Monitoring and Interpretation of Process Variability Generated from the Integration of the Multivariate Cumulative Sum Control Chart and Artificial Intelligence
Variability in manufacturing processes must be properly monitored and controlled to avoid incurring quality problems; otherwise, the probability of manufacturing defective products increases, and, consequently, production costs rise. This paper presents the development of a methodology to locate the...
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MDPI AG
2024-10-01
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| Series: | Applied Sciences |
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| author | Edgar Augusto Ruelas-Santoyo Vicente Figueroa-Fernández Moisés Tapia-Esquivias Yaquelin Verenice Pantoja-Pacheco Edgar Bravo-Santibáñez Javier Cruz-Salgado |
| author_facet | Edgar Augusto Ruelas-Santoyo Vicente Figueroa-Fernández Moisés Tapia-Esquivias Yaquelin Verenice Pantoja-Pacheco Edgar Bravo-Santibáñez Javier Cruz-Salgado |
| author_sort | Edgar Augusto Ruelas-Santoyo |
| collection | DOAJ |
| description | Variability in manufacturing processes must be properly monitored and controlled to avoid incurring quality problems; otherwise, the probability of manufacturing defective products increases, and, consequently, production costs rise. This paper presents the development of a methodology to locate the source(s) of variation in the manufacturing process in case of a statistical deviation so that the user can quickly take corrective actions to eliminate the source of variation, thus avoiding the manufacture of out-of-specification products. The methodology integrates the multivariate cumulative sum control chart and the multilayer perceptron artificial neural network for the detection and interpretation of the source(s) of variation generated in the manufacturing processes. A case study was carried out with a printed circuit board manufacturing process, and it was possible to classify the origin of the variation with a sensitivity of 92.41% and specificity of 91.16%. The results demonstrate the viability of the proposed methodology to monitor and interpret the source of statistical variation present in production systems. |
| format | Article |
| id | doaj-art-0ac54947891f49938866b3d07eb1d441 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-0ac54947891f49938866b3d07eb1d4412025-08-20T02:49:55ZengMDPI AGApplied Sciences2076-34172024-10-011421970510.3390/app14219705Monitoring and Interpretation of Process Variability Generated from the Integration of the Multivariate Cumulative Sum Control Chart and Artificial IntelligenceEdgar Augusto Ruelas-Santoyo0Vicente Figueroa-Fernández1Moisés Tapia-Esquivias2Yaquelin Verenice Pantoja-Pacheco3Edgar Bravo-Santibáñez4Javier Cruz-Salgado5Department of Industrial Engineering, Tecnológico Nacional de México en Celaya, Antonio García Cubas No. 600, Fovissste, Celaya 38010, Guanajuato, MexicoDepartment of Industrial Engineering, Tecnológico Nacional de México en Celaya, Antonio García Cubas No. 600, Fovissste, Celaya 38010, Guanajuato, MexicoDepartment of Industrial Engineering, Tecnológico Nacional de México en Celaya, Antonio García Cubas No. 600, Fovissste, Celaya 38010, Guanajuato, MexicoDepartment of Industrial Engineering, Tecnológico Nacional de México en Celaya, Antonio García Cubas No. 600, Fovissste, Celaya 38010, Guanajuato, MexicoIntensive Care Unit, Centro Médico Nacional del Bajío, Blvd. Adolfo López Mateos 811, Obrera, León de los Aldama 37000, Guanajuato, MexicoDepartment of Industrial and Mechanical Engineering, Universidad de las Américas Puebla, Ex Hacienda Sta. Catarina Mártir S/N, San Andrés Cholula 72810, Puebla, MexicoVariability in manufacturing processes must be properly monitored and controlled to avoid incurring quality problems; otherwise, the probability of manufacturing defective products increases, and, consequently, production costs rise. This paper presents the development of a methodology to locate the source(s) of variation in the manufacturing process in case of a statistical deviation so that the user can quickly take corrective actions to eliminate the source of variation, thus avoiding the manufacture of out-of-specification products. The methodology integrates the multivariate cumulative sum control chart and the multilayer perceptron artificial neural network for the detection and interpretation of the source(s) of variation generated in the manufacturing processes. A case study was carried out with a printed circuit board manufacturing process, and it was possible to classify the origin of the variation with a sensitivity of 92.41% and specificity of 91.16%. The results demonstrate the viability of the proposed methodology to monitor and interpret the source of statistical variation present in production systems.https://www.mdpi.com/2076-3417/14/21/9705artificial neural networkmultivariate statistical process control |
| spellingShingle | Edgar Augusto Ruelas-Santoyo Vicente Figueroa-Fernández Moisés Tapia-Esquivias Yaquelin Verenice Pantoja-Pacheco Edgar Bravo-Santibáñez Javier Cruz-Salgado Monitoring and Interpretation of Process Variability Generated from the Integration of the Multivariate Cumulative Sum Control Chart and Artificial Intelligence Applied Sciences artificial neural network multivariate statistical process control |
| title | Monitoring and Interpretation of Process Variability Generated from the Integration of the Multivariate Cumulative Sum Control Chart and Artificial Intelligence |
| title_full | Monitoring and Interpretation of Process Variability Generated from the Integration of the Multivariate Cumulative Sum Control Chart and Artificial Intelligence |
| title_fullStr | Monitoring and Interpretation of Process Variability Generated from the Integration of the Multivariate Cumulative Sum Control Chart and Artificial Intelligence |
| title_full_unstemmed | Monitoring and Interpretation of Process Variability Generated from the Integration of the Multivariate Cumulative Sum Control Chart and Artificial Intelligence |
| title_short | Monitoring and Interpretation of Process Variability Generated from the Integration of the Multivariate Cumulative Sum Control Chart and Artificial Intelligence |
| title_sort | monitoring and interpretation of process variability generated from the integration of the multivariate cumulative sum control chart and artificial intelligence |
| topic | artificial neural network multivariate statistical process control |
| url | https://www.mdpi.com/2076-3417/14/21/9705 |
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