A Hybrid Machine Learning and Multi-Objective Optimization Framework for Enhancing the Grinding Expert System in Smart Manufacturing
- This paper presents a hybrid Machine Learning (ML) and multi-objective optimization framework for a data-driven grinding expert system, targeting optimal surface roughness, grinding force, and material removal rate. It integrates Gaussian Process Regression (GPR) for accurate ML-based predictive modeling, Knowledge-based Adaptive Design of Experiments (KADoE) to reduce experimental trials, NSGA-II for generating trade-off solutions, feedback-driven self-learning to refine model accuracy, and Multi-Criteria Decision-Making (MCDM) methods (e.g., TOPSIS, and AHP) for final selection. Experimental validation showed prediction accuracies above 80%, highlighting the framework’s robustness and effectiveness in enhancing grinding process efficiency and quality in smart manufacturing.
| Document Type: | Conference Proceeding |
|---|---|
| Author: | Saman Fattahi, Bahman AzarhoushangORCiDGND, Heike Kitzig-FrankORCiDGND |
| Parent Title (English): | 19th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME ‘25), 16-18 July 2025, Ischia, Italy |
| Language: | English |
| Year of Completion: | 2025 |
| Release Date: | 2025/10/08 |
| Tag: | Data-driven manufacturing; Gaussian process regression; Grinding expert system; Multi-criteria decision-making; Smart manufacturing |
| Page Number: | 6 |
| Licence (German): |  Urheberrechtlich geschützt |