Quality design method using process capability index based on Monte-Carlo method and real-coded genetic algorithm

Otsuka, Akimasa; Nagata, Fusaomi

doi:10.1016/j.ijpe.2018.08.016

Cited by 25 publications

(14 citation statements)

References 14 publications

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“…Comparisons of the two methodologies as alternative approaches to optimization are for instance made in Korany et al 24 to determine significant factors for optimization of a condensation reaction. DoE-related fields, such as tolerance design, have recently been shown to benefit from ML approaches, 25 but the DoE methodology as such has not been assessed in this regard. The above-cited papers compare or combine the two concepts in specific areas of interest or for specific problem investigations, but a paper producing a generalizable assessment of how the two methodologies can be applied jointly to increase investigative potency in production, or discussing the implications of ML for the future use of DoE, has not been put forward so far.…”

Section: Literature Overviewmentioning

confidence: 99%

Machine learning and Design of Experiments: Alternative approaches or complementary methodologies for quality improvement?

Freiesleben

Keim

Grutsch

2020

Quality & Reliability Eng

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Machine Learning (ML), or the ability of self-learning computer algorithms to autonomously structure and interpret data, is a methodological approach to solve complicated optimization problems based on abundant data. ML is recently gaining momentum as algorithmic applications, computing potency, and available data sets increased manifold over the past two decades, providing an information-rich environment in which human reasoning can partially be replaced by computer reasoning. In this paper, we want to assess the implications of ML for Design of Experiments (DoE), a statistical methodology widely used in Quality Management for quantifying effects and interactions of factors with influence on the production quality or the process yield. We specifically want to assess the future role and importance of DoE: Will it remain unaltered by ML, will it be made obsolete, or will it be reinforced? With this, we want to

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Section: Literature Overviewmentioning

confidence: 99%

Machine learning and Design of Experiments: Alternative approaches or complementary methodologies for quality improvement?

Freiesleben

Keim

Grutsch

2020

Quality & Reliability Eng

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“…Liao and Pearn 24 modified the weighted standard deviation index C pk ( WSD ) to propose an adequate measure of lognormal process capability. Otsuka and Nagata 25 proposed a novel approach to process capability which controls part dimensions based on product performance. Zhou et al 26 proposed a sensitivity analysis of process capability evaluation for small-batch production runs.…”

Section: Literature Reviewmentioning

confidence: 99%

A fuzzy approach to determine process quality for one-sided specification with imprecise data

Chen

Chang

2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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Process quality is a crucial determinant of client satisfaction and affects a product’s value; therefore, maintaining quality control is a vital aspect of corporate sustainability and development. Many researchers have developed evaluation models for process quality. The majority of such research assumes that collected measurement data are precise, but fuzziness and stochastic uncertainty are unavoidable features of any collected data. When the measurements of a quality characteristic are insufficiently precise, a crisp-based approach is not suitable for the assessment of process quality. This study endeavored to use one-sided Six Sigma quality indices as measurement tools to accurately reflect process yield and quality levels. Taking Buckley’s approach into consideration, we extend the crisp estimators from the indices into fuzzy estimators. We then develop a fuzzy hypothesis testing method for one-sided Six Sigma quality indices, with the intent of increasing reliability of evaluation for process quality levels. Finally, we present a real-world case to illustrate implementation of the proposed approach, demonstrating its effectiveness and practical applicability.

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“…Since real-coded coding does not require codec decoding, it is closer to the problem, and it is easier to combine with other search techniques. Therefore, processing function optimization is increasingly using real-coded genetic algorithms [39,40].…”

Section: Solving the Relationship Weightmentioning

confidence: 99%

Factors and Paths of Transformation and Upgradation of Chemical Industry in Shandong, China

et al. 2020

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In the context of the Internet, there is a historical fusion between the new round of scientific and technological revolution and industrial transformation and the accelerated transformation of economic development methods. The task of the Shandong chemical industry’s transformation and upgradation is arduous and urgent. In this context, this article studies the influencing factors and development paths of the transformation and upgradation of the Shandong chemical industry. It provides theoretical guidance for high-quality and sustainable development of the Shandong chemical industry. First, this paper uses the fuzzy cognitive map tool to learn the historical laws of the transformation and upgradation of the Shandong chemical industry. It then determines the path of chemical industry transformation and upgradation, according to the interactions and weights between the learned indicators. By adjusting the influence coefficient between the indicators on each path, the degree of change in the influence of the indicator on the target value is analyzed. The critical path and the key indicators on the path according to the degree of impact are determined. Finally, the key path selected in this article is: return on investment (C8) → unit production value environmental consumption (C9) → number of R&D personnel (C4) → R&D expenditure internal expenditure (C6) → industrial output (C1) → energy consumption coefficient (C3). The key indicator on this path is R&D expenditure internal expenditure. According to the results, the development suggestion for the transformation and upgradation of the chemical industry in Shandong is to take the path of green development through technological innovation.

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Quality design method using process capability index based on Monte-Carlo method and real-coded genetic algorithm

Cited by 25 publications

References 14 publications

Machine learning and Design of Experiments: Alternative approaches or complementary methodologies for quality improvement?

Machine learning and Design of Experiments: Alternative approaches or complementary methodologies for quality improvement?

A fuzzy approach to determine process quality for one-sided specification with imprecise data

Factors and Paths of Transformation and Upgradation of Chemical Industry in Shandong, China

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