Toz yatak füzyon birleştirme eklemeli imalatta kusur tespiti için öğrenme aktarımı kullanan derin öğrenme tabanlı bir yaklaşım

Yıl 2022, Cilt: 37 Sayı: 1, 361 - 376, 10.11.2021

Burhan Duman , Koray Özsoy

https://doi.org/10.17341/gazimmfd.870436

Cited By: 6

Öz

Günümüzde toz yatak füzyon birleştirme (TYB) metal eklemeli imalat, karmaşık geometrili parça imalatında sıklıkla tercih edilmesine rağmen, parça imalat süreçlerinin gerçek zamanlı izlenmesi yeterli düzeyde değildir. Bu nedenle makine kontrol sistemi büyük ölçüde açık döngü olarak kalmaktadır. Bazı metal eklemeli imalat makineleri toz yatağının izlenmesini görüntülerle sunarken, toz yatağı katmanında oluşabilecek kusurların otomatik tespiti ve kontrol sistemini uyarıcı yeteneğinin olduğuna rastlanmamıştır. Çalışmada, herhangi bir TYB metal eklemeli imalat makinesinde gerçek zamanlı kontrol sisteminin bir bileşeni olma potansiyeline sahip toz yatağı görüntülerinin yerinde izlenmesi ve kusurların tespiti için makine öğrenmesi temelli örnek bir yaklaşım sunulmuştur. Makine öğrenmesinin alt alanlarından olan derin öğrenme yöntemi kullanılarak, işlemin bir katmanının oluşturulmasında meydana gelebilecek kusurları tespitine yönelik sınıflandırma yapılmıştır. Kusurları algılama ve sınıflandırma işlemi evrişimli sinir ağları modeli kullanılarak yerine getirilmiştir. Modelin eğitimi ve performansı için veri seti, EOS M290 makinesinde imal edilmiş örnek bir üç boyutlu yapının fotoğrafları ile oluşturulmuştur. VGG-16, InceptionV3 ve DenseNet ön öğrenmeli modellerinden transfer öğrenimi yapılarak en iyi performans %86 doğruluk değeri ile VGG-16 modelinde elde edilmiştir.

Anahtar Kelimeler

Eklemeli İmalat, Toz Yatak Füzyon, Derin Öğrenme, Kusur Tespiti, Öğrenme Aktarımı

Teşekkür

Yazarlar, çalışma için imalat ve görüntüleme işlemlerindeki yardımlarından dolayı 3DDT firmasına teşekkür eder.

A deep learning-based approach for defect detection in powder bed fusion additive manufacturing using transfer learning

Öz

Although powder bed fusion joining (TYB) metal additive manufacturing is frequently preferred in the production of complex geometry parts today, real-time monitoring of part manufacturing processes is insufficient. Therefore, the machine control system remains largely open loop. While some metal additive manufacturing machines present the powder bed monitoring with images, it has not been found that they can automatically detect the defects that may occur in the powder bed layer and stimulate the control system. In the study, an exemplary machine learning-based approach is presented for on-site monitoring and defect detection of powder bed images, which can be a component of a real-time control system in any TYB metal additive manufacturing machine. Using the deep learning method, which is one of the subfields of machine learning, a classification was made to detect the defects that may occur in creating a layer of the process. Detection and classification of defects were carried out using the convolutional neural networks model. The data set for training and performance of the model was created with photographs of a three-dimensional sample structure manufactured on the EOS M290 machine. The best performance was obtained in the VGG-16 model with 86% accuracy by performing transfer learning from VGG-16, Inception V3, and DenseNet pre-learning models.

Anahtar Kelimeler

Additive Manufacturing, , Powder Bed Fusion, Deep Learning, Detection Defect, Transfer Learning

