Modeling a dynamic design system using the Mahalanobis Taguchi system — two steps optimal based neural network

Abstract

This work presents a novel algorithm, the MTS-TSO based Neural Network (NN) algorithm, which combines the Mahalanobis Taguchi System (MTS) with the Two-Step Optimal (TSO) method for parameter selections which are adjusted under a dynamic environment for product parameter design. The utility of the algorithm is assessed in two dimensions: the MTS shows how individual product parameter dimensions are selected; and, the TSO-NN links parameters selection decisions across two different times and it can be used to focus on dynamic system design (DSD) and to identify product architecture dimensions that are critical for a dynamic design system strategy. The MTS which can easily solve product parameter design problems and shows it’s computationally efficient in the previous works. Additionally, the TSO algorithm is a simple and efficient means of constructing a dynamic design system, which is verified by the neural network algorithm from this work, and the neural network algorithm from this work, and the neural network is already successfully applied in dynamic system of the past studies. Based on the main aims and verifies of this work, we conclude that the MTS-TSO based neural network algorithm can be applied successfully to dynamic environments for solving product design problems.