A Confidence Interval-Based Process Optimization Method Using Second-Order Polynomial Regression Analysis Article Swipe
Jungwon Yu
,
Soyoung Yang
,
Jin‐Hong Kim
,
Young-Jae Lee
,
Kil-Taek Lim
,
Seiki Kim
,
Sung‐Soo Ryu
,
Hyeondeok Jeong
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/pr8101206
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/pr8101206
In the manufacturing processes, process optimization tasks, to optimize their product quality, can be performed through the following procedures. First, process models mimicking functional relationships between quality characteristics and controllable factors are constructed. Next, based on these models, objective functions formulating process optimization problems are defined. Finally, optimization algorithms are applied for finding solutions for these functions. It is important to note that different solutions can be found whenever these algorithms are independently executed if a unique solution does not exist; this may cause confusion for process operators and engineers. This paper proposes a confidence interval (CI)-based process optimization method using second-order polynomial regression analysis. This method evaluates the quality of the different solutions in terms of the lengths of their CIs; these CIs enclose the outputs of the regression models for these solutions. As the CIs become narrower, the uncertainty about the solutions decreases (i.e., they become statistically significant). In the proposed method, after sorting the different solutions in ascending order, according to the lengths, the first few solutions are selected and recommended for the users. To verify the performance, the method is applied to a process dataset, gathered from a ball mill, used to grind ceramic powders and mix these powders with solvents and some additives. Simulation results show that this method can provide good solutions from a statistical perspective; among the provided solutions, the users are able to flexibly choose and use proper solutions fulfilling key requirements for target processes.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/pr8101206
- https://www.mdpi.com/2227-9717/8/10/1206/pdf?version=1601012741
- OA Status
- gold
- Cited By
- 15
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3088445218
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3088445218Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/pr8101206Digital Object Identifier
- Title
-
A Confidence Interval-Based Process Optimization Method Using Second-Order Polynomial Regression AnalysisWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-09-24Full publication date if available
- Authors
-
Jungwon Yu, Soyoung Yang, Jin‐Hong Kim, Young-Jae Lee, Kil-Taek Lim, Seiki Kim, Sung‐Soo Ryu, Hyeondeok JeongList of authors in order
- Landing page
-
https://doi.org/10.3390/pr8101206Publisher landing page
- PDF URL
-
https://www.mdpi.com/2227-9717/8/10/1206/pdf?version=1601012741Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2227-9717/8/10/1206/pdf?version=1601012741Direct OA link when available
- Concepts
-
Mathematical optimization, Process (computing), Polynomial regression, Sorting, Computer science, Polynomial, Grind, Regression analysis, Algorithm, Mathematics, Engineering, Machine learning, Mathematical analysis, Mechanical engineering, Operating system, GrindingTop concepts (fields/topics) attached by OpenAlex
- Cited by
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15Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 4, 2022: 6, 2021: 2Per-year citation counts (last 5 years)
- References (count)
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30Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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