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.

Keywords:

• Energy efficiency
• process parameters selection
• hierarchic genetic algorithm (HGA)
• polishing process

Acknowledgements

The authors would like to thank the support from the National Natural Science Foundation of China (NSFC) (NO.51475096), the NSFC-GuangDong Collaborative Fund (NO.U1501248) and China Postdoctoral Science Foundation (NO.2016M602443).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (NSFC) [Grant Number: 51475096]; NSFC-Guangdong Collaborative Fund [Grant Number: U1501248]; China Postdoctoral Science Foundation[Grant Number: 2016M602443].