Artificial neural network based approach for dynamic parameter design

The Taguchi parameter design method has been recognized as an important tool for improving the quality of a product or a process. However, the statistical methods and optimization procedures proposed by Taguchi have much room for improvement. For instance, the two-step procedure proposed by Taguchi may fail to identify an optimum design condition if an adjustment parameter does not exist, the optimal setting of a design parameter is determined only among the levels included in the parameter design experiment, and, for the dynamic parameter design, the signal parameter is assumed to follow a uniform rather than a general distribution. This paper develops an artificial neural network based dynamic parameter design approach to overcome the shortcomings of the Taguchi and existing alternative approaches. First, an artificial neural network is trained to map the relationship between the characteristic, design, noise and signal parameters. Second, Latin hypercube samples of the signal and noise parameters are obtained and used to estimate the slope between the signal parameter and characteristic as well as the variance of the characteristic at each set of design parameter settings. Then, the dynamic parameter design problem is formulated as a nonlinear optimization problem and solved to find the optimal settings of the design parameters using sequential quadratic programming. The effectiveness of the proposed approach is illustrated with an example.     

Optimization of user experience in interaction design through a Taguchi‐based hybrid approach

User experience (UX), which encompasses all aspects of a user’s interaction with an interactive product, has been recognized as central to interaction design. This paper proposes a Taguchi‐based hybrid approach to realize the optimal UX design. In this approach, design analysis is first used to identify design patterns and UX characteristics. According to the results, a Taguchi experiment is conducted and the signal‐to‐noise ratios are computed. Subsequently, the preference weights for the UX characteristics are obtained by using analytical hierarchy process‐based group decision making. A multiperformance characteristic index is then defined based on the gray relational grade (GRG) obtained through gray relational analysis. On the basis of the GRG, the optimal design can be obtained. A mobile health application design was used to demonstrate the proposed approach. The results show that this approach can effectively enhance UX quality and be used as a universal design approach for optimizing UX.     

Application of entropy measurement technique in grey based Taguchi method for solution of correlated multiple response optimization problems: A case study in welding

In the present work, an attempt has been made to apply an efficient technique, in order to solve correlated multiple response optimization problems, in the field of submerged arc welding. The traditional grey based Taguchi approach has been extended to tackle correlated multi-objective optimization problems. The Taguchi optimization technique is based on the assumption that the quality indices (i.e. responses) are independent or uncorrelated. But, in practical cases, the assumption may not be valid always. However, the common trend in the solution of multi-objective optimization problems is to initially convert these multi-objectives into an equivalent single objective function. While deriving this equivalent objective function, different priority weights are assigned to different responses, according to their relative importance. But, there is no specific guideline for assigning these response weights. In this context, the present study aims to apply the entropy measurement technique in order to calculate the relative response weights from the analysis of entropy of the entire process. Principal Component Analysis (PCA) has been adopted to eliminate correlation that exists among the responses and to convert correlated responses into uncorrelated and independent quality indices, called principal components. These have been accumulated to calculate the overall grey relational grade, using the theory of grey relational analysis. Finally, the grey based Taguchi method has been used to derive an optimal process environment capable of producing the desired weld quality. The previously mentioned method has been applied to optimize bead geometry parameters of submerged arc bead-on-plate weldment. The paper highlights a detailed methodology of the proposed technique and its effectiveness.     

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

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.     

Dimensional quality optimisation of high-speed CNC milling process with dynamic quality characteristic

High-speed CNC milling is becoming an indispensable process for manufacturing industry. To increase the process versatility, flexibility, and robustness, Taguchi dynamic approach coupled with a proposed ideal function model is employed to develop an optimal high-speed CNC milling process with high-dimensional quality. Additionally, a test sample with three typical geometries including square, circle, and triangle essentially representing the geometrical variations in the mould and die products was designed for use throughout the experiments.

Experimental results show that the dimensional accuracy of the machined product has been greatly improved by the optimal conditions obtained by Taguchi dynamic approach. Moreover, the product can be easily machined to the desired dimension simply by adjusting the programmed dimension according to the mathematical equation by linear regression analysis of confirmed results.     

