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학술지 A Confidence Interval-Based Process Optimization Method Using Second-Order Polynomial Regression Analysis
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저자
유정원, 양소영, 김진홍, 이영재, 임길택, 김세기, 류성수, 정현덕
발행일
202010
출처
Processes, v.8 no.10, pp.1-20
ISSN
2227-9717
출판사
MDPI
DOI
https://dx.doi.org/10.3390/PR8101206
협약과제
20PS1400, 세라믹산업 제조혁신을 위한 클라우드 기반 빅데이터 플랫폼 개발, 지수영
초록
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.
KSP 제안 키워드
Ceramic powder, Controllable factors, Functional relationship, Manufacturing processes, Optimization algorithm, Optimization problem, Process Optimization, Process model, Product Quality, Quality characteristic, Regression Model
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