Robust process capability indices and statistical inference based on model selection

Wang, Shuai; Chiang, Jyun‐You; Tsai, Tzong‐Ru; Qin, Yan

doi:10.1016/j.cie.2021.107265

Cited by 25 publications

(14 citation statements)

References 31 publications

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“…If 𝐿𝑤 ≤ 𝐶 𝐵𝑆 𝑝 ≤ 𝑈𝑝, 𝐼(𝐿𝑤 ≤ 𝐶 𝐵𝑆 𝑝 ≤ 𝑈𝑝) = 1, otherwise 𝐼(𝐿𝑤 ≤ 𝐶 𝐵𝑆 𝑝 ≤ 𝑈𝑝) = 0. Then the estimated value of CP can be obtained based on Equation (24).…”

Section: Simulation Proceduresmentioning

confidence: 99%

See 1 more Smart Citation

Generalized interval estimation of process capability indices for the Birnbaum–Saunders distribution

Bao-cai

Xia

et al. 2022

Quality & Reliability Eng

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Since sampling variation would lead to the inaccurate assessment of process capability indices (PCIs), the interval estimation of PCIs has received considerable attention recently. The coverage probabilities (CPs) of the widely used bootstrap confidence intervals (BCIs) of PCIs are not sufficiently close to their nominal confidence level. Moreover, the bootstrap method is time-consuming. This paper develops a procedure for constructing generalized confidence intervals (GCIs) of two widely used percentile-based PCIs for the Birnbaum-Saunders (BS) distribution. A simulation study is conducted and the results indicate that the proposed GCI outperforms its bootstrap counterparts in terms of the CPs, the average widths (AWs) of the confidence intervals, and the variability of the interval widths. Finally, two real examples are used to illustrate the implementation of the proposed procedure.

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Section: Simulation Proceduresmentioning

confidence: 99%

“…In the paper of Besseris, 23 three robust estimates of a PCI were considered and four typical Weibull distributions were chosen to explore the trends of the three robust estimators. More literature on robust estimation can also be found in Wang et al, 24 Besseris, 25 Wooluru et al, 26 and so on.…”

Section: Introductionmentioning

confidence: 99%

Generalized interval estimation of process capability indices for the Birnbaum–Saunders distribution

Bao-cai

Xia

et al. 2022

Quality & Reliability Eng

View full text Add to dashboard Cite

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“…According to Equation (10), the fitness value C Fit was a hypersphere with a maximum radius of 1, and the closer the value was to 1, the better the optimization results gained. The historical individual optimal position X P and group historical optimal position X g were selected according to the fitness values.…”

Section: B Fitness Evaluationmentioning

confidence: 99%

“…On the one hand, it usually takes a long time to optimize different properties results with the combination of composition and process parameters [9]. On the other hand, the equipment process control capabilities also restrict the level of product quality control [10]; in this situation, it is necessary to transform the original point optimization problem into an interval optimization problem.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Objective Process Parameter Interval Optimization Method for Steel Production

Yan

2021

Metals

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Customized small batch orders and sustainable development requirements pose challenges for product quality control and manufacturing process optimization for steel production. Building a multi-quality objective process parameter optimization method that converts the original single target optimization into multi-objective interval capability optimization has become a new method to ensure product quality qualification rate and reduce production costs. Aiming at the multi-quality objective control problem of plate products, we proposed a novel multi-objective process parameter interval optimization model (MPPIO) with equipment process control capability and parameter sensitive analysis. The multi-output support vector regression method was used to establish a multi-quality objective prediction model, which was settled as a verification model for the process parameter optimization results based on the particle swarm optimization algorithm (PSO). The process control capability functions of key parameters were fitted based on the real data in production. With these functions, each optimized particle of the classical PSO was converted into the particle beam of the MIPPO. The iteration process was weight controlled by calculating the Morris sensitivity between each input parameter and output index in the multi-quality objective prediction model, and finally the processing control window of each key parameter was determined according to the process parameter optimization results. The experimental results show that the MPPIO model can obtain the optimal parameter optimization results with the maximum processing capacity and meet the customized processing range requirements. The MPPIO model can reduce the difficulty of control and save production costs while ensuring the product properties is qualified.

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“…Regarding product quality, quite a few quality control engineers and experts in statistics have been dedicated to their studies on process capability indicators in terms of evaluation, analysis, and improvement [3,31,[34][35]. In addition, Six Sigma is also a method commonly adopted in the industry to improve process quality [15,[21][22]25].…”

Section: Introductionmentioning

confidence: 99%

Untitled

2021

EiN

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In practice, lifetime performance index CL has been a method commonly applied to the evaluation of quality performance. L is the upper or lower limit of the specification. The product lifetime distribution is mostly abnormal distribution. This study explored that the lifetime of commodities comes from exponential distribution. Complete data collection is the primary goal of analysis. However, the censoring type is one of the most commonly used methods due to considerations of manpower and material cost or the timeliness of product launch. This study adopted Type-II right censoring to find out the uniformly minimum variance unbiased (UMVU) estimator of the lifetime performance index CL and its probability density function. Afterward this study obtained the 100×(1-α)% confidence interval of the lifetime performance index CL as well as created the uniformly most powerful (UMP) test and the power of the test for the product lifetime performance index. Last, this study came up with a numerical example to demonstrate the suggested method as well as the application of the model.

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Robust process capability indices and statistical inference based on model selection

Cited by 25 publications

References 31 publications

Generalized interval estimation of process capability indices for the Birnbaum–Saunders distribution

Generalized interval estimation of process capability indices for the Birnbaum–Saunders distribution

A Novel Multi-Objective Process Parameter Interval Optimization Method for Steel Production

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