Revisión de la literatura sobre el uso del aprendizaje profundo enfocado en sistemas de inspección ópticos automatizados para la detección de defectos superficiales en el sector de la manufactura
DOI:
https://doi.org/10.18779/ingenio.v6i2.680Palabras clave:
manufactura, aprendizaje profundo, redes neuronales convolucionales, algoritmos optimizadores, inspección ópticos automatizados, generalización, desequilibrioResumen
El sector de la manufactura utiliza metodologías de aprendizaje automático supervisado que permiten mejorar procesos de inspección mediante la visión artificial. La inspección óptica automatizada ofrece eficiencia en el proceso de inspección para la detección de defectos en la fabricación de diversos productos. Este trabajo aporta con la identificación de aquellas limitaciones en el procesamiento de datos basados en el conjunto de reglas definidas y la gestión del dominio del proceso. Se propone una revisión de literatura sobre el uso del aprendizaje profundo enfocado a los sistemas de inspección ópticos automatizados para la detección de defectos superficiales en el sector de la manufactura. El objetivo propuesto es de identificar las diferentes arquitecturas orientadas en redes neuronales convolucionales aplicadas en sistemas de inspección óptico con el fin de automatizar la extracción de características o patrones. Por medio de la exploración de trabajos relevantes se permite identificar un total de 47 documentos seleccionados que abordan los problemas de generalización y técnicas de optimización, finalmente se contrasta la información de las diferentes arquitecturas para la elaboración de una tabla comparativa que evidencia mejoras en la precisión de los sistemas de inspección óptico mediante el porcentaje alcanzado. Estos resultados contribuyen como un insumo al conjunto de literatura existente para mejoras al sector de la manufactura.
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H. Giang Nguyen, M. Meiners, L. Schmidt, and J. Franke, “Deep learning-based automated optical inspection system for crimp connections,” 2020 10th International Electric Drives Production Conference, EDPC 2020 - Proceedings, Dec. 2020, doi: 10.1109/EDPC51184.2020.9388203.
M. A. Mallaiyan Sathiaseelan, O. P. Paradis, S. Taheri, and N. Asadizanjani, “Why Is Deep Learning Challenging for Printed Circuit Board (PCB) Component Recognition and How Can We Address It?,” undefined, vol. 5, no. 1, 2021, doi: 10.3390/CRYPTOGRAPHY5010009.
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A. Kulkarni and C. Xu, “A Deep Learning Approach in Optical Inspection to Detect Hidden Hardware Trojans and Secure Cybersecurity in Electronics Manufacturing Supply Chains,” Front Mech Eng, vol. 7, Jul. 2021, doi: 10.3389/FMECH.2021.709924.
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T.-H. Kim, H.-R. Kim, and Y.-J. Cho, “Product Inspection Methodology via Deep Learning: An Overview,” Sensors , vol. 21, no. 15. 2021. doi: 10.3390/s21155039.
J. Lehr, A. Sargsyan, M. Pape, J. Philipps, and J. Krüger, “Automated Optical Inspection Using Anomaly Detection and Unsupervised Defect Clustering,” IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, vol. 2020-September, pp. 1235–1238, Sep. 2020, doi: 10.1109/ETFA46521.2020.9212172.
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Y. Yang et al., “A lightweight deep learning algorithm for inspection of laser welding defects on safety vent of power battery,” Comput Ind, vol. 123, Dec. 2020, doi: 10.1016/j.compind.2020.103306.
Y. F. Chen, F. S. Yang, E. Su, and C. C. Ho, “Automatic Defect Detection System Based on Deep Convolutional Neural Networks,” 2019 International Conference on Engineering, Science, and Industrial Applications, ICESI 2019, Aug. 2019, doi: 10.1109/ICESI.2019.8863029.
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B. M. Haddad, S. F. Dodge, L. J. Karam, N. S. Patel, and M. W. Braun, “Locally Adaptive Statistical Background Modeling with Deep Learning-Based False Positive [1] R. Alvarado-Salazar and J. Llerena-Izquierdo, “Revisión de la literatura sobre el uso de Inteligencia Artificial enfocada a la atención de la discapacidad visual,” Revista InGenio, vol. 5, no. 1, pp. 10–21, 2022, doi: https://doi.org/10.18779/ingenio.v5i1.472.
