Statistical analyses and regression modeling for influence
of process parameters on material removal rate in
Gaoyan Zhong1*, Jiangyan Xu1, Yuetong Wu2 and Shoufeng Yang3
1College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China
2Southampton Management School, University of Southampton, Southampton SO17 1BJ, UK
3Faculty of Engineering and the Environment, University of Southampton, Southampton SO17 1BJ, UK
- Corresponding Author:
- Gaoyan Zhong
College of Engineering, Nanjing Agricultural University
Fax: +8625 58606580
E-mail: [email protected]
Received Date: July 16, 2015; Accepted Date: August 31, 2015; Published Date: September 10, 2015
Citation: Zhong G, Xu J, Wu Y, Yang S (2015) Statistical Analyses and Regression Modeling for Influence of Process Parameters on Material Removal Rate in Ultrasonic Machining. Global J Technol Optim 6: 187. doi:10.4172/2229-8711.1000187
Copyright: © 2015 Zhong G, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The traditional regression model in machining process neglects nonlinear influence of machining parameters on process response, which causes the analyses to have a low accuracy. The primary objective of this study is to propose an optimal regression model to analyze the material removal rate in ultrasonic machining through the experimental tests, statistical analyses and regression modeling. Three main factors affecting the machining process response, namely abrasive granularity, feed pressure and feed speed, were selected for this purpose, and the experiments were performed in accordance with an L16 orthogonal array using Taguchi method. Analysis of variance (ANOVA) was used to investigate the statistical significance of the parameters at 95% confidence level and to determine the percentage contribution of the parameters to the process response. On this basis, the optimal regression model was proposed. Compared with traditional regression model, the analytical precision of the optimal regression model is quite higher than that of traditional regression model. The results obtained from the new experimental conditions show that the optimal regression model can correctly reflect the influence of machining parameters on process response, which can provide a theoretical basis for selection of machining parameters to improve its machining efficiency.