Optimization of multi-response dynamic systems integrating multiple regression and Taguchi’s dynamic signal-to-noise ratio concept

Abstract

The principal difference between a dynamic and a static system is that the former includes a signal factor for expressing the intended output while the later does not. Assuming a linear association exists between the response and signal variables, Taguchi offered a two-stage route for optimizing a dynamic system: maximize the dynamic signal-to noise ratio (DSN) and then, change the gradient to the desired gradient by a suitable modification parameter. Some researchers have indicated limitations to Taguchi's DSN analysis, and advocated alternative approaches for optimization of a dynamic system. However, the Taguchi method as well as these alternative approaches is useful for optimizing a single-response dynamic system only. In realism, the majority of the contemporary manufacturing practices encompass numerous response variables as well as industries demand for developing procedures for optimizing multi-response dynamic system. This paper proposes a novel procedure that integrates multiple regression (MR) technique and Taguchi's DSN concept to optimize the multi-response dynamic system. In this method, appropriate multiple regression equations according to a chosen model for dynamic system are fitted first based on the observed experimental data and then DSN (called MRDSN) for different response variables are computed using the MRbased predicted values. Finally, weighted MRDSN is considered as the objective function for the optimization. The proposed procedure is investigated with respect to three modelling approaches for the dynamic systems. The results of analysis reveal that the proposed procedure with response modelling approach results in the best optimization performance. It also results in better optimization performance than back-propagation neural network-based approach and data mining-based approach reported by the past researchers.

Robust parameter design and economical multi-objective optimization on characterizing rubber for shoe soles

“…Pal & Gauri [17] proposed the use of multiple regression and the Taguchi SNR applied to the RPD for dynamic characteristics. Fang et al [18] applied optimization techniques for multi-objective DRP in the analysis of fatigue in truck cabins.…”

“…From the previous model, the models for the mean in eq are defined. (17) and the variance in eq. (18).…”

Use of the stochastic frontier and the grey relational analysis in robust design of multi-objective problems

“…The study can determine the optimal levels of the controllable design parameters in a continuous space and allows for increasing robustness in the dynamic Taguchi method. Pal and Gauri (2017) develope a procedure, which uses the multi-regression (MR) technique and the Taguchi dynamic S/N ratio (DSN) to optimize the multi-response dynamic systems. First, the multiple regression equations for the dynamic system are fitted based on the sample data, then, the dynamic S/N ratio (known as MRDSN) for the different response variables is calculated based on the multiple regression values.…”

“…The results have shown that the MRWSN method is superior to the CDF method in terms of optimization performance. Pal and Gauri (2018) revise the MRDSN method (Pal and Gauri, 2017) by introducing a new performance metric called weighted predicted response-based S/N ratio (WPRSNR). The experimental findings have shown that the modified MRDSN method outperforms the MRDSN procedure in searching the optimal design solution with respect to the total S/N and the MSE of the individual responses.…”

A Study of the Impacts of Measurement errors on Robust Parameter Design for Multi-response

“…Hsieh et al [16] applied artificial neural network to the problem of multi-response optimization in RPD. Pal and Gauri [17] presented using multiple regression and the Taguchi signal-to-noise ratio to robust parameter design the event of dynamic characteristics. Fang et al [18] studied the analysis of fatigue in truck cabins with respect to multi-response optimization based robust parameter design.…”