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

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 automated method for the preparation of orthogonal arrays for use in Taguchi designed experiments

In the last several years, the use of designed experiments in manufacturing and engineering design environments has become increasingly popular through the introduction of the ideas of Dr. G. Taguchi. The Taguchi Method, a systematic technique for experimental design and analysis, employs team oriented solutions to analyze design and production problems and their causes. The Taguchi Method as provided a simplified approach to the design of statistically significant experiments. This has greatly increased the number of experimental design practitioners.

This paper presents a computer program that addresses the need for an automated system able to design the experimental matrices for the orthogonal arrays that are required by Taguchi's Method. The program was written tode sign simple arrays as well as complex multi-level arrays with two-way interactions.     

Improving the lighting performance of a 3535 packaged hi-power LED using genetic programming,quality loss functions and particle swarm optimization

The lighting performance of a 3535 packaged hi-power LED (light-emitting diode) is mainly influenced by its geometric design and the refractive properties of its materials. In the past, engineers often determined the settings of the geometric parameters and selected the refractive properties of the materials through a trial-and-error process based on the principles of optics and their own experience. This procedure was costly and time-consuming, and its use did not ensure that the settings of the design parameters were optimal. Therefore, this study proposed a hybrid approach based on genetic programming (GP), Taguchi quality loss functions, and particle swarm optimization (PSO) to solve the multi-response parameter design problems. The feasibility and effectiveness of the proposed approach was demonstrated by a case study on improving the lighting performance of an LED. The confirmation results showed that all of the key quality characteristics of an LED fulfill the required specifications, and the comparison found that the proposed hybrid approach outperforms the traditional Taguchi method in solving this multi-response parameter design problem. The proposed hybrid approach can be extended to solve parameter design problems with multiple responses in various application fields.     

Optimization of user experience in mobile application design by using a fuzzy analytic-network-process-based Taguchi method

Mobile application (app) design is an expanding research area, with user experience (UX) as its core. UX encompasses all aspects of human–computer interaction, and thus the optimization of UX has multiple objectives. Quality characteristics related to UX are subjective and even subconscious; moreover, there exists interdependence among UX quality characteristics. However, very little attention has been focused on these issues when optimizing UX based on multiple objectives. In this paper, a fuzzy analytic network process (ANP)-based Taguchi method is proposed for optimizing UX in mobile app design. First, design patterns and UX quality characteristics are determined. Subsequently, a Taguchi experiment is designed and carried out, and then signal-to-noise (S/N) ratios are calculated. A fuzzy ANP is adopted to derive the preference weights for the UX quality characteristics. Based on these weights, the S/N ratios are converted into a multiperformance characteristic index (MPCI) by using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Finally, according to the MPCI, the significant design patterns are identified by using the analysis of variance, and the optimal design is obtained by using the response table and response graph. A mobile health app design was presented to illustrate the proposed approach. The results suggest that the proposed approach can effectively manage the interdependence among the subjective and even subconscious UX quality characteristics in the optimization process, and be used as a universal robust design approach to optimize UX in mobile app design.     

Parameter optimization of continuous sputtering process based on Taguchi methods,neural networks,desirability function,and genetic algorithms

To combat climate change, many industries have participated in the research on alternative energies. Industrial Technology Research Institute in Taiwan has developed techniques for the solar energy selective absorption film continuous sputtering process. For this extremely complicated process, plenty of parameters would influence the output quality. If parameters settings simply rely on the experience of engineers, the defect rate may increase due to instability. A more reliable approach is desirable to optimize the condition of manufacturing process parameters, thus improving the quality.The present study applies a systematic procedure for the parameter optimization of the absorption film continuous sputtering process. First, possible variables are determined based on collected data and engineering knowledge. Second, Taguchi methods are utilized to search for the significant factors and the optimal level combination of parameters. Finally, the integration of back-propagation neural network, desirability function, and genetic algorithms is used to obtain the optimal parameters setting. According to the experiment results, the performance of the integrated procedure is better than that of Taguchi methods and traditional approach. Furthermore, if applying the integrated method, the saving energy would achieve 9770.53 kiloliter of oil equivalent (kLOE) per year, which is 11.2 times the saving kLOE of the traditional paint process.     

