Designing the optimal process mean vector for mixed multiple quality characteristics

2012 ◽  
Vol 44 (11) ◽  
pp. 1002-1021 ◽  
Author(s):  
Paul L. Goethals ◽  
Byung Rae Cho
Keyword(s):  
Quality Characteristics ◽  
Optimal Process ◽  
Process Mean ◽  
Multiple Quality Characteristics ◽  
Mean Vector

Multiple Quality Characteristics Optimization of Ball Grid Array Wire Bonding Process

2015 ◽  
Vol 137 (4) ◽  
Author(s):  
Tzeng Yih-Fong ◽  
Chen Fu-Chen ◽  
Chen Chih-Huang
Keyword(s):  
Ball Grid Array ◽  
Quality Characteristics ◽  
Wire Bonding ◽  
Bonding Process ◽  
Optimal Process ◽  
Factor B ◽  
Control Factors ◽  
Multiple Quality Characteristics ◽  
Factor C ◽  
Factor G

This paper presents an integrated approach combining principal component analysis (PCA) and Taguchi methods to develop a ball grid array (BGA), gold (Au) wire bonding process with multiple quality characteristics optimization. Eight main process factors of BGA wire bonding technology are selected as the control factors for parameter design. They are the factor A (seating ultrasonic generator (USG)), factor B (TIP height), factor C (C/V), factor D (USG current), factor E (USG bond time), factor F (bond force), factor G (FS threshold), and factor H (FAB size). The quality characteristics of the process in the study, including the wire pull strength, the ball shear strength, the ball thickness difference, the ball size difference, and the percentage of the Au–Al intermetallic compound (IMC) are measured. The optimal process parameters that meet the requirements for multiple quality characteristics are A1B3C1D3E3F1G1H2. They are then used to be tested for verification. Experimental results confirm that the optimal process design indeed enhances the quality characteristics investigated. The analysis of variance (ANOVA) results also show that the most important control factors affecting the quality characteristics are factor B (TIP height), factor C (C/V), and factor G (FS threshold), which accounts for 72.34% of total process variance. Thus, they must be strictly monitored during processing.

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.

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