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.
Highlights
In manufacturing, one of the major concerns faced by the industry is in saving production costs and time, and at the same time, improving the quality of the products
We propose a confidence interval (CI)-based process optimization method using second-order polynomial regression analysis; it aimed at assessing the quality of the different solutions from a statistical point of view and selecting statistically significant solutions to be provided to the users
We proposed a CI-based process optimization method using second-order polynomial regression analysis
Summary
One of the major concerns faced by the industry is in saving production costs and time, and at the same time, improving the quality of the products. These can be achieved by optimizing crucial controllable factors ( known as input, process, or design parameters) in target processes [1,2]. To obtain the best values of the controllable factors, we must first formulate functional relationships (i.e., process models) between these factors and quality characteristics; regression analysis and machine learning methods can be used to construct these relationships. To build the process models, we prepare a process dataset, composed of input-output observation pairs, beforehand. Experimental designs [3], such as full factorial, central composite, and Box-Behnken designs have been widely employed to collect these datasets in systematic ways
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