{"title":"数控钻孔混合自适应控制提高刀具寿命和表面质量","authors":"J Susai Mary, M A Sai Balaji, D Dinakaran","doi":"10.1784/insi.2023.65.10.570","DOIUrl":null,"url":null,"abstract":"Intelligent machining requires the online adaptation of the machining parameters to improve tool life and product quality and to reduce machining costs. This article presents a novel hybrid adaptive control (HAC) system for a drilling process. The HAC system is a combination of two adaptive controls: geometric adaptive control (GAC) and adaptive control by optimisation (ACO). It keeps the roughness of the holes within tolerance without compromising tool life. A response surface model (RSM) is used for modelling the drill wear and surface roughness with speed, feed, acceleration and force signals as inputs. The model predicts the wear and roughness with prediction accuracies of 97.1% and 93.6%, respectively. The roughness control is achieved through a Massachusetts Institute of Technology rule and tool wear is minimised by genetic algorithm optimisation. The adaptive algorithms are simulated and validated for the machining conditions given by the adaptive algorithms. The results show an improved tool life of 7% and surface roughness of 11%.","PeriodicalId":13956,"journal":{"name":"Insight","volume":"207 1","pages":"0"},"PeriodicalIF":1.0000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hybrid adaptive control of CNC drilling for enhancement of tool life and surface quality\",\"authors\":\"J Susai Mary, M A Sai Balaji, D Dinakaran\",\"doi\":\"10.1784/insi.2023.65.10.570\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Intelligent machining requires the online adaptation of the machining parameters to improve tool life and product quality and to reduce machining costs. This article presents a novel hybrid adaptive control (HAC) system for a drilling process. The HAC system is a combination of two adaptive controls: geometric adaptive control (GAC) and adaptive control by optimisation (ACO). It keeps the roughness of the holes within tolerance without compromising tool life. A response surface model (RSM) is used for modelling the drill wear and surface roughness with speed, feed, acceleration and force signals as inputs. The model predicts the wear and roughness with prediction accuracies of 97.1% and 93.6%, respectively. The roughness control is achieved through a Massachusetts Institute of Technology rule and tool wear is minimised by genetic algorithm optimisation. The adaptive algorithms are simulated and validated for the machining conditions given by the adaptive algorithms. The results show an improved tool life of 7% and surface roughness of 11%.\",\"PeriodicalId\":13956,\"journal\":{\"name\":\"Insight\",\"volume\":\"207 1\",\"pages\":\"0\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2023-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Insight\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1784/insi.2023.65.10.570\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"INSTRUMENTS & INSTRUMENTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Insight","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1784/insi.2023.65.10.570","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
Hybrid adaptive control of CNC drilling for enhancement of tool life and surface quality
Intelligent machining requires the online adaptation of the machining parameters to improve tool life and product quality and to reduce machining costs. This article presents a novel hybrid adaptive control (HAC) system for a drilling process. The HAC system is a combination of two adaptive controls: geometric adaptive control (GAC) and adaptive control by optimisation (ACO). It keeps the roughness of the holes within tolerance without compromising tool life. A response surface model (RSM) is used for modelling the drill wear and surface roughness with speed, feed, acceleration and force signals as inputs. The model predicts the wear and roughness with prediction accuracies of 97.1% and 93.6%, respectively. The roughness control is achieved through a Massachusetts Institute of Technology rule and tool wear is minimised by genetic algorithm optimisation. The adaptive algorithms are simulated and validated for the machining conditions given by the adaptive algorithms. The results show an improved tool life of 7% and surface roughness of 11%.
期刊介绍:
Official Journal of The British Institute of Non-Destructive Testing - includes original research and devlopment papers, technical and scientific reviews and case studies in the fields of NDT and CM.