The Taguchi robust parameter design has been widely used over the past decade to solve many single-response process parameter designs. However, the Taguchi method is unable to deal with multi-response problems that are of main interest today, owing to increasing complexity of manufacturing processes and products. Several recent studies have been conducted in order to solve this problem. But, they did not effectively treat situations where responses are correlated and situations in which control factors have continuous values. This study proposed an integrated model for experimental design of processes with multiple correlated responses, composed of three stages which (1) use expert system, designed for selecting an inner and an outer orthogonal array, to design an actual experiment, (2) use Taguchi's quality loss function to present relative significance of responses, and multivariate statistical methods to uncorrelate and synthesise responses into a single performance measure, (3) use neural networks to construct the response function model and genetic algorithms to optimise parameter design. The effectiveness of the proposed model is illustrated with three examples. Results of analysis showed that the proposed approach could yield a better solution in terms of the optimal parameters setting that results in a higher process performance measure than the traditional experimental design.

An integrated approach to optimise parameter design of multi-response processes based on Taguchi method and artificial intelligence

A novel approach for optimization of correlated multiple responses based on desirability function and fuzzy logics

This paper presents a comparative study for optimizing the thermal parameters of the reflow soldering process using traditional and artificial intelligence (AI) approaches. High yields in the reflow soldering process are essential to a profitable printed circuit board (PCB) assembly operation. A reflow thermal profile is a time-temperature graph which is used to properly control the thermal mass and heat distribution to form robust solder joints between the PCB and electronics components during reflow soldering. An inhomogeneous temperature distribution for a reflow thermal profile can cause various soldering defects, which can jeopardize product reliability and lead to significant productivity loss. In the multi-objective optimization problem, three alternative optimization methods are discussed and compared: response surface methodology (RSM), nonlinear programming (NLP), and a hybrid AI technique. A dataset was gathered using a 3^8^-^4 experimental design for the development of meta-models through response surface quadratic modeling. In the first method, RSM is used to acquire the optimal heating parameters, while in the second method NLP is used to derive a global solution based on the meta-models. The back-propagation neural network (BPN) is used in the third method to formulate the nonlinear relationship between the heating inputs and responses. A genetic algorithm (GA) is then used to elicit the optimal heating parameters from the established BPN model. The evaluation results show that all three methods provide satisfactory soldering performance in terms of the process capability, sigma level, and process window indices (PWIs). Particularly, the hybrid AI approach provides superior nonlinear formulation capability and optimization performance.

Thermal parameters optimization of a reflow soldering profile in printed circuit board assembly: A comparative study

Applying soft computing techniques to optimise a dental milling process

In this paper, a new method is proposed to optimize a multi-response optimization problem based on the Taguchi method for the processes where controllable factors are the smaller-the-better (STB)-type variables and the analyzer desires to find an optimal solution with smaller amount of controllable factors. In such processes, the overall output quality of the product should be maximized while the usage of the process inputs, the controllable factors, should be minimized. Since all possible combinations of factors’ levels, are not considered in the Taguchi method, the response values of the possible unpracticed treatments are estimated using the artificial neural network (ANN). The neural network is tuned by the central composite design (CCD) and the genetic algorithm (GA). Then data envelopment analysis (DEA) is applied for determining the efficiency of each treatment. Although the important issue for implementation of DEA is its philosophy, which is maximization of outputs versus minimization of inputs, this important issue has been neglected in previous similar studies in multi-response problems. Finally, the most efficient treatment is determined using the maximin weight model approach. The performance of the proposed method is verified in a plastic molding process. Moreover a sensitivity analysis has been done by an efficiency estimator neural network. The results show efficiency of the proposed approach.

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A neuro-data envelopment analysis approach for optimization of uncorrelated multiple response problems with smaller the better type controllable factors

Engineers have widely applied the Taguchi method, a traditional approach for robust experimental design, to a variety of quality engineering problems for enhancing system robustness. However, the Taguchi method is unable to deal with dynamic multiresponse owing to increasing complexity of the product or design process. Although several alternative approaches have been presented to resolve this problem, they cannot effectively treat situations in which the control factors have continuous values. This study incorporates desirability functions into a hybrid neural network/genetic algorithm approach to optimize the parameter design of dynamic multiresponse with continuous values of parameters. The objective is to find the optimal combination of control factors to simultaneously maximize robustness of each response. The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.

Hsu-Hwa Chang

Papers

Neuro-genetic approach to optimize parameter design of dynamic multiresponse experiments