Statistical Process Control for Monitoring Nonlinear Profiles: A Six Sigma Project on Curing Process

Chang, Sin-Chung; Tsai, Tzong‐Ru; Lin, Dennis K.J.; Chou, Shih-Hsiung; Lin, Yu Siang

doi:10.1080/08982112.2012.641149

Cited by 19 publications

(11 citation statements)

References 22 publications

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“…Relationships between the process variable and the covariates are called profiles, and various techniques have been suggested to monitor linear and nonlinear profiles. Some of these may be found in Ding et al, 1 Moguerza et al, 2 Williams et al, 3 Vaghefi et al, 4 Fan et al, 5 Fan et al, 6 Lee et al, 7 Chang et al, 8 and references therein. A good review of these techniques and assessment of their relative effectiveness may be found in Fan et al 6 As related therein, In this article, a third approach is suggested, which may be viewed as a hybrid of the parametric and the nonparametric approaches.…”

Section: Introductionmentioning

confidence: 98%

Modeling and Monitoring Ecological Systems—A Statistical Process Control Approach

Shore

2013

Quality & Reliability Eng

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Statistical process control monitoring of nonlinear relationships (profiles) has been the subject of much research recently. While attention is primarily given to the statistical aspects of the monitoring techniques, little effort has been devoted to developing a general modeling approach that would introduce ‘uniformity of practice’ in modeling nonlinear profiles (analogously with the three‐sigma limits of Shewhart control charts). In this article, we use response modeling methodology (RMM) to demonstrate implementation of this approach to statistical process control monitoring of ecological relationships. Using 10 ecological models that have appeared in the literature, it is first shown that RMM models can replace (approximate) current ecological models with negligible loss in accuracy. Computer simulation is then used to demonstrate that estimated RMM models and estimated data generating ecological models achieve goodness‐of‐fit that is practically indistinguishable from one another. A regression‐adjusted control scheme, based on control charts for the predicted median and for residuals variation, is developed and demonstrated for three types of ‘out of control’ scenarios. Copyright © 2013 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 98%

Modeling and Monitoring Ecological Systems—A Statistical Process Control Approach

Shore

2013

Quality & Reliability Eng

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“…Recently, the monitoring of simple linear profiles can be found in Zhu and Lin and Noorossana et al ., Eyvazian et al ., and Amiri et al . And the monitoring of nonlinear profiles can be found in Williams et al ., Shiau et al ., and Chang et al …”

Section: Introductionmentioning

confidence: 99%

“…For examples, Kang and Albin 2 provided a simple linear profile from a semiconductor manufacturing process, and Young et al 3 provided a nonlinear profile from a particleboard manufacturing process. A comprehensive review of profile monitoring can be found in Noorossana et al 4 Recently, the monitoring of simple linear profiles can be found in Zhu and Lin 5 and Noorossana et al, 6,7 Eyvazian et al, 8 and Amiri et al 9 And the monitoring of nonlinear profiles can be found in Williams et al, 10,11 Shiau et al, 12 and Chang et al 13 Assuring the process capability in linear or linear profiles to meet the requirement is a very important task. Some process capability analyses based on C pk and S pk for simple linear profiles can be found in Hosseinifard and Abbasi, 14,15 Ebadi and Shahriari, 16 and Wang.…”

Section: Introductionmentioning

confidence: 99%

Measuring Process Yield for Nonlinear Profiles

Wang

Guo

2013

Quality & Reliability Eng

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Assuring the process capability in nonlinear profiles to meet the requirement is a very important task. This paper aims at evaluating the process yield for nonlinear profiles in manufacturing processes. We present the statistical properties of the estimated SpkA and obtain its lower confidence bound. This index provides an exact measure of the process yield for nonlinear profiles. A simulation study is conducted to assess the performance of the proposed method. The simulation results confirm that the estimated SpkA value is close to the target value and has the smallest standard deviation. One real example is used to demonstrate the application of the proposed approach. Copyright © 2013 John Wiley & Sons, Ltd.

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“…Therefore, it is easier for quality engineers to monitor the pressure profile using statistical process control (SPC) tool for saving energy. Chang et al (2012) only investigated the air temperature profile of the curing process. When this process is out of control, it is very difficult for quality engineers to pinpoint the root cause.…”

Section: Introductionmentioning

confidence: 99%

“…Paynabar and Jin (2011) presented pressing force profile signals in a valve seat assembly operation. Chang et al (2012) investigated the temperature profile from a curing process for high-pressure hose products. These studies only consider one profile type in their respective applications.…”

Section: Introductionmentioning

confidence: 99%

On monitoring of multiple non-linear profiles

Chou

Chang

Tsai

2013

International Journal of Production Research

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Most state-of-the-art profile monitoring methods involve studies of one profile. However, a process may contain several sensors or probes that generate multiple profiles over time. Quality characteristics presented in multiple profiles may be related multiple aspects of product or process quality. Existing charting methods for simultaneous monitoring of each multiple profile may result in higher false alarm rates. Or worse, they cannot correctly detect potential profile relationship changes. In this study, we propose two approaches to detect process shifts in multiple nonlinear profiles. A simulation study was conducted to evaluate the performance of the proposed approaches in terms of average run length under different process shift scenarios. Pros and cons of the proposed methods are discussed. A guideline for choosing the proposed methods is introduced. In addition, a hybrid method combining the salient points of both approaches is explored. Finally, a real-world data set from a vulcanization process is used to demonstrate the implementation of the proposed methods.

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Statistical Process Control for Monitoring Nonlinear Profiles: A Six Sigma Project on Curing Process

Cited by 19 publications

References 22 publications

Modeling and Monitoring Ecological Systems—A Statistical Process Control Approach

Modeling and Monitoring Ecological Systems—A Statistical Process Control Approach

Measuring Process Yield for Nonlinear Profiles

On monitoring of multiple non-linear profiles

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