Surface Roughness Prediction and Optimization in the Orthogonal Cutting of Graphite/Polymer Composites Based on Artificial Neural Network

Zhenping Wan,Zhiqing Zhang,Xiaofang Huang,Qingda Guo,Dayong Yang

doi:10.3390/pr9101858

Abstract

Graphite/polymer composites are brittle materials that are prone to producing cracks and concavities on machined surfaces, and their surface quality shows greater randomness. This work aims to overcome the large fluctuations in the machined surface quality of graphite/polymer composites, realize the prediction of machined surface roughness under different machining conditions and optimize the process parameters. A graphite/polymer composite material was cut orthogonally using different machining parameters, and the machined surface roughness of the cut samples was measured by a noncontact surface profiler to obtain training samples for Artificial Neural Network (ANN). In this study, a trained radial basis function neural network was used to predict the machined surface roughness, and the prediction accuracy was more than 93%. A Genetic Algorithm (GA) was used to optimize the established ANN, and then grey relational analysis was used to compare the accuracy of the GA optimization results. The ANN prediction after GA optimization showed that the lowest machined surface roughness of the graphite/polymer composites was 1.81 μm, and the corresponding optimal cutting speed, cutting depth, tool rake angle, and rounded edge radius were 11.2 m/min, 0.1 mm, 6.85°, and 11.16 μm, respectively. A verification experiment showed that the lowest machined surface roughness was obtained when the above process parameters were selected, which was only 1.95 μm, and the prediction error of the ANN was approximately 7%. The combination of a GA and an ANN can accurately predict the surface roughness of graphite/polymer composite materials and optimize the process parameters.

Highlights

Graphite and its composite materials have been widely used in industrial applications such as biomedical implants [1], fuel cell bipolar plates [2], EDM electrodes [3], semiconductor fixtures, diesel engine [4,5,6], and mechanical seals [7]
Yang et al [12] studied the machinability of graphite/polymer composites by orthogonal cutting, and their experimental results showed that the surface roughness of the machined surface fluctuated greatly, with a fluctuation range of 25%
To accurately predict the machined surface roughness of high-purity graphite and obtain a better machined surface quality, Yang et al [13] obtained the milling parameters that minimized the machined surface roughness when a Grey Relational Analysis (GRA) was used to optimize the milling process parameters, and a prediction model for the machined surface roughness of high-purity graphite was established by a regression analysis method based on the experimental data [14]

Summary

Introduction

Graphite and its composite materials have been widely used in industrial applications such as biomedical implants [1], fuel cell bipolar plates [2], EDM electrodes [3], semiconductor fixtures, diesel engine [4,5,6], and mechanical seals [7]. Yang et al [12] studied the machinability of graphite/polymer composites by orthogonal cutting, and their experimental results showed that the surface roughness of the machined surface fluctuated greatly, with a fluctuation range of 25%. Such a large fluctuation range of roughness is highly unsuitable for the selection of process parameters during an actual machining process. An ANN prediction model for the machined surface roughness of graphite/polymer composites was established based on the results of orthogonal cutting experiments. TToo eennssuurerethteheacaccucraucryacoyf tohfe tmheeamsuereamsuernetmreesunlttsr,e5suwlotsrk, p5iewceosrkpieces werwTeheemrreeamfochareci,hn7ien5derdofuofgorhreneaeacschshdggarrtoaouupppo,i,naantsndwdeeaerceahcmwheoawrskuoprreikedpcfeioewrceeaascwrhaagnsrdorouampn,ldyaonmmdetlahyseumnretehdae1sau5vrteeirmdage1se. times Theorfeaflol rme,ea7s5urreomugenhtndeastsa dwaatsatapkoeinnatss wtheermeamcheianseudrseudrfafocer reoaucghhgnerossufpor, aeancdh tghroeunpt.he average of all measurement data was taken as the machined surface roughness for each group

Machined Surface Roughness at Different Cutting Parameters

The Structure of the ANN

ANN Training

Optimization Model

Solving Process

Comparative Analysis and Experimental Verification

Verification Experiment

Findings

Conclusions