This research presents the parametric effect of machining control variables while turning EN31 alloy steel with a Chemical Vapor deposited (CVD) Ti(C,N) + Al O + 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.
展开▼
机译：本研究介绍了加工控制变量的参数效应，同时用化学气相沉积（CVD）Ti（C，N）+ Al O +锡涂覆的硬质合金工具插入物。本研究中考虑的三个级别的三个加工参数是饲料，每分钟转速（RPM）和切割深度（A）。这三种因素对材料去除率（MRR），表面粗糙度（RA）和切割力（FC）的影响对该研究具有特异性。结果表明，转向控制变量对过程响应具有重大影响。此外，本文演示了一种自适应神经模糊推理系统（ANFIS）模型，以预测各种参数组合的处理响应。观察到，用于预测的ANFI模型在改变参数组合时预测过程响应是准确的。所提出的模型分别为MRR，RA和FC呈现0.99,0.98和0.964的相关系数。
展开▼
