Economic parameter design for ultra-fast laser micro-drilling process

Abstract

The basic requirement in this type of micro-drilling process is to achieve high product quality with the minimum machining cost, which can be realised through parameter design. In this paper, we propose a new economic parameter design under the framework of Bayesian modelling and optimisation. First of all, the Bayesian seemingly unrelated regression (SUR) models are utilised to develop the relationship models between input factors and output responses in the laser micro-drilling process. After that, simulated response values which reflect the real laser micro-drilling process are obtained by using the Gibbs sampling procedure. Moreover, a novel rejection cost function and a quality loss function are constructed based on the simulated responses. Finally, an optimisation scheme integrating the rejection cost (i.e. rework cost and scrap cost) function and the quality loss function is implemented by using multi-objective genetic algorithm to find feasible economic parameter settings for laser micro-drilling process.

KEYWORDS:

• Quality engineering
• quality loss function
• Bayesian methods
• robust design
• economic parameter design
• rejection cost function

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 71771121], [grant number 71371099], [grant number 71871119], [grant number 71471088]; the Hong Kong RGC General Research Funds [grant number 16201718], [grant number 16203917]; the Fundamental Research Fund for the Central Universities [grant number 3091511102]; and Natural Sciences and Engineering Research Council of Canada (NSERC) [grant number RGPIN-2018-03862].