Katkı Üretiminde Evrişimsel Derin Öğrenme ile Kusur Tespiti

Öz

Eklemeli Üretim teknolojileri, karmaşık geometrik formlara sahip bileşenleri üretme kapasitesi, azaltılmış üretim giderleri, minimum malzeme kullanımı ve zaman verimliliği dahil olmak üzere çok çeşitli avantajlar sunar. Bu araştırma, çok ölçekli evrişimsel sinir ağlarını kullanan bir derin öğrenme modelinin geliştirilmesi yoluyla Erimiş Biriktirme Modelleme sürecindeki geometrik kusurların ve boyutsal düzensizliklerin yanı sıra yüzey kalitesi kusurlarının tespit edilmesi için önemli bir çaba oluşturmaktadır. Önerilen metodoloji, her biri farklı boyutlardaki kusurları tanımlayabilen üç farklı ölçeği kapsamaktadır. Model, çeşitli veri kümeleri aracılığıyla hassas bir şekilde eğitilebilmesi için kapsamlı hibridizasyon prosedürlerinden geçirildi ve eğitim süreci, istenen doğruluk düzeyine ulaşılıncaya kadar birçok kez tekrarlandı. Modelleri eğitmek için yeterince kapsamlı bir görüntü veri kümeleri kullanılır ve bu da ağın hassas kalibrasyonuna yol açar. Sonuç olarak, büyük ölçekli kusurları tespit etmek için uzun süreye ve karmaşık hesaplamalara duyulan ihtiyaç ortadan kalkar. Bu çalışmada kusur tespiti için en yüksek doğrulama doğruluğu %94'e ulaştı.

Anahtar Kelimeler

• Eklemeli Üretim
• Birleştirilmiş Biriktirme Modelleme
• Çok Ölçekli Evrişimsel Sinir Ağı
• Derin Öğrenme
• Kusur Tespiti.

Defects Detection at Additive Manufacturing by Convolutional Deep Learning

Abstract

Additive Manufacturing technologies present a wide array of benefits, including the capacity to manufacture components with complex geometric forms, reduced production expenses, minimized material usage, and time efficiency. This research constitutes a significant effort to pinpoint geometric defects and dimensional irregularities as well as surface quality imperfections in the Fused Deposition Modeling process through the development of a deep learning model utilizing multi-scale convolutional neural networks. The proposed methodology encompasses three distinct scales, each capable of identifying defects of varying dimensions. The model underwent extensive hybridizing procedures for precisely training through diverse datasets, and the training process is repeated numerous times until the desired level of accuracy was attained. A sufficiently extensive image datasets are employed to train the models, leading to the precise calibration of the network. As a result, the necessity for prolonged time and intricate computations to identify large-scale defects is eliminated. The highest validation accuracy for defect detection in this study reached 94%.

Keywords

• Additive Manufacturing
• Fused Deposition Modeling
• Multiscale Convolutional Neural Network
• Deep Learning
• Defect Detection

References

1. Alpaydın, E. (2014). Introduction to Machine Learning, (Adaptive Computation and Machine