A note on Group Selection with multiple quality characteristics: power comparison of two methods

2018 ◽  
Vol 57 (5) ◽  
pp. 1366-1370 ◽  
Author(s):  
W.L. Pearn ◽  
Chen-ju Lin ◽  
Y.H. Chen ◽  
J.Y. Huang
Keyword(s):  
Group Selection ◽  
Quality Characteristics ◽  
Power Comparison ◽  
Multiple Quality Characteristics

Group selection for processes with multiple quality characteristics

2017 ◽  
Vol 47 (16) ◽  
pp. 3923-3934 ◽  
Author(s):  
Chen-Ju Lin ◽  
W. L. Pearn ◽  
J. Y. Huang ◽  
Y. H. Chen
Keyword(s):  
Group Selection ◽  
Quality Characteristics ◽  
Multiple Quality Characteristics ◽  
Selection For

A Solution Framework for Response Surface-Based Multiple Quality Characteristics Optimization

2018 ◽  
Vol 25 (05) ◽  
pp. 1850025
Author(s):  
Sasadhar Bera ◽  
Indrajit Mukherjee
Keyword(s):  
Response Surface ◽  
Optimization Problems ◽  
Real Life ◽  
Quality Characteristics ◽  
Operating Conditions ◽  
Simultaneous Optimization ◽  
Trade Off ◽  
Multiple Quality Characteristics ◽  
Pin On Disc ◽  
Application Potential

A common problem generally encountered during manufacturing process improvement involves simultaneous optimization of multiple ‘quality characteristics’ or so-called ‘responses’ and determining the best process operating conditions. Such a problem is also referred to as ‘multiple response optimization (MRO) problem’. The presence of interaction between the responses calls for trade-off solution. The term ‘trade-off’ is an explicit compromised solution considering the bias and variability of the responses around the specified targets. The global exact solution in such types of nonlinear optimization problems is usually unknown, and various trade-off solution approaches (based on process response surface (RS) models or without using process RS models) had been proposed by researchers over the years. Considering the prevalent and preferred solution approaches, the scope of this paper is limited to RS-based solution approaches and similar closely related solution framework for MRO problems. This paper contributes by providing a detailed step-by-step RS-based MRO solution framework. The applicability and steps of the solution framework are also illustrated using a real life in-house pin-on-disc design of experiment study. A critical review on solution approaches with details on inherent characteristic features, assumptions, limitations, application potential in manufacturing and selection norms (indicative of the application potential) of suggested techniques/methods to be adopted for implementation of framework is also provided. To instigate research in this field, scopes for future work are also highlighted at the end.

Optimizing parameters for continuous electrospinning of polyacrylonitrile nanofibrous yarn using the Taguchi method

2017 ◽  
Vol 48 (3) ◽  
pp. 559-579 ◽  
Author(s):  
Chang-Mou Wu ◽  
Ching-Hsiang Hsu ◽  
Ching-Iuan Su ◽  
Chun-Liang Liu ◽  
Jiunn-Yih Lee
Keyword(s):  
Principal Component Analysis ◽  
Taguchi Method ◽  
Flow Rate ◽  
Draw Ratio ◽  
Principal Component ◽  
Quality Characteristics ◽  
Optimal Parameters ◽  
Signal To Noise ◽  
Multiple Quality Characteristics ◽  
Nanofibrous Yarn

In this study, the Taguchi method, analysis of variance, and principal component analysis were used to design the optimal parameters with respect to different quality characteristics for the continuous electrospinning of polyacrylonitrile nanofibrous yarn. The experiment was designed using a Taguchi L9(34) orthogonal array. The Taguchi method is a unique statistical method for efficiently evaluating optimal parameters and the effects of different factors on quality characteristics. The experimental results obtained by this method are more accurate and reliable than one-factor-at-a-time experiments. The control factors discussed in this work include the draw ratio, nozzle size, flow rate, and draw temperature. The quality characteristics taken into consideration are fiber diameter, fiber uniformity, and fiber arrangement. The parameters to optimize the different quality characteristics were obtained from the main effect plot of the signal-to-noise ratios, after which analysis of variance and confidence intervals were applied to confirm that the results were acceptable. Multiple quality characteristics were analyzed by principal component analysis from the normalized signal-to-noise ratios and the principal component score. Combining the experimental and analysis results, the optimum parameters for multiple quality characteristics were found to be a draw ratio of 2.0, a nozzle number of 22 G, a flow rate of 7 ml/h, and a draw temperature 120℃.

Super-Efficiency DEA Approach for Optimizing Multiple Quality Characteristics in Parameter Design

2010 ◽  
Vol 1 (2) ◽  
pp. 58-71 ◽  
Author(s):  
Abbas Al-Refaie
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Quality Characteristics ◽  
Performance Measure ◽  
Parameter Design ◽  
Data Envelopment ◽  
Super Efficiency ◽  
Multiple Quality Characteristics ◽  
Decision Making Unit ◽  
Manufacturing Applications

This paper proposes an efficient approach for optimizing the multiple quality characteristics (QCHs) in manufacturing applications on the Taguchi method using the super efficiency technique in data envelopment analysis (DEA). Each experiment in Taguchi’s orthogonal array (OA) is treated as a decision making unit (DMU) with multiple QCHs set as inputs or outputs. DMU’s efficiency is measured then adopted as a performance measure to identify the combination of optimal factor levels. Three real case studies were employed for illustration in which the proposed approach provided the largest total anticipated improvements in multiple QCHs among other techniques such as principal component analysis (PCA) and DEA based ranking (DEAR) approach. Analysis of variance is finally employed to decide significant factor effects and to predict performance.

Optimizing the Sintering Parameter of Metal Injection Molding Compact Using Robust Engineering Technique

2012 ◽  
Vol 445 ◽  
pp. 357-361
Author(s):  
Khairur Rijal Jamaludin ◽  
Norhamidi Muhamad ◽  
Sufizar Ahmad ◽  
Mohd Halim Irwan Ibrahim ◽  
Nor Hafiez Mohd Nor ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Injection Molding ◽  
Quality Characteristics ◽  
Metal Injection Molding ◽  
Sintered Part ◽  
Multiple Quality Characteristics ◽  
Sintering Time ◽  
Engineering Technique ◽  
Grey Relational Grade ◽  
Grey Relational

Sintering is a key step in the metal injection molding (MIM) process, which affects the mechanical properties of the sintered part. The mechanical properties of the sintered compacts are resulted from tremendous sintered part densification. This work utilizes robust engineering technique in optimizing sintering parameters of metal injection molding compacts. Three quality characteristics; shrinkage, density and flexure strength is optimized using Taguchi method-based grey analysis. The modified algorithm adopted here was successfully used for both detraining the optimum setting of the process parameters and for combining multiple quality characteristics into one integrated numerical value called grey relational grade. The sintering parameters investigated are: sintering temperature, sintering time, and heating rate. The result concluded that sintering time is the most significant for the combination of the quality characteristics.

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