{"title":"基于人工神经网络的钻井过程监控微处理器装置的实现","authors":"Karavaev Yury, Klekovkin Anton, Bezák Pavol","doi":"10.1109/PC.2013.6581402","DOIUrl":null,"url":null,"abstract":"This paper deals with research of implementation of artificial neural networks for machining processes monitoring. A microprocessor device, neural network algorithm and program for it were developed. Different neural networks parameters were simulate, and on the example of the real drilling process the artificial neural network was trained to recognize three possible cases: normal drilling process, drilling bit wear, and drilling bit breakage. The results of experiments are described.","PeriodicalId":232418,"journal":{"name":"2013 International Conference on Process Control (PC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"The implementation of microprocessor device for drilling process monitoring based on artificial neural network\",\"authors\":\"Karavaev Yury, Klekovkin Anton, Bezák Pavol\",\"doi\":\"10.1109/PC.2013.6581402\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper deals with research of implementation of artificial neural networks for machining processes monitoring. A microprocessor device, neural network algorithm and program for it were developed. Different neural networks parameters were simulate, and on the example of the real drilling process the artificial neural network was trained to recognize three possible cases: normal drilling process, drilling bit wear, and drilling bit breakage. The results of experiments are described.\",\"PeriodicalId\":232418,\"journal\":{\"name\":\"2013 International Conference on Process Control (PC)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 International Conference on Process Control (PC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PC.2013.6581402\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Process Control (PC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PC.2013.6581402","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The implementation of microprocessor device for drilling process monitoring based on artificial neural network
This paper deals with research of implementation of artificial neural networks for machining processes monitoring. A microprocessor device, neural network algorithm and program for it were developed. Different neural networks parameters were simulate, and on the example of the real drilling process the artificial neural network was trained to recognize three possible cases: normal drilling process, drilling bit wear, and drilling bit breakage. The results of experiments are described.