No abstract

In this work, machine learning (ML) approach based on polynomial regression was explored to analyze the optimal processing parameters and predict the target particle sizes for ball milling of alumina ceramics. Data points were experimentally obtained by measuring the particle sizes. Prediction interval (PI)-based optimization methods using polynomial regression analysis are proposed. As a first step, functional relations between the inputs and the responses are derived by applying the regression analysis. Later, based on these relations, objective functions to be maximized are defined by desirability approach. Finally, the proposed PI-based methods optimize both parameter points and intervals of the target mill for accomplishing userspecified target responses. The optimization results show that the PI-based point optimization methods can select and recommend statistically reliable optimized parameter points even though unique solutions for the objective functions do not exist. From the results of confirmation experiments, it is established that the optimized parameter points can produce desired particle sizes with quality responses quite similar to the target responses.

In this work, machine learning (ML) approach based on polynomial regression was explored to analyze the optimal processing parameters and predict the target particle sizes for ball milling of alumina ceramics. Data points were experimentally obtained by measuring the particle sizes. Prediction interval (PI)-based optimization methods using polynomial regression analysis are proposed. As a first step, functional relations between the inputs and the responses are derived by applying the regression analysis. Later, based on these relations, objective functions to be maximized are defined by desirability approach. Finally, the proposed PI-based methods optimize both parameter points and intervals of the target mill for accomplishing user-specified target responses. The optimization results show that the PI-based point optimization methods can select and recommend statistically reliable optimized parameter points even though unique solutions for the objective functions do not exist. From the results of confirmation experiments, it is established that the optimized parameter points can produce desired particle sizes with quality responses quite similar to the target responses.