{"title":"Hybrid FE-ML model for turning of 42CrMo4 steel","authors":"","doi":"10.1016/j.cirpj.2024.10.003","DOIUrl":null,"url":null,"abstract":"<div><div>Metal cutting processes contribute significant share of the added value of industrial products. The need for machining has grown exponentially with increasing demands for quality and accuracy, and despite of more than a century of research in the field, there are no reliable and accurate models that describe all the physical phenomena needed to optimize the machining processes. The scientific community has begun to explore hybrid methods instead of expanding the capabilities of individual modelling schemes, which has been more efficient than efficacious direction. Following this trend, we propose a hybrid finite element — machine learning method (FEML) for modelling metal cutting. The advantages of the FEML method are reduced need for experimental data, reduced computational time and improved prediction accuracy. This paper describes the FEML model, which uses a Coupled Eulerian Lagrangian (CEL) formulation and deep neural networks (DNN) from the TensorFlow Python library. The machining experiments include forces, chip morphology and surface roughness. The experimental data was divided into training dataset and validation dataset to confirm the model predictions outside the experimental data range. The hybrid FEML model outperformed the DNN and FEM models independently, by reducing the computational time, improving the average prediction error from 23% to 13% and reduced the need for experimental data by half.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"CIRP Journal of Manufacturing Science and Technology","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1755581724001597","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
Abstract
Metal cutting processes contribute significant share of the added value of industrial products. The need for machining has grown exponentially with increasing demands for quality and accuracy, and despite of more than a century of research in the field, there are no reliable and accurate models that describe all the physical phenomena needed to optimize the machining processes. The scientific community has begun to explore hybrid methods instead of expanding the capabilities of individual modelling schemes, which has been more efficient than efficacious direction. Following this trend, we propose a hybrid finite element — machine learning method (FEML) for modelling metal cutting. The advantages of the FEML method are reduced need for experimental data, reduced computational time and improved prediction accuracy. This paper describes the FEML model, which uses a Coupled Eulerian Lagrangian (CEL) formulation and deep neural networks (DNN) from the TensorFlow Python library. The machining experiments include forces, chip morphology and surface roughness. The experimental data was divided into training dataset and validation dataset to confirm the model predictions outside the experimental data range. The hybrid FEML model outperformed the DNN and FEM models independently, by reducing the computational time, improving the average prediction error from 23% to 13% and reduced the need for experimental data by half.
期刊介绍:
The CIRP Journal of Manufacturing Science and Technology (CIRP-JMST) publishes fundamental papers on manufacturing processes, production equipment and automation, product design, manufacturing systems and production organisations up to the level of the production networks, including all the related technical, human and economic factors. Preference is given to contributions describing research results whose feasibility has been demonstrated either in a laboratory or in the industrial praxis. Case studies and review papers on specific issues in manufacturing science and technology are equally encouraged.