Title: Multi-response optimisation of cavitation peening parameters for improving fatigue performance using the desirability function approach
Authors: Mohammadsadegh Mobin; Mahmood Mobin; Zhaojun Li
Addresses: Department of Industrial Engineering and Engineering Management, Western New England University, 1215 Wilbraham Rd., Springfield, MA, 01119, USA ' Department of Mechanical Engineering, Amirkabir University of Technology, 424 Hafez Avenue, Tehran, Iran ' Department of Industrial Engineering and Engineering Management, Western New England University, 1215 Wilbraham Rd., Springfield, MA, 01119, USA
Abstract: In this study, first, full factorial design (FFD) of experiment is applied to investigate the effects of cavitation peening process parameters including standoff distance, cavitation number, nozzle size, and exposure time on the fatigue performance of carburised steel. The response variables are considered as residual stress, surface roughness, and austenitic ratio. In addition, the desirability function approach is used in order to optimise three response variables, individually and simultaneously. First, the individual desirability function is used which optimises each response variable at a time. In order to have better results, the composite desirability function method is utilised to optimise response variables simultaneously. The optimisation results provide the optimal sets of design parameters to have desired response variables. The outcomes obtained from FFD method combined with desirability function method are compared with that found in the literature which applied Taguchi method and response surface methodology.
Keywords: multi-response optimisation; desirability function; cavitation peening; fatigue strength; full factorial design; FFD; design of experiments; standoff distance; cavitation number; nozzle size; exposure time; carburised steel; residual stress; surface roughness; surface quality; austenitic ratio; Taguchi methods; response surface methodology; RSM.
International Journal of Applied Decision Sciences, 2016 Vol.9 No.2, pp.156 - 181
Available online: 02 Nov 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article