Kaynakça

• 1. Suat Y. A., Koc B., Yilmaz O. Building strategy effect on mechanical properties of high strength low alloy steel in wire+ arc additive manufacturing. Zavarivanje i zavarene konstrukcije, 65(3), 125-136, 2020.
• 2. Ertugrul I. The Fabrication Of Micro Beam From Photopolymer By Digital Light Processing 3d Printing Technology. Micromachines, 11(5), 518, 2020.
• 3. Sezer H., Eren O, Börklü H., Özdemir V. Additive manufacturing of carbon fiber reinforced plastic composites by fused deposition modelling: Effect of fiber content and process parameters on mechanical properties, 34(2), 663-674, 2019.
• 4. DebRoy T., Wei H.L., Zuback J.S., Mukherjee T., Elmer J.W., Milewski J.O., Beese A.M., Wilson-Heid A., De, A., Zhang W., Additive Manufacturing of Metallic Components – Process, Structure And Properties. Prog. Mater. Sci., 92, 112–224, 2018.
• 5. Liu S., Shin Y.C. Additive Manufacturing Of Ti6al4v Alloy: A Review. Mater. Des., 164, 107552, 2019.
• 6. Chen Y., Li T., Jia Z., Scarpa F., Yao C.W., Wang L. 3D Printed Hierarchical Honeycombs With Shape Integrity Under Large Compressive Deformations. Material Design. 137, 226–234, 2018.
• 7. OTAG, 2020. T.C Cumhurbaşkanlığı Savunma Sanayi Başkanlığı Eklemeli İmalat Teknolojileri Yol Haritalar Erişim Tarihi: 15.12.2020, https://arge.ssb.gov.tr/Documents/Eklemeli_Imalat%20_Teknolojileri_OTAG_Sonuc_Raporu.pdf
• 8. Poyraz Ö., Kuşhan M. C. Investigation of the effect of different process parameters for laser additive manufacturing of metals. Journal of the Faculty of Engineering and Architecture of Gazi University, 33(2), 729-742, 2018.
• 9. Sames WJ, List FA, Pannala S.The Metallurgy And Processing Science Of Metal Additive Manufacturing. Int Mater Rev 61(5), 315–360, 2016.
• 10. Tapia, G., Elwany, A. A Review On Process Monitoring And Control İn Metal-Based Additive Manufacturing. J Manuf Sci Eng, 136(6), 60801, 2014.
• 11. Clijsters S., Craeghs T., Buls S, In Situ Quality Control Of The Selective Laser Melting Process Using A High-Speed, Realtime Melt Pool Monitoring System. Int J Adv Manuf Technol, 75(5),1089–1101, 2014.
• 12. Doubenskaia M.A., Zhirnov I.V., Teleshevskiy V.I., Bertrand P., Smurov I.Y. Determination Of True Temperature İn Selective Laser Melting Of Metal Powder Using İnfrared Camera. Mater. Sci. Forum, 834,93-102, 2015.
• 13. Grasso M.V., Laguzza Q. Semeraro B.M. Colosimoın-Process Monitoring Of Selective Laser Melting: Spatial Detection Of Defects Via İmage Data Analysis. J. Manuf. Sci. Eng., 139 (5), 051001, 2017.
• 14. Kanko J.A., A.P. Sibley, J.M. Fraserın Situ Morphology-Based Defect Detection Of Selective Laser Melting Through Inline Coherent Imaging, J. Mater. Process. Technol., 231, 488-500, 2016.
• 15. Zhang B., J. Ziegert F. Farahi A. DaviesIn Situ Surface Topography Of Laser Powder Bed Fusion Using Fringe Projection Addit. Manuf., 12, 100-107, 2016.
• 16. Meng L., McWilliams B., Jarosinski W., Park H.Y., Jung Y.G., Lee J., Zhang J. Machine Learning in Additive Manufacturing: A Review. JOM, 1-15, 2020.
• 17. Everton S.K., Hirsch M., Stravroulakis, P. Review Of Insitu Process Monitoring And In-Situ Metrology For Metal Additive Manufacturing. Mater Des 95:431–445, 2016.
• 18. Fathizadan S. A Novel Real-Time Thermal Analysis and Layer Time Control Framework for Large-Scale Additive Manufacturing. Journal of Manufacturing Science and Engineering, 143.1, 2020.
• 19. Gobert C., Reutzel E.W., Petrich J., Nassar A.R., Phoha S. Application Of Supervised Machine Learning For Defect Detection During Metallic Powder Bed Fusion Additive Manufacturing Using High Resolution Imaging. Additive Manufacturing, 21, 517-528, 2018.
• 20. Scime L., Beuth J. Anomaly Detection And Classifcation In A Laser Powder Bed Additive Manufacturing Process Using A Trained Computer Vision Algorithm. Addit Manuf., 19, 114–126, 2018.
• 21. Okaro IA, Jayasinghe S, Sutcliffe C. Automatic Fault Detection For Laser Powder-Bed Fusion Using Semi-Supervised Machine Learning, Addit Manuf., 27, 42–53, 2019.
• 22. Shevchik SA, Kenel C, Leinenbach C. Acoustic Emission For İn Situ Quality Monitoring İn Additive Manufacturing Using Spectral Convolutional Neural Networks. Addit Manuf, 21, 598–604, 2018.
• 23. Ye D., Hong G.S., Zhang Y. Defect Detection In Selective Laser Melting Technology By Acoustic Signals With Deep Belief Networks. Int J Adv Manuf Technol, 96(5), 2791–2801, 2018.
• 24. Khanzadeh M, Chowdhury S, Marufuzzaman M. Porosity Prediction: Supervised-Learning Of Thermal History For Direct Laser Deposition. J Manuf Syst, 47, 69–82, 2018.
• 25. Baumgartl H., Tomas J., Buettner R., Merkel M. A Deep Learning-Based Model For Defect Detection In Laser-Powder Bed Fusion Using In-Situ Thermographic Monitoring. Progress in Additive Manufacturing, 5, 277-285, 2020.
