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...
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| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Journal of Manufacturing and Materials Processing |
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| Online Access: | https://www.mdpi.com/2504-4494/9/2/62 |
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| author | Saman Fattahi Bahman Azarhoushang Heike Kitzig-Frank |
| author_facet | Saman Fattahi Bahman Azarhoushang Heike Kitzig-Frank |
| author_sort | Saman Fattahi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fdda952fa7cd4d87b3cd6c162299e089 |
| institution | DOAJ |
| issn | 2504-4494 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Manufacturing and Materials Processing |
| spelling | doaj-art-fdda952fa7cd4d87b3cd6c162299e0892025-08-20T03:12:11ZengMDPI AGJournal of Manufacturing and Materials Processing2504-44942025-02-01926210.3390/jmmp9020062Knowledge-Based Adaptive Design of Experiments (KADoE) for Grinding Process Optimization Using an Expert System in the Context of Industry 4.0Saman Fattahi0Bahman Azarhoushang1Heike Kitzig-Frank2KSF Institute for Advanced Manufacturing, Furtwangen University, 78532 Tuttlingen, GermanyKSF Institute for Advanced Manufacturing, Furtwangen University, 78532 Tuttlingen, GermanyKSF Institute for Advanced Manufacturing, Furtwangen University, 78532 Tuttlingen, GermanyThe 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.https://www.mdpi.com/2504-4494/9/2/62adaptive DoEgrinding expert systemdata-driven manufacturingIndustry 4.0knowledge-based adaptive design of experiments (KADoE) |
| spellingShingle | Saman Fattahi Bahman Azarhoushang Heike Kitzig-Frank Knowledge-Based Adaptive Design of Experiments (KADoE) for Grinding Process Optimization Using an Expert System in the Context of Industry 4.0 Journal of Manufacturing and Materials Processing adaptive DoE grinding expert system data-driven manufacturing Industry 4.0 knowledge-based adaptive design of experiments (KADoE) |
| title | Knowledge-Based Adaptive Design of Experiments (KADoE) for Grinding Process Optimization Using an Expert System in the Context of Industry 4.0 |
| title_full | Knowledge-Based Adaptive Design of Experiments (KADoE) for Grinding Process Optimization Using an Expert System in the Context of Industry 4.0 |
| title_fullStr | Knowledge-Based Adaptive Design of Experiments (KADoE) for Grinding Process Optimization Using an Expert System in the Context of Industry 4.0 |
| title_full_unstemmed | Knowledge-Based Adaptive Design of Experiments (KADoE) for Grinding Process Optimization Using an Expert System in the Context of Industry 4.0 |
| title_short | Knowledge-Based Adaptive Design of Experiments (KADoE) for Grinding Process Optimization Using an Expert System in the Context of Industry 4.0 |
| title_sort | knowledge based adaptive design of experiments kadoe for grinding process optimization using an expert system in the context of industry 4 0 |
| topic | adaptive DoE grinding expert system data-driven manufacturing Industry 4.0 knowledge-based adaptive design of experiments (KADoE) |
| url | https://www.mdpi.com/2504-4494/9/2/62 |
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