Robust product development for multiple quality characteristics using computer experiments and an optimization technique

Many industries have employed the Taguchi method over the years to improve product and process performance. This method is powerful and effective in helping manufacturers to design their products and processes as well as to solve troublesome quality problems. Several studies have presented approaches to multiple quality characteristics. Few published articles have focused primarily on optimising the correlated multiple quality characteristics. This research presents an approach to optimising the correlated multiple quality characteristics using principal component analysis and grey relational analysis based on quality loss. As can be seen in the results, the advantage of this approach is that the chosen optimal design for multiple quality characteristics is robust for the correlated multiple quality characteristics. This method finds the optimal parameter conditions more easily, even when the average quality losses of the quality characteristics are all diverse.

Optimizing Multiple Quality Characteristics of Stereolithography Process via Taguchi Method-based Grey Analysis for SL5530 Epoxy Resin Material to Enhance Part Quality

In the area of quality control, several correlative quality characteristics may be considered simultaneously in a manufacturing process. In such a case, the Schewhart control charts neglect the influence of the correlation among these quality characteristics, leading to an incorrect judgment. Numerous methods have been proposed to control a process with multiple quality characteristics, however, most research emphasises only the variation of a process mean and devotes much less attention to the variation of a variance, relatively speaking. In this study, a neural network procedure is proposed for detecting variations in the variances, with the assumption that the mean value of the multiple quality characteristics of a process is under control. Two criteria, the average run length (ARL) and the average number of abnormal cases found, are used to evaluate the performance of the proposed procedure. The developed neural model is compared with the traditional |Σ| control method with regard to the aspect of detecting the variations in the variances in a process. The simulation results demonstrate the superiority of the proposed procedure in process control while multiple quality characteristics are simultaneously considered.

Pharmaceutical formulation design with the development of analytical technologies and methodologies is an integral part of a drug’s research and development. Conventional pharmaceutical formulation design problems are typically studied under the assumption of an unconstrained experimental region with a single quality characteristic. In real world pharmaceutical formulation design problems, however, we are often faced with multiple quality characteristics with a constrained experimental region due to physical limitations. In order to address these needs, we propose a multidisciplinary optimization process for the optimal design of pharmaceutical formulations by developing a response surface simplex design (RSSD) and a response surface D-optimal design (REDD) for experimental modeling and by developing a partitioned composite desirability function (PCDF) for multidisciplinary optimization. Two actual case studies are also presented here for model verification purposes.

Simultaneous and non-destructive prediction of multiple internal quality characteristics in mandarin citrus with near-infrared spectroscopy and ensemble learning strategy

This study focuses on applying intelligent modeling methods to different injection molding process parameters, to analyze the influence of temperature distribution and warpage on the actual development of auto locks. It explores the auto locks using computer-aided engineering (CAE) simulation performance analysis and the optimization of process parameters by combining multiple quality characteristics (warpage and average temperature). In this experimental design, combinations were explored for each single objective optimization process parameter, using the Taguchi robust design process, with the L18 (21 × 37) orthogonal table. The control factors were injection time, material temperature, mold temperature, injection pressure, packing pressure, packing time, cooling liquid, and cooling temperature. The warpage and temperature distribution were analysed as performance indices. Then, signal-to-noise ratios (S/N ratios) were calculated. Gray correlation analysis, with normalization of the S/N ratio, was used to obtain the gray correlation coefficient, which was substituted into the fuzzy theory to obtain the multiple performance characteristic index. The maximum multiple performance characteristic index was used to find multiple quality characteristic-optimized process parameters. The optimal injection molding process parameters with single objective are a warpage of 0.783 mm and an average temperature of 235.23 °C. The optimal parameters with multi-objective are a warpage of 0.753 mm and an average temperature of 238.71 °C. The optimal parameters were then used to explore the different cooling designs (original cooling, square cooling, and conformal cooling), considering the effect of the plastics temperature distribution and warpage. The results showed that, based on the design of the different cooling systems, conformal cooling obtained an optimal warpage of 0.661 mm and a temperature of 237.62 °C. Furthermore, the conformal cooling system is smaller than the original cooling system; it reduces the warpage by 12.2%, and the average temperature by 0.46%.

A study on MOORA-based taguchi method for optimization in automated GMA welding process

This paper presents experimental study of laser cutting of Aluminium alloy sheet with the aim to optimize multiple quality characteristics such as cut edge surface roughness and kerf deviation, simultaneously. The Taguchi method combined with Grey relational analysis has been used for parameter optimization. The Principal component analysis and entropy measurement method have been used for eliminating co-linearity and deciding the weighting factors, respectively. The results indicate considerable improvements in multiple quality characteristics.