基于田口法的产品质量工程优化系统开发

产品质量工程是提高产品质量与可靠性、降低研制成本、缩短研制周期的重要途径，是目前研究的热点，田口法是它的核心技术；该文简要介绍了田口法的核心理论一在线与脱线质量工程理论，质量损失模型和分析工具(信噪比和正交试验设计)；基于田口法，以JAVA编程语言为工具，对田口法的脱线质量工程进行了可视化应用软件系统的开发；本文介绍了该软件系统的作用、特点及运行过程，并对软件的系统功能模块和总体设计作了详细说明，最后指出了本软件的开发方向。     

An integrated neuro-genetic approach incorporating the Taguchi method for product design

Product design is a multidisciplinary activity that requires the integration of concurrent engineering approaches into a design process that secures competitive advantages in product quality. In concurrent engineering, the Taguchi method has demonstrated an efficient design approach for product quality improvement. However, the Taguchi method intuitively uses parameters and levels in measuring the optimum combination of design parameter values, which might not guarantee that the final solution is the most optimal. This work proposes an integrated procedure that involves neural network training and genetic algorithm simulation within the Taguchi quality design process to aid in searching for the optimum solution with more precise design parameter values for improving the product development. The concept of fractals in computer graphics is also considered in the generation of product form alternatives to demonstrate its application in product design. The stages in the general approach of the proposed procedures include: (1) use of the Taguchi experimental design procedure, (2) analysis of the neural network and genetic algorithm process, and (3) generation of design alternatives. An electric fan design is used as an example to describe the development and explore the applicability of the proposed procedures. The results indicate that the proposed procedures could enhance the efficiency of product design efforts by approximately 7.8%. It is also expected that the proposed design procedure will provide designers with a more effective approach to product development.     

Solving the multi-response problem in Taguchi method by benevolent formulation in DEA

The Taguchi method is an efficient approach for optimizing a single quality response. In practice, however, most products/processes have more than one quality response of main interest. Recently, the multi-response problem in the Taguchi method has gained a considerable research attention. This research, therefore, proposes an efficient approach for solving the multi-response problem in the Taguchi method utilizing benevolent formulation in data envelopment analysis (DEA). Each experiment in Taguchi’s orthogonal array (OA) is treated as a decision making unit (DMU) with multiple responses set inputs and/or outputs. Each DMU is evaluated by benevolent formulation. The ordinal value of the DUM’s efficiency is then used to decide the optimal factor levels for multi-response problem. Three frequently-investigated case studies are adopted to illustrate the proposed approach. The computational results showed that the proposed approach provides the largest total anticipated improvement among principal component analysis (PCA), DEA based ranking approach (DEAR) and other techniques in literature. In conclusion, the proposed approach may provide a great assistant to practitioners for solving the multi-response problem in manufacturing applications on the Taguchi method.     

Hybrid evolutionary optimization for nutraceutical manufacturing processes

In this paper, an intelligent approach, called HERON (hybrid evolutionary optimization for nutraceutical manufacturing), is proposed to optimize a variety of manufacturing processes in the nutraceutical field. The approach integrates the Taguchi method, an artificial neural network (ANN), and a genetic algorithm (GA). The Taguchi method is used to cost-effectively gather the data on the process parameters. Data obtained by the Taguchi method are divided into input and output data for an ANN’s input and output parameters, respectively. The ANN trains itself to develop the relationship between its input and output parameters. The trained ANN is then integrated into a GA as the fitness function, such that the GA can evolutionarily obtain the optimal process parameters. The HERON is validated through a manufacturing process on soft-shell turtle soft-capsules. The objective is to minimize the soft-capsule defect rate. Compared to the defect rates obtained by the empirical and Taguchi methods, the HERON reduces the defect rate by 43.75 and 32.5 %, respectively. In addition, compared to the manufacturing costs obtained by the empirical and Taguchi methods, the HERON reduces the manufacturing cost by 11.81 and 25.29 %, respectively.     

Multi-attribute decision making for green electrical discharge machining

This paper aims to develop a combination of Taguchi and fuzzy TOPSIS methods to solve multi-response parameter optimization problems in green manufacturing. Electrical Discharge Machining (EDM), a commonly used non-traditional manufacturing process was considered in this study. A decision making model for the selection of process parameters in order to achieve green EDM was developed. An experimental investigation was carried out based on Taguchi L9 orthogonal array to analyze the sensitivity of green manufacturing attributes to the variations in process parameters such as peak current, pulse duration, dielectric level and flushing pressure. Weighing factors for the output responses were determined using triangular fuzzy numbers and the most desirable factor level combinations were selected based on TOPSIS technique. The model developed in this study can be used as a systematic framework for parameter optimization in environmentally conscious manufacturing processes.     

