상세정보
학술지
A machine learning approach for ball milling of alumina ceramics
- 발행일
- 202212
- 출처
- International Journal of Advanced Manufacturing Technology, v.123 no.11-12, pp.4293-4308
- ISSN
- 0268-3768
- 출판사
- Springer
- 협약과제
- 22ZD1100, 대경권 지역산업 기반 ICT 융합기술 고도화 지원사업, 문기영
- 초록
- In this work, machine learning 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 collected by measuring the particle sizes. Prediction interval (PI)-based optimization methods using polynomial regression analysis are proposed. As a first step, functional relations between processing parameters (inputs) and quality responses (outputs) 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 final powders with quality responses quite similar to the target responses.
- KSP 제안 키워드
- Ball-milling, Desirability approach, Machine Learning Approach, Optimal processing parameters, Prediction interval, alumina ceramics, objective function, optimization methods, particle size, polynomial regression analysis