018 and 4140 Steel\",\"authors\":\"I. Escamilla, P. Perez, L. Torres, Patricia Zambrano\",\"doi\":\"10.1109/CERMA.2008.68\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The aim of this research is to present a new methodology for predicting and optimizing the surface roughness during machining of 1018 and 4140 Steel. There is particular interest in finding the best machining value parameters that should be used to achieve good surface roughness. These parameter values can be found by this neural intelligent approach. This methodology analyzes and identifies the parameters involved in the machining process; with this information the model is able to predict the surface roughness value in different conditions and then optimize the results with different intelligent heuristics. The experimental results show that we may conclude that this intelligent system is a suitable methodology for predicting and optimizing surface roughness during the machining of 1018 and 4140 Steel.\",\"PeriodicalId\":126172,\"journal\":{\"name\":\"2008 Electronics, Robotics and Automotive Mechanics Conference (CERMA '08)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 Electronics, Robotics and Automotive Mechanics Conference (CERMA '08)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CERMA.2008.68\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Electronics, Robotics and Automotive Mechanics Conference (CERMA '08)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CERMA.2008.68","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Development and Application of an Intelligent System to Predict and Optimize the Surface Roughness of 1018 and 4140 Steel
The aim of this research is to present a new methodology for predicting and optimizing the surface roughness during machining of 1018 and 4140 Steel. There is particular interest in finding the best machining value parameters that should be used to achieve good surface roughness. These parameter values can be found by this neural intelligent approach. This methodology analyzes and identifies the parameters involved in the machining process; with this information the model is able to predict the surface roughness value in different conditions and then optimize the results with different intelligent heuristics. The experimental results show that we may conclude that this intelligent system is a suitable methodology for predicting and optimizing surface roughness during the machining of 1018 and 4140 Steel.