Prediction of Crack for Drilling Process on Alumina Using Neural Network and Taguchi Method

Abstract

This study analyzes a variety of significant drilling conditions on aluminum oxide (withL18orthogonal array) using a diamond drill. The drilling parameters evaluated are spindle speed, feed rate, depth of cut, and diamond abrasive size. An orthogonal array, signal-to-noise (S/N) ratio, and analysis of variance (ANOVA) are employed to analyze the effects of these drilling parameters. The results were confirmed by experiments, which indicated that the selected drilling parameters effectively reduce the crack. The neural network is applied to establish a model based on the relationship between input parameters (spindle speed, feed rate, depth of cut, and diamond abrasive size) and output parameter (cracking area percentage). The neural network can predict individual crack in terms of input parameters, which provides faster and more automated model synthesis. Accurate prediction of crack ensures that poor drilling parameters are not suitable for machining products, avoiding the fabrication of poor-quality products. Confirmation experiments showed that neural network precisely predicted the cracking area percentage in drilling of alumina.

Fatigue crack growth prediction of 7075 aluminum alloy based on the GMSVR model optimized by the artificial bee colony algorithm

“…In recent years, with the development of artificial intelligence technology, some intelligent methods were applied to predict the FCG, such as the neural network (Zhi et al , 2014; Luo and Cui, 2012; Shi et al , 2015; Lee, 2015) and the grey model (Ni, 2013; Song et al , 2012; Zhaoyang et al , 2015). But the neural network, on the one hand, needs abundant samples and, on the other hand, easy to fall into the “local extremum”.…”