• 26. Deng L., Yu D. Deep Learning: Methods and Applications. Foundations and Trends in Signal Processing, 7(3–4), 197–387, 2014.
• 27. Süzen AA., Duman B., Şen B. Benchmark Analysis of Jetson TX2, Jetson Nano and Raspberry PI using Deep-CNN. In 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), IEEE. 1-5, 2020.
• 28. LisaLab, 2020. Erişim Tarihi: 15.12.2020, https://github.com/lisa-lab/DeepLearningTutorials
• 29. Shrestha A., Mahmood A. Review Of Deep Learning Algorithms And Architectures. IEEE Access, 7, 53040-53065, 2019.
• 30. Aksoy B., Köse U. Optimization of real-time wireless sensor based big data with deep autoencoder network: a tourism sector application with distributed computing. Turkish Journal of Electrical Engineering and Computer Sciences, 28(6), 2020.
• 31. Hinton G.E., Salakhutdinov R.R. Reducing The Dimensionality Of Data With Neural Networks. Science, 313(5786), 504-507, 2006.
• 32. Krizhevsky A., Sutskever I., Hinton G.E. ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25 (2), 2012.
• 33. Taigman Y., Yang M., Ranzato M.A., Wolf L. Deepface: Closing The Gap To Human-Level Performance In Face Verification. In Proceedings of the IEEE Conference On Computer Vision And Pattern Recognition, 1701-1708, 2014.
• 34. Szegedy C., Liu W., Jia Y., Sermanet P., Reed S., Anguelov D., Rabinovich A. Going Deeper With Convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, 1-9, 2015.
• 35. Voulodimos A., Doulamis N., Doulamis A., Protopapadakis E. Deep Learning For Computer Vision: A Brief Review. Computational intelligence and neuroscience, Vol:2018,1-13, 2018.
• 36. Hatt M., Parmar C., Qi J., El Naqa I. Machine (Deep) Learning Methods For Image Processing And Radiomics. IEEE Transactions on Radiation and Plasma Medical Sciences, 3(2), 104-108, 2019.
• 37. Yang H., Luo L., Chueng L. P., Ling D., Chin F. Deep Learning And its Applications To Natural Language Processing. In Deep learning: Fundamentals, theory and applications, Springer, Cham, 89-109, 2019.
• 38. Mahdavifar S., Ghorbani A.A. Application of Deep Learning To Cybersecurity: A Survey, Neurocomputing, 347,149-176, 2019.
• 39. Süzen, A.A. Developing A Multi-Level Intrusion Detection System Using Hybrid-Dbn. Journal Of Ambient Intelligence And Humanized Computing.1-11, 2020.
• 40. Zemouri R., Zerhouni N., Racoceanu D. Deep Learning in The Biomedical Applications: Recent And Future Status. Applied Sciences, 9(8), 1526, 2019.
• 41. LeCun Y., Bengio Y., Hinton G. Deep Learning. Nature, 521, 436–444, 2015.
• 42. Karaali İ., Eminağaoğlu M Mermer işlemede kalite sınıflandırması için evrişimsel sinir ağı modeli. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 36(1), 347-358, 2021.
• 43. Analyticsspeps, 2020. Erişim Tarihi: 22.12.2020 https://www.analyticssteps.com/blogs/convolutional-neural-network-cnn-graphical-visualization-code-explanation
• 44. Hidaka A., Kurita T. Consecutive Dimensionality Reduction By Canonical Correlation Analysis For Visualization Of Convolutional Neural Networks. In Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications, 160-167. The ISCIE Symposium on Stochastic Systems Theory and Its Applications, 2017.
• 45. Jain G., Mittal D., Thakur D., Mittal M.K. A Deep Learning Approach To Detect Covid-19 Coronavirus With X-Ray Images. Biocybernetics and Biomedical Engineering, 40(4), 1391-1405, 2020.
• 46. Yıldız O. Derin öğrenme yöntemleriyle dermoskopi görüntülerinden melanom tespiti: Kapsamlı bir çalışma. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 34(4), 2241-2260, 2019.
• 47. Simonyan K., Zisserman A. Very Deep Convolutional Networks For Large-Scale İmage Recognition. Arxiv Preprint Arxiv:1409.1556, 2014.
• 48. Coşkun M., Yıldırım Ö., Uçar A., Demir Y. An Overview Of Popular Deep Learning Methods. European Journal of Technique(EJT), 7(2), 165-176, 2017.
• 49. Russakovsky O., Deng J., Su H., Krause J., Satheesh S., Ma S., Berg AC. Imagenet Large Scale Visual Recognition Challenge. International journal of computer vision, 115(3), 211-252, 2015.
• 50. Vgg16, 2020 Popular neteorks Erişim Tarihi: 15.12.2020, https://neurohive.io/en/popular-networks/vgg16/
• 51. Szegedy C., Vanhoucke V., Ioffe S., Shlens J., Wojna Z. Rethinking The Inception Architecture For Computer Vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2818-2826, 2016.
• 52. Huang G., Liu Z., Van Der Maaten L., Weinberger K.Q. Densely Connected Convolutional Networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, 4700-4708, 2017.
• 53. He K., Zhang X., Ren S., Sun J. Deep Residual Learning For Image Recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778, 2016.
• 54. Mardani R., Vasmehjani A.A., Zali F., Gholami A., Nasab S.D.M., Kaghazian H., Ahmadi N. Laboratory parameters in detection of COVID-19 patients with positive RT-PCR; a diagnostic accuracy study. Archives of academic emergency medicine, 8(1), 2020.