Artificial neural networks back propagation algorithm for cutting force components predictions

Issam Hanafi,Abdellatif Khamlichi,Francisco Mata Cabrera,Ignacio Garrido,José Tejero Manzanares

doi:10.1051/meca/2013084

Abstract

Reinforced Poly Ether Ether Ketone with 30% of Carbon Fiber (PEEK CF30) offer several thermo-mechanical advantages over standard materials and alloys which make them better candidates in different applications. However, the hard and abrasive nature of the reinforcement fiber is responsible for rapid tool wear and high machining costs. It is very important to find highly effective ways to machine that material. Accordingly, it is important to predict forces when machining fiber matrix composites because this will help to choose perfect tools for machining and ultimately save both money and time. In this study, Artificial Neural Network (ANN) was applied to predict the cutting force components in turning operations of PEEK CF30 using TiN coated cutting tools under dry conditions where the machining parameters are cutting speed ranges, feed rate, and depth of cut. For this study, the experiments have been conducted using full factorial design experiments (DOEs) on CNC turning machine. The results indicated that the well-trained (ANN) model could be able to predict the cutting force components in turning of Carbon Fiber Reinforcement Polymer (CFRP) composites. Complementary results that were not used during derivation of the ANN model have enabled one to assess the validity of the obtained predictions.

Highlights

The results indicate that the built Artificial Neural Network (ANN) model succeeded in predicting satisfactorily the cutting force components F p, F c and F a as the obtained predictions are very close to experimental observations
The parameters considered for the experimentation were cutting speed ranges, feed rate, and depth of cut
The neural network based models have shown close matching between predicted outputs and directly measured cutting force components. Validity of these ANN models was further assessed by considering additional complementary tests

Summary

Introduction

Tool (K20), a cubic boron nitride (CBN) and a polycrystalline diamond (PCD) using Response Surface Methodology (RSM). The presented model accounts for the particle fracture and particle contribution to the friction force generated along the chip tool interface, The results show acceptable agreement between the theoretically predicted and experimentally measured cutting forces [11]. Tsao and Hocheng [13] have established the relationship between the input parameters feed rate, spindle speed, and drill diameter; with the output parameters thrust force and surface roughness in drilling composite laminates They have used radial basis function network for the prediction. Mishra et al [14] have predicted the tensile strength of the unidirectional glass fiber reinforced polymer composites They have considered the input parameters as feed rate, drill point geometry and spindle speed. The experimental and predicted results have a close relationship with each other, which indicates that the neural network can be applied for the prediction of cutting force components in turning of CFRP composites

Experimental work

Derivation of ANN based models

Results and discussion

Conclusion