A Novel Methodology for Performance Evaluation in Advanced Quality Control
Current global conditions and challenges in industrial manufacturing, marked by dynamism, competition, and the need for responsible resource management, have increased the demand for sustainable manufacturing practices. The integration of Industry 4.0 and the recent development of Industry 5.0 have...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/2/259 |
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| author | Ethel García Rita Peñabaena-Niebles Winston S. Percybrooks Kevin Palomino |
| author_facet | Ethel García Rita Peñabaena-Niebles Winston S. Percybrooks Kevin Palomino |
| author_sort | Ethel García |
| collection | DOAJ |
| description | Current global conditions and challenges in industrial manufacturing, marked by dynamism, competition, and the need for responsible resource management, have increased the demand for sustainable manufacturing practices. The integration of Industry 4.0 and the recent development of Industry 5.0 have added dynamism, which has generated profound implications for quality control and process monitoring, focusing mainly on recognising control patterns within the manufacturing environment. This study introduces a novel methodology for evaluating the performance of pattern classification models used in advanced quality control. Our approach incorporates robust performance metrics, early detection, window size, network hyperparameters, and concurrent patterns within a simulated monitoring environment. Unlike previous research, our evaluation methodology addresses the sensitivity of classification models to various factors, emphasising the critical balance between early detection and minimising false alarms. The findings reveal that window size significantly impacts the model’s sensitivity to pattern changes, highlighting that measuring early detection alone is impractical in real-world applications. Furthermore, optimal hyperparameter selection enhances the model’s practical applicability. |
| format | Article |
| id | doaj-art-ff135949434348a5a1657647f4198f64 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-ff135949434348a5a1657647f4198f642025-01-24T13:39:55ZengMDPI AGMathematics2227-73902025-01-0113225910.3390/math13020259A Novel Methodology for Performance Evaluation in Advanced Quality ControlEthel García0Rita Peñabaena-Niebles1Winston S. Percybrooks2Kevin Palomino3Department of Industrial Engineering, Universidad del Norte, Barranquilla 081007, ColombiaDepartment of Industrial Engineering, Universidad del Norte, Barranquilla 081007, ColombiaDepartment of Industrial Engineering, Universidad del Norte, Barranquilla 081007, ColombiaDepartment of Industrial Engineering, Universidad del Norte, Barranquilla 081007, ColombiaCurrent global conditions and challenges in industrial manufacturing, marked by dynamism, competition, and the need for responsible resource management, have increased the demand for sustainable manufacturing practices. The integration of Industry 4.0 and the recent development of Industry 5.0 have added dynamism, which has generated profound implications for quality control and process monitoring, focusing mainly on recognising control patterns within the manufacturing environment. This study introduces a novel methodology for evaluating the performance of pattern classification models used in advanced quality control. Our approach incorporates robust performance metrics, early detection, window size, network hyperparameters, and concurrent patterns within a simulated monitoring environment. Unlike previous research, our evaluation methodology addresses the sensitivity of classification models to various factors, emphasising the critical balance between early detection and minimising false alarms. The findings reveal that window size significantly impacts the model’s sensitivity to pattern changes, highlighting that measuring early detection alone is impractical in real-world applications. Furthermore, optimal hyperparameter selection enhances the model’s practical applicability.https://www.mdpi.com/2227-7390/13/2/259control chart pattern recognitionconcurrent patternwindow sizelong short term memory |
| spellingShingle | Ethel García Rita Peñabaena-Niebles Winston S. Percybrooks Kevin Palomino A Novel Methodology for Performance Evaluation in Advanced Quality Control Mathematics control chart pattern recognition concurrent pattern window size long short term memory |
| title | A Novel Methodology for Performance Evaluation in Advanced Quality Control |
| title_full | A Novel Methodology for Performance Evaluation in Advanced Quality Control |
| title_fullStr | A Novel Methodology for Performance Evaluation in Advanced Quality Control |
| title_full_unstemmed | A Novel Methodology for Performance Evaluation in Advanced Quality Control |
| title_short | A Novel Methodology for Performance Evaluation in Advanced Quality Control |
| title_sort | novel methodology for performance evaluation in advanced quality control |
| topic | control chart pattern recognition concurrent pattern window size long short term memory |
| url | https://www.mdpi.com/2227-7390/13/2/259 |
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