Last modified: 2019-04-26
Abstract
Purpose – is to develop the Bayesian method of optimal engineering design by a series of experiments, aiming to manage experimental resources in a rational economic way.
Research methodology – is based on modelling of experimental data by Gaussian random fields (GRF) and using matrices of fractional Euclidean distances. Next, the P-algorithm for the planning of the experiment series is created in order to optimize the values of the response surface.
Findings – the application of the developed method in engineering design enable us to create plans for the experiment series in order to create new functional products and processes managing experimental resources in a rational economic way.
Research limitations – the creation of the plans of the experiment series can require a large amount of computer time related to the application of the Monte Carlo procedure in order to ensure the optimality of created plans. However, this limitation can be avoided using distributed computing tools.
Practical implications – The created method helps engineers to seek solutions to experimental problems, considering the economic viability of each potential solution along with the technical aspects.
Originality/Value – in creating functional products and processes engineers are using the experimental design process, which usually is highly iterative. The developed approach enables us to design the experimental series inflexible way, decreasing the number of required experiments and avoiding of rather expensive methods such as factorial experiments, steepest descent, etc., usually applied for experimental design in engineering practice.
DOI: https://doi.org/10.3846/cibmee.2019.012