The paper concerns with the peculiarities of consumer choice in information product markets. This is a multidisciplinary study based on both information system research and microeconomic theory. An extension is introduced to the conventional general theory of consumer choice for explicitly taking into account the impact of information product quality on consumer behaviour. Multiple quality characteristics, considered against the price of product, are an essential reason for consumer choice of high tech product in general and information product in particular. We assume that consumers are able to aggregate their preferences of multiple product characteristics into a product preference order. On the supply side, the product quality characteristics incur costs. In the case of information product, those costs are the costs of the first copy, and marginal costs are near zero. All of the above constitute the distinctive characteristics of the competitive mechanism in the digital economy and in information product markets. A model, based on the game theory is used to consider two special cases. The first one deals with monopolistic competition for a share of the market with a limited number of customers. Conditions are derived for IT firm survival. The second one considers conditions at which a monopoly is able to successfully introduce a new version if its information product.KeywordsQuality CharacteristicInformation ProductConsumer ChoiceMonopoly PowerInformation System ResearchThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

An integrated contract manufacturer selection and product quality optimization methodology for the mechanical manufacturing industry

Today's fast growing sheet metal industries are demanding overall improvement in cut quality rather than improving different quality characteristics individually. The laser cutting has proven to be the quality cutting process for almost any category of materials with few limitations. The multiple quality characteristics may be improved by controlling the process variables at optimum level. The paper presents a methodology for the simultaneous optimization of multiple quality characteristics in laser cutting process by developing the software 'CATFMO' Computer Aided Taguchi-Fuzzy Multi-Optimization. The software results have been validated by comparing the published experimental data and found suitable. This methodology has been applied for simultaneous optimization of kerf width, cut edge surface roughness and heat affected zone in laser cutting of difficult-to-laser-cut Aluminium alloy sheet.

This paper considers the targeting problem for products with multiple quality characteristics. Typically, these quality characteristics cannot be measured exactly, or related directly to the process input and/or process parameters. However, they can be estimated through an indirect relationship with the multiple observed process parameters. In practice, these relations can not be explicitly written in mathematical form. Therefore, in this paper a fuzzy relation between the observed parameters and required quality characteristics is proposed. In addition to that, a fuzzy-based process targeting model is developed, and the cost function is defined using unsymmetrical interval-based Taguchi (UIT) loss function. Also, an evolutionary algorithm is designed to solve the model and to obtain the optimal process targets. The utility of the proposed model and algorithm is illustrated by a realistic example form the literature.

Applying Grey Relational Analysis and Fuzzy Neural Network to Improve The Yield of Thin-Film Sputtering Process in Color Filter Manufacturing

The output of a manufacturing process is typically evaluated based on multiple quality characteristics or responses, which are often correlated and non-normally distributed. Monitoring such processes with multiple quality characteristics is challenging, especially across varying response dimensionalities. The Mahalanobis-Taguchi System (MTS) is a prominent approach for such multivariate process control, yet research on its effectiveness remains fragmented. This study proposes an integrated MTS-based framework to address both low and high-dimensional response scenarios. While traditional MTS uses orthogonal array design for variable selection, hybrid MTS approaches employ metaheuristics such as Binary Particle Swarm Optimization (BPSO), Binary Whale Optimization Algorithm (BWOA), and Hybrid Binary Particle Swarm Optimization (HBPSO). The proposed framework incorporates a desirability function and ANOVA analysis to evaluate monitoring performance for the appropriate selection of useful variables and threshold determination for a specific data set. Comparative analysis, using literature benchmarks and real-world manufacturing data, reveals that BPSO-based MTS generally outperforms others, with HBPSO excelling in high-dimensional contexts. Notably, the method for threshold determination in MTS shows insignificant influence on overall performance, considering all datasets. This study provides practical insights into selecting effective MTS-based monitoring strategies across varying dimensionalities.

Present work highlights application of utility theory combined with Principal Component Analysis (PCA) and Taguchi’s robust design for simultaneous optimization of correlated multiple surface quality characteristics of mild steel machined product prepared by straight turning operation. The study aims at evaluating the most favorable process environment followed by an optimal parametric combination for achieving high surface quality. Traditional Taguchi based hybrid optimization approaches rely on the assumption that quality indices are uncorrelated or independent. But it is felt that, in practice, there may be some correlation among various quality indices (responses) under consideration. To overcome this limitation of Taguchi approach, the present study proposes application of PCA to convert correlated responses into uncorrelated quality indices called principal components. Finally based on utility theory, Taguchi method has been applied to solve this optimization problem. The study demonstrates detailed methodology and concludes robustness and flexibility of the proposed optimization technique and validates its effectiveness through a case study in which correlated multiple response characteristics of turning operation have been optimized.