The optimal approach for parameter settings based on adjustable contracting capacity for the hospital supply chain logistics system

This paper establishes a simulation model for the supply chain of the hospital logistic system (SCHLS) based on the dynamic Taguchi method. The model derives optimal factor level combinations in the SCHLS setting when establishing adjustable contracting capacity between SCHLS and the pharmaceutical company. To attempt the goal, this study adopts the SCHLS case in the North Alliance of the Department of Health of Taiwan. The data collection uses a simulation based on Taguchi’s L₁₈ orthogonal table for SCHLS. This work also proposes an optimal approach, including the neural network (NN) and genetic algorithm (GA), to obtain an optimal robust design in achieving optimal SCHLS multi-performance. The research results show that the predicted response of multi-performance in the optimal approach is better than that in Taguchi.     

The use of the Taguchi method with grey relational analysis to optimize the thin-film sputtering process with multiple quality characteristic in color filter manufacturing

The paper proposes an approach to improve the yield of Chrome (Cr) thin-film sputtering process of the black matrix (BM) in color filter manufacturing and to find the robust parameters of the process with multiple quality characteristics by using the Taguchi method combined with the grey relational analysis. The research considers the correlation between quality characteristics and applies the principal component analysis to eliminate the multiple co-linearity. The weights of the quality characteristics are determined by employing the entropy measurement method. Two quality characteristics and four control factors with three levels were selected. Based on the Taguchi quality design concept, a L9 orthogonal array table was chosen for the experiments. The confirmation experiment verifies the proposed grey-based Taguchi method has the ability to find out the optimal process parameters with multiple quality characteristics. Besides, manufacturing with the attained optimal process parameters can reduce the opportunities of repair and rework of Cr thin-film and raise the yield.     

A synergistic Mahalanobis–Taguchi system and support vector regression based predictive multivariate manufacturing process quality control approach

The primary objective of this study is to propose and verify a new synergistic prediction-based multivariate process quality control (MPQC) approach for manufacturing processes. The proposed approach considers the influence of covariates (e.g. uncontrollable inputs) and output (or response) uncertainties to predict, monitor, diagnose, and adjust for out-of-control scenarios. The prediction-based real-time synergistic approach integrates off-line and on-line multivariate quality control strategies. In this approach, based on a current state prediction of responses, process control variables are adjusted to prevent any out-of-control or abnormal situations in the process. The unique approach is designed based on a Mahalanobis–Taguchi System (MTS), support vector regression (SVR), bootstrap prediction interval (PI), and derivative-free Nelder-Mead (NM) optimisation strategy. Two real-life case studies demonstrate the suitability of the proposed approach and show improvements in process performance. This easy-to-implement distribution-free predictive quality control approach provides the necessary flexibility to industry practitioners for real-life implementation in discrete or continuous manufacturing processes.     

An efficient hybrid Taguchi-genetic algorithm for protein folding simulation

Given the amino-acid sequence of a protein, the prediction of a protein’s tertiary structure is known as the protein folding problem. The protein folding problem in the hydrophobic–hydrophilic lattice model is to find the lowest energy conformation. In order to enhance the performance of predicting protein structure, in this paper we propose an efficient hybrid Taguchi-genetic algorithm that combines genetic algorithm, Taguchi method, and particle swarm optimization (PSO). The GA has the capability of powerful global exploration, while the Taguchi method can exploit the optimum offspring. In addition, we present the PSO inspired by a mutation mechanism in a genetic algorithm. We demonstrate that our algorithm can be applied successfully to the protein folding problem based on the hydrophobic-hydrophilic lattice model. Simulation results indicate that our approach performs very well against existing evolutionary algorithm.     

Optimization of friction welding of tube to tube plate using an external tool

The field of materials technology has been witnessing tremendous developments. Friction welding is an important solid state joining technique. In this research study, friction welding of tube to tube plate using an external tool has been performed and the process parameters are optimized by Taguchi L₈ orthogonal array. The prioritization of the process parameters has been obtained and ANOVA has been conducted to predict the statistical significance of the process parameters. This is followed by the optimization of welding process parameters using genetic algorithm. The practical feasibility of applying Genetic Algorithm to friction welding process has been ensured by means of studying the deviation between predicted and experimentally obtained welding process parameters.