A data mining approach to dynamic multiple responses in Taguchi experimental design

Optimization of multi-response dynamic systems using multiple regression-based weighted signal-to-noise ratio

“…Realizing the need of the modern industries, some authors took research interest in developing appropriate procedure for optimizing multi-response dynamic systems. Different researchers have advocated different approaches for modelling the multiple responses but most of them (Tong et al, 2001;Hsieh et al, 2004;Chang, 2006;Chang, 2008;Wu, 2009;Chang and Chen, 2011) have used CDF as the performance metric for optimization of the multi-response dynamic systems. Tong et al (2004), Wang and Tong (2004), Wu and Yeh (2005), Wang (2007) and Gauri (2014) have used different performance metrics.…”

“…Industry has increasingly emphasized developing procedures capable of simultaneously optimizing the multi-response dynamic systems in light of the increasing complexity of modern product design. To cope with the need of the modern industries, several studies (Tong et al, 2001;Tong et al, 2004;Hsieh et al, 2005;Wu & Yeh, 2005;Chang, 2006;Wang, 2007;Chang, 2008;Tong et al, 2008;Wu, 2009;Chang & Chen, 2011;Gauri, 2014) have proposed different procedures for optimizing a multi-response dynamic system. The goal of optimizing a multi-response dynamic system is to find a setting combination of control factors (controllable variables) that would result in the optimum values of all response variables at all signal levels.…”

“…Tong et al (2001), Hsieh et al (2005), and Wu (2009) have modelled the response variables using response surface methodology (RSM) and then determined the optimal settings of the control factors by maximizing an overall performance measure (OPI), which is essentially the CDF. On the other hand, Chang (2006), Chang (2008) and Chang and Chen (2011) used artificial neural networks (ANN) for modelling the response functions and then obtained the optimal settings of the control factors by considering OPI, which is essentially CDF, as the performance metric.…”

Optimization of Multi-response Problems with Continuous Functional Responses by considering dispersion effects

“…Chang [22] also used ANN to build the dynamic response model and proposed modified desirability functions for optimizing phase. Chang [23] proposed a data mining approach which uses artificial neural networks, desirability functions, and simulated annealing algorithm to optimize problems with dynamic multiple responses. Chang and Chen [24] used a genetic algorithm to optimize a dynamic multi-response model which was created using ANN.…”

Quality Improvement using Data Mining in Manufacturing Processes

“…Some researchers combined data mining and traditional statistical methods and applied to quality improvement. Examples are the use of MSPC (multivariate statistical control charts) and neural networks in detergent-making company (Seyed Taghi Akhavan Niaki, 2005;Tai-Yue Wang, 2002), the combination of automated decision system and six sigma in the General Electric financial Assurance businesses (Angie Patterson, 2005), the combined used of decision tree and SPC with data from Holmes and Mergen (Ruey-Shiang Guh, 2008), the use of SVR (support vector regression) and control charts (Ben Khediri ISSam, 2008), the use of ANN (artificial neural network), SA ( simulated annealing) and Taguchi experiment design (Hsu-Hwa Chang, 2008). Giovanni C Porzio(2003) has presented a methods for visually mining off-line data with combination of ANN and T 2 control chart and to identify the assignable variation automatically.…”