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Stochastic Optimization of Quality Assurance Systems in Manufacturing: Integrating Robust and Probabilistic Models for Enhanced Process Performance and Product Reliability.

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Titel: Stochastic Optimization of Quality Assurance Systems in Manufacturing: Integrating Robust and Probabilistic Models for Enhanced Process Performance and Product Reliability.
Autoren: Afolabi, Kehinde, Akintayo, Busola, Babatunde, Olubayo, Kareem, Uthman Abiola, Ogbemhe, John, Ighravwe, Desmond, Oludolapo, Olanrewaju
Quelle: Journal of Manufacturing & Materials Processing; Aug2025, Vol. 9 Issue 8, p250, 19p
Schlagwörter: QUALITY assurance, PROCESS optimization, STOCHASTIC programming, GENETIC algorithms, MANUFACTURING industries, STOCHASTIC models
Abstract: This research integrates stochastic optimization techniques with robust modeling and probabilistic modeling approaches to enhance photovoltaic cell manufacturing processes and product reliability. The study employed an adapted genetic algorithm to tackle uncertainties in the manufacturing process, resulting in improved operational efficiency. It consistently achieved optimal fitness, with values remaining at 1.0 over 100 generations. The model displayed a dynamic convergence rate, demonstrating its ability to adjust performance in response to process fluctuations. The system preserved resource efficiency by utilizing approximately 2600 units per generation, while minimizing machine downtime to 0.03%. Product reliability reached an average level of 0.98, with a maximum value of 1.02, indicating enhanced consistency. The manufacturing process achieved better optimization through a significant reduction in defect rates, which fell to 0.04. The objective function value fluctuated between 0.86 and 0.96, illustrating how the model effectively managed conflicting variables. Sensitivity analysis revealed that changes in sigma material and lambda failure had a minimal effect on average reliability, which stayed above 0.99, while average defect rates remained below 0.05. This research exemplifies how stochastic, robust, and probabilistic optimization methods can collaborate to enhance manufacturing system quality assurance and product reliability under uncertain conditions. [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
Beschreibung
Abstract:This research integrates stochastic optimization techniques with robust modeling and probabilistic modeling approaches to enhance photovoltaic cell manufacturing processes and product reliability. The study employed an adapted genetic algorithm to tackle uncertainties in the manufacturing process, resulting in improved operational efficiency. It consistently achieved optimal fitness, with values remaining at 1.0 over 100 generations. The model displayed a dynamic convergence rate, demonstrating its ability to adjust performance in response to process fluctuations. The system preserved resource efficiency by utilizing approximately 2600 units per generation, while minimizing machine downtime to 0.03%. Product reliability reached an average level of 0.98, with a maximum value of 1.02, indicating enhanced consistency. The manufacturing process achieved better optimization through a significant reduction in defect rates, which fell to 0.04. The objective function value fluctuated between 0.86 and 0.96, illustrating how the model effectively managed conflicting variables. Sensitivity analysis revealed that changes in sigma material and lambda failure had a minimal effect on average reliability, which stayed above 0.99, while average defect rates remained below 0.05. This research exemplifies how stochastic, robust, and probabilistic optimization methods can collaborate to enhance manufacturing system quality assurance and product reliability under uncertain conditions. [ABSTRACT FROM AUTHOR]
ISSN:25044494
DOI:10.3390/jmmp9080250