基于机器学习的数控机床自动诊断模型生成方法

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2020-09-01 DOI:10.1145/3324884.3418915

K. Masalimov

{"title":"基于机器学习的数控机床自动诊断模型生成方法","authors":"K. Masalimov","doi":"10.1145/3324884.3418915","DOIUrl":null,"url":null,"abstract":"This article presents a description of a system for the automatic generation of predictive diagnostic models of CNC machine tools. This system allows machine tool maintenance specialists to select and operate models based on LSTM neural networks to determine the state of elements of CNC machines. Examples of changes in the accuracy of the models used during operation are given to determine the state of the cutting tool (more than 95%) and the bearings of electric motors (more than 91%).","PeriodicalId":106337,"journal":{"name":"2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A Machine Learning based Approach to Autogenerate Diagnostic Models for CNC machines\",\"authors\":\"K. Masalimov\",\"doi\":\"10.1145/3324884.3418915\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article presents a description of a system for the automatic generation of predictive diagnostic models of CNC machine tools. This system allows machine tool maintenance specialists to select and operate models based on LSTM neural networks to determine the state of elements of CNC machines. Examples of changes in the accuracy of the models used during operation are given to determine the state of the cutting tool (more than 95%) and the bearings of electric motors (more than 91%).\",\"PeriodicalId\":106337,\"journal\":{\"name\":\"2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3324884.3418915\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3324884.3418915","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 2

摘要

本文介绍了一种用于数控机床预测诊断模型自动生成的系统。该系统允许机床维护专家基于LSTM神经网络选择和操作模型，以确定数控机床元件的状态。给出了在操作过程中使用的模型精度变化的例子，以确定刀具的状态(超过95%)和电动机的轴承(超过91%)。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A Machine Learning based Approach to Autogenerate Diagnostic Models for CNC machines

This article presents a description of a system for the automatic generation of predictive diagnostic models of CNC machine tools. This system allows machine tool maintenance specialists to select and operate models based on LSTM neural networks to determine the state of elements of CNC machines. Examples of changes in the accuracy of the models used during operation are given to determine the state of the cutting tool (more than 95%) and the bearings of electric motors (more than 91%).

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)

自引率

0.00%

发文量