Shivakoti Ishwer, Rodrigues Lewlyn L R, Cep Robert, Pradhan Premendra Mani, Sharma Ashis, Kumar Bhoi Akash
Department of Mechanical Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majhitar 737136, India.
Department of Humanities and Management, Manipal Institute of Technology, Manipal Academy of Higher Education, Mangalore 576104, India.
Materials (Basel). 2020 Jul 14;13(14):3137. doi: 10.3390/ma13143137.
This research presents the parametric effect of machining control variables while turning EN31 alloy steel with a Chemical Vapor deposited (CVD) Ti(C,N) + AlO + TiN coated carbide tool insert. Three machining parameters with four levels considered in this research are feed, revolutions per minute (RPM), and depth of cut (a). The influences of those three factors on material removal rate (MRR), surface roughness (Ra), and cutting force (Fc) were of specific interest in this research. The results showed that turning control variables has a substantial influence on the process responses. Furthermore, the paper demonstrates an adaptive neuro fuzzy inference system (ANFIS) model to predict the process response at various parametric combinations. It was observed that the ANFIS model used for prediction was accurate in predicting the process response at varying parametric combinations. The proposed model presents correlation coefficients of 0.99, 0.98, and 0.964 for MRR, Ra, and Fc, respectively.
本研究展示了使用化学气相沉积（CVD）Ti（C，N）+AlO+TiN涂层硬质合金刀具车削EN31合金钢时加工控制变量的参数效应。本研究考虑的具有四个水平的三个加工参数为进给量、每分钟转数（RPM）和切削深度（a）。这三个因素对材料去除率（MRR）、表面粗糙度（Ra）和切削力（Fc）的影响是本研究特别关注的。结果表明，车削控制变量对过程响应有重大影响。此外，本文展示了一种自适应神经模糊推理系统（ANFIS）模型，用于预测各种参数组合下的过程响应。据观察，用于预测的ANFIS模型在预测不同参数组合下的过程响应时是准确的。所提出的模型对于MRR、Ra和Fc的相关系数分别为0.99、0.98和0.964。