H.-I. Lin and F. S. Wibowo, “Image Data Assessment Approach for Deep Learning-Based Metal Surface Defect-Detection Systems,” IEEE Access, vol. 9, pp. 47621–47638, 2021, doi: 10.1109/ACCESS.2021.3068256.
A. A. R. M. A. Ebayyeh and A. Mousavi, “A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry,” IEEE Access, vol. 8, pp. 183192–183271, 2020, doi: 10.1109/ACCESS.2020.3029127.
Q. Luo et al., “Automated Visual Defect Classification for Flat Steel Surface: A Survey,” IEEE Trans Instrum Meas, vol. 69, no. 12, pp. 9329–9349, 2020, doi: 10.1109/TIM.2020.3030167.
Y.-T. Li, P. Kuo, and J.-I. Guo, “Automatic Industry PCB Board DIP Process Defect Detection System Based on Deep Ensemble Self-Adaption Method,” IEEE Trans Compon Packaging Manuf Technol, vol. 11, no. 2, pp. 312–323, 2021, doi: 10.1109/TCPMT.2020.3047089.
A. M. Kamoona, A. K. Gostar, A. Bab-Hadiashar, and R. Hoseinnezhad, “Point Pattern Feature-Based Anomaly Detection for Manufacturing Defects, in the Random Finite Set Framework,” IEEE Access, vol. 9, pp. 158672–158681, 2021, doi: 10.1109/ACCESS.2021.3130261.
Y. Yang et al., “A lightweight deep learning algorithm for inspection of laser welding defects on safety vent of power battery,” Comput Ind, vol. 123, 2020, doi: 10.1016/j.compind.2020.103306.
L. C. Chen et al., “Edge-glued wooden panel defect detection using deep learning,” Wood Sci Technol, vol. 56, no. 2, pp. 477–507, Mar. 2022, doi: 10.1007/S00226-021-01316-3/TABLES/9.
Y. C. Huang, K. C. Hung, C. C. Liu, T. H. Chuang, and S. J. Chiou, “Customized Convolutional Neural Networks Technology for Machined Product Inspection,” Applied Sciences 2022, Vol. 12, Page 3014, vol. 12, no. 6, p. 3014, Mar. 2022, doi: 10.3390/APP12063014.
C. H. Lin, S. H. Wang, and C. J. Lin, “Using convolutional neural networks for character verification on integrated circuit components of printed circuit boards,” Applied Intelligence 2019 49:11, vol. 49, no. 11, pp. 4022–4032, May 2019, doi: 10.1007/S10489-019-01486-5.
P. M. Bhatt et al., “Image-Based Surface Defect Detection Using Deep Learning: A Review,” J Comput Inf Sci Eng, vol. 21, no. 4, Aug. 2021, doi: 10.1115/1.4049535/1094064.
R. K. Sheu, L. C. Chen, M. S. Pardeshi, K. C. Pai, and C. Y. Chen, “AI Landing for Sheet Metal-Based Drawer Box Defect Detection Using Deep Learning (ALDB-DL),” Processes 2021, Vol. 9, Page 768, vol. 9, no. 5, p. 768, Apr. 2021, doi: 10.3390/PR9050768.
H. I. Lin and F. S. Wibowo, “Image Data Assessment Approach for Deep Learning-Based Metal Surface Defect-Detection Systems,” IEEE Access, vol. 9, pp. 47621–47638, 2021, doi: 10.1109/ACCESS.2021.3068256.
J. W. Wang, C. C. Wang, and T. C. Cheng, “AI-based Automatic Optical Inspection of Glass Bubble Defects,” ACM International Conference Proceeding Series, pp. 242–246, Apr. 2020, doi: 10.1145/3396743.3396768.
S. A. Reynoso Farnes, D. M. Tsai, and W. Y. Chiu, “Autofocus Measurement for Electronic Components Using Deep Regression,” IEEE Trans Compon Packaging Manuf Technol, vol. 11, no. 4, pp. 697–707, Apr. 2021, doi: 10.1109/TCPMT.2021.3060809.
