Title
A Process Parameters Selection Approach For Trade-Off Between Energy Consumption And Polishing Quality
Abstract
Increasing energy consumption and consequent environmental issues are receiving more and more attention in manufacturing industry. It is well known that operational parameters selection in the machining process plays an important role in improving energy efficiency. In order to reduce energy cost and simultaneously ensure production performance, this paper provides a new approach to the selection of operational process parameters for multi-objective optimisation. The research is focused on the porcelain tile polishing with the chip formation energy and surface quality considered as the optimisation objectives. The four key operational parameters considered in the optimisation model are rotational speed of the polishing head, forward speed of tile, frequency of lateral oscillation and polishing head pressure. Furthermore, chip formation energy and surface quality are defined as the functions of the above operational parameters based on the kinematic equation of polishing machine. Then, a conceptual framework based on hierarchic genetic algorithm is applied to find out the optimum combination for the trade-off between chip formation energy and surface quality. Finally, a case study shows that the proposed approach can determine the Pareto front of polishing parameters for the trade-off and parameters selection has significant influences on energy efficiency of porcelain tile polishing.
Year
DOI
Venue
2018
10.1080/0951192X.2017.1407875
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
Keywords
Field
DocType
Energy efficiency, process parameters selection, hierarchic genetic algorithm (HGA), polishing process
Process engineering,Polishing,Kinematics equations,Efficient energy use,Chip formation,Manufacturing engineering,Rotational speed,Engineering,Tile,Energy consumption,Pressure head
Journal
Volume
Issue
ISSN
31
4-5
0951-192X
Citations 
PageRank 
References 
1
0.34
1
Authors
5
Name
Order
Citations
PageRank
Haidong Yang16212.34
Hongcheng Li2293.76
Chengjiu Zhu311.69
Hua Fang434332.48
Jun Li510.34