Aprimorando o Controle de Qualidade na Fabricação por Fundição com Vision Transformers
Resumo
O controle de qualidade é essencial na manufatura moderna para garantir a confiabilidade do produto e a competitividade. Este artigo aborda a detecção de defeitos em discos de fundição para impulsores de bombas submersíveis utilizando inspeção visual automatizada. Propomos um método baseado em Vision Transformers (ViT), que utilizam mecanismos de autoatenção para aprender padrões visuais de forma eficaz. Experimentos reais e simulados mostraram alta precisão (99,22%) e forte robustez a ruídos nas imagens, mantendo 95,45% e 98,28% de precisão com ruídos Gaussiano e Sal-e-Pimenta, respectivamente. Os resultados confirmam a confiabilidade do método e seu potencial para otimizar o processo de fundição.Referências
Dabhi, R. (2020). Casting product image data for quality inspection. [link]. Accessed: 2024-05-08.
De Souza Gil, E., De Abreu Dias, L. M., De Souza Brito, A., and Oliveira, F. G. (2024). Enhancing casting manufacturing quality control with vision transformers. In 2024 Brazilian Symposium on Robotics (SBR) and 2024 Workshop on Robotics in Education (WRE), pages 162–167.
Dong, X., Taylor, C. J., and Cootes, T. F. (2018). Small defect detection using convolutional neural network features and random forests. In Proceedings of the European Conference on Computer Vision (ECCV) Workshops, pages 0–0.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N. (2021). An image is worth 16x16 words: Transformers for image recognition at scale.
Duan, L., Yang, K., and Ruan, L. (2021). Research on automatic recognition of casting defects based on deep learning. IEEE Access, 9:12209–12216.
Kumaresan, S., Aultrin, K. J., Kumar, S., and Anand, M. D. (2021). Transfer learning with cnn for classification of weld defect. Ieee Access, 9:95097–95108.
Omar, F., Sohrab, H., Saad, M., Hameed, A., and Bakhsh, F. I. (2022). Deep learning binary-classification model for casting products inspection. In 2022 2nd Int. Conf. on Power Electronics IoT Applications in Renewable Energy and its Control (PARC), pages 1–6.
Rocha, C. S., Menezes, M. A., and Oliveira, F. G. (2016). Detecção automática de microcomponentes smt ausentes em placas de circuito impresso. In Workshop on Industry Applications (WIA) in the 29th Conference on Graphics, Patterns and Images (SIBGRAPI 2016), São José dos Campos, Sp, Brazil, volume 1.
Silva., C. N., Ferreira., N. P., Meireles., S. S., Otani., M., J. da Silva., V., O. de Freitas., C. A., and Oliveira., F. G. (2022). The visual inspection of solder balls in semiconductor encapsulation. In Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - ICINCO, pages 750–757. INSTICC, SciTePress.
Simonyan, K. and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition.
Tan, M. and Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In Int. conference on machine learning, pages 6105–6114. PMLR.
De Souza Gil, E., De Abreu Dias, L. M., De Souza Brito, A., and Oliveira, F. G. (2024). Enhancing casting manufacturing quality control with vision transformers. In 2024 Brazilian Symposium on Robotics (SBR) and 2024 Workshop on Robotics in Education (WRE), pages 162–167.
Dong, X., Taylor, C. J., and Cootes, T. F. (2018). Small defect detection using convolutional neural network features and random forests. In Proceedings of the European Conference on Computer Vision (ECCV) Workshops, pages 0–0.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N. (2021). An image is worth 16x16 words: Transformers for image recognition at scale.
Duan, L., Yang, K., and Ruan, L. (2021). Research on automatic recognition of casting defects based on deep learning. IEEE Access, 9:12209–12216.
Kumaresan, S., Aultrin, K. J., Kumar, S., and Anand, M. D. (2021). Transfer learning with cnn for classification of weld defect. Ieee Access, 9:95097–95108.
Omar, F., Sohrab, H., Saad, M., Hameed, A., and Bakhsh, F. I. (2022). Deep learning binary-classification model for casting products inspection. In 2022 2nd Int. Conf. on Power Electronics IoT Applications in Renewable Energy and its Control (PARC), pages 1–6.
Rocha, C. S., Menezes, M. A., and Oliveira, F. G. (2016). Detecção automática de microcomponentes smt ausentes em placas de circuito impresso. In Workshop on Industry Applications (WIA) in the 29th Conference on Graphics, Patterns and Images (SIBGRAPI 2016), São José dos Campos, Sp, Brazil, volume 1.
Silva., C. N., Ferreira., N. P., Meireles., S. S., Otani., M., J. da Silva., V., O. de Freitas., C. A., and Oliveira., F. G. (2022). The visual inspection of solder balls in semiconductor encapsulation. In Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - ICINCO, pages 750–757. INSTICC, SciTePress.
Simonyan, K. and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition.
Tan, M. and Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In Int. conference on machine learning, pages 6105–6114. PMLR.
Publicado
01/07/2025
Como Citar
DIAS, Lucas Matos A.; GIL, Emanuelle S.; BRITO, Alternei S.; OLIVEIRA, Felipe G..
Aprimorando o Controle de Qualidade na Fabricação por Fundição com Vision Transformers. In: CONFERÊNCIA DE TECNOLOGIA DO ICET (CONNECTECH), 2. , 2025, Itacoatiara/AM.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 262-269.
DOI: https://doi.org/10.5753/connect.2025.12340.
