A Confidence Interval-Based Process Optimization Method Using Second-Order Polynomial Regression Analysis
Open Access
- 23 September 2020
- Vol. 8 (10), 1206
- https://doi.org/10.3390/pr8101206
Abstract
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.Keywords
Funding Information
- Ministry of Trade, Industry and Energy (20004367)
This publication has 32 references indexed in Scilit:
- Using a multiple response optimization approach to optimize the coefficient of performanceApplied Thermal Engineering, 2016
- A multistage and multiple response optimization approach for serial manufacturing systemEuropean Journal of Operational Research, 2016
- Particle swarm optimizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- Application of response surface methodology for modeling of ball mills in copper sulphide ore grindingPowder Technology, 2013
- A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical OptimizationJournal of Applied Mathematics, 2013
- Experimental designs and their recent advances in set-up, data interpretation, and analytical applicationsJournal of Pharmaceutical and Biomedical Analysis, 2011
- Modeling and Optimization of Machining ProcessesPublished by Springer Science and Business Media LLC ,2010
- Optimizing power consumption for CNC turned parts using response surface methodology and Taguchi's technique—A comparative analysisJournal of the American Academy of Dermatology, 2008
- Application of Taguchi and response surface methodologies for geometric error in surface grinding processInternational Journal of Machine Tools and Manufacture, 2004
- Self-Organizing Hierarchical Particle Swarm Optimizer With Time-Varying Acceleration CoefficientsIEEE Transactions on Evolutionary Computation, 2004