Knowledge-Based Adaptive Design of Experiments (KADoE) for Grinding Process Optimization Using an Expert System in the Context of Industry 4.0

The integration of human–cyber–physical systems (HCPSs), IoT, digital twins, and big data analytics underpins Industry 4.0, transforming traditional manufacturing into smart manufacturing with capabilities for real-time monitoring, quality assessment, and anomaly detection. A key advancement is the transition from static to adaptive design of experiments (DoE), using real-time analytics for iterative refinement. This paper introduces an innovative adaptive DoE embedded in an expert system for grinding, combining data-driven and knowledge-based methodologies. The KSF Grinding Expert™ system recommends optimized grinding control variables, guided by a multi-objective optimization framework using Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Gray Relational Analysis (GRA). The rule-based adaptive DoE iteratively refines recommendations through feedback and historical insights, reducing the number of trials by excluding suboptimal parameters. A case study validates the approach, demonstrating significant enhancements in process efficiency and precision. This knowledge-based adaptive strategy reduces experimental trials, adapts DoE according to different grinding processes, and can prevent critical defects such as surface cracks. In the case study, optimized results which are offered by the expert system and validated with over 90% accuracy are incorporated into the system’s knowledge base, enabling continuous improvement and reduced experimentation in future iterations.