Using an Interval Type-2 Fuzzy Neural Network and Tool Chips for Flank Wear Prediction

Cheng-Jian Lin,Shao-Hsien Chen,Jyun-Yu Jhang,Kuu-Young Young

doi:10.1109/access.2020.3006849

Abstract

The precision of part machining is influenced by the tool life. Tools gradually wear out during the cutting process, which reduces the machining accuracy. Many studies have used machining parameters and sensor signals to predict flank wear; however, these methods have many limitations related to sensor installation, which is not only time-consuming and costly but also impractical in industry. This paper proposes an interval type-2 fuzzy neural network (IT2FNN) based on the dynamic-group cooperative differential evolution algorithm for flank wear prediction. Moreover, the Taguchi method is used to design cutting experiments for collecting experimental data and reducing the number of experiments. The CIE-xy color chromaticity values, spindle speed, feed per tooth, cutting depth, and cutting time are used as inputs of the IT2FNN, and the output is the flank wear value. The experimental results indicate that the proposed method can effectively predict flank wear with higher efficiency than other algorithms.

Highlights

Tool life prediction is one of the most important research areas in the machinery industry
Bar-Hen and Etsion [2] studied the effect of the coating thickness and substrate roughness on the flank wear
The detailed operation of each layer of the interval type-2 fuzzy neural network (IT2FNN) is described in the following text

Summary

INTRODUCTION

Tool life prediction is one of the most important research areas in the machinery industry. Lin et al.: Using an Interval Type-2 Fuzzy Neural Network and Tool Chips for Flank Wear Prediction sensor. Mikołajczyk et al [6] presented a model for the automatic prediction of tool life in turning operations In this model, cutting-edge wear parameters obtained through image processing are used as inputs for an artificial neural network. The proposed method is named the dynamic-group cooperative DE (DGCDE) algorithm and is used to update the parameters of an interval type-2 fuzzy neural network (IT2FNN). In traditional flank wear prediction methods, machining parameters, cutting forces, current values, and vibration signals are used as inputs. In this study, the chip color was used as an input parameter for obtaining accurate flank wear prediction results. A flank wear prediction model was established and its accuracy was verified through experimental results

FLANK WEAR PREDICTION FRAMEWORK

R1 G1 B1 R1G1

PARAMETER LEARNING BASED ON THE DGCDE ALGORITHM

EXPERIMENTAL RESULTS

CONCLUSION