Development of a new robust design methodology based on Bayesian perspectives

Robust design (RD), implemented in statistical and mathematical procedures to simultaneously minimise the process bias and variability, is widely used in many areas of engineering and technology to represent complex real-world industrial settings. For RD modelling and optimisation, response surface methodology (RSM) is often utilised as an estimation method to represent the functional relationship between input factors and their associated output responses. Although conventional RSM-based RD methods may offer significant advantages regarding process design, there is room for improvement. In this context, a new RD methodology is developed in this paper by integrating Bayesian principles into the RD procedure. Numerical examples and comparative studies are conducted by using two conventional RSM-based RD models and the proposed model. The results of two numerical examples demonstrate that the proposed RD method provides significantly better RD solutions in terms of the expected quality loss (EQL) than conventional methods.

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