J. Llerena-Izquierdo, F. Procel-Jupiter, and A. Cunalema-Arana, “Mobile Application with Cloud-Based Computer Vision Capability for University Students’ Library Services,” Advances in Intelligent Systems and Computing, vol. 1277, pp. 3–15, Jun. 2021, doi: 10.1007/978-3-030-60467-7_1.
H. Giang Nguyen, M. Meiners, L. Schmidt, and J. Franke, “Deep learning-based automated optical inspection system for crimp connections,” 2020 10th International Electric Drives Production Conference, EDPC 2020 - Proceedings, Dec. 2020, doi: 10.1109/EDPC51184.2020.9388203.
M. A. Mallaiyan Sathiaseelan, O. P. Paradis, S. Taheri, and N. Asadizanjani, “Why Is Deep Learning Challenging for Printed Circuit Board (PCB) Component Recognition and How Can We Address It?,” undefined, vol. 5, no. 1, 2021, doi: 10.3390/CRYPTOGRAPHY5010009.
Y. T. Li, P. Kuo, and J. I. Guo, “Automatic Industry PCB Board DIP Process Defect Detection System Based on Deep Ensemble Self-Adaption Method,” IEEE Trans Compon Packaging Manuf Technol, vol. 11, no. 2, pp. 312–323, Feb. 2021, doi: 10.1109/TCPMT.2020.3047089.
G. S. Junior, J. Ferreira, C. Millán-Arias, R. Daniel, A. C. Junior, and B. J. T. Fernandes, “Ceramic Cracks Segmentation with Deep Learning,” Applied Sciences 2021, Vol. 11, Page 6017, vol. 11, no. 13, p. 6017, Jun. 2021, doi: 10.3390/APP11136017.
K. J. Wang, H. Fan-Jiang, and Y. X. Lee, “A multiple-stage defect detection model by convolutional neural network,” Comput Ind Eng, vol. 168, p. 108096, Jun. 2022, doi: 10.1016/J.CIE.2022.108096.
J. Li, N. Cai, Z. Mo, G. Zhou, and H. Wang, “IC solder joint inspection via generator-adversarial-network based template,” Mach Vis Appl, vol. 32, no. 4, pp. 1–16, Jul. 2021, doi: 10.1007/S00138-021-01218-1/TABLES/4.
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H. G. Nguyen, R. Habiboglu, and J. Franke, “Enabling deep learning using synthetic data: A case study for the automotive wiring harness manufacturing,” Procedia CIRP, vol. 107, pp. 1263–1268, Jan. 2022, doi: 10.1016/J.PROCIR.2022.05.142.
A. Kulkarni and C. Xu, “A Deep Learning Approach in Optical Inspection to Detect Hidden Hardware Trojans and Secure Cybersecurity in Electronics Manufacturing Supply Chains,” Front Mech Eng, vol. 7, Jul. 2021, doi: 10.3389/FMECH.2021.709924.
N. Hussain et al., “A deep neural network and classical features based scheme for objects recognition: an application for machine inspection,” Multimed Tools Appl, pp. 1–23, Apr. 2020, doi: 10.1007/S11042-020-08852-3/TABLES/8.
L.-C. Chen et al., “Edge-glued wooden panel defect detection using deep learning,” Wood Sci Technol, vol. 56, no. 2, pp. 477–507, 2022, doi: 10.1007/s00226-021-01316-3.
T.-H. Kim, H.-R. Kim, and Y.-J. Cho, “Product Inspection Methodology via Deep Learning: An Overview,” Sensors , vol. 21, no. 15. 2021. doi: 10.3390/s21155039.
J. Lehr, A. Sargsyan, M. Pape, J. Philipps, and J. Krüger, “Automated Optical Inspection Using Anomaly Detection and Unsupervised Defect Clustering,” IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, vol. 2020-September, pp. 1235–1238, Sep. 2020, doi: 10.1109/ETFA46521.2020.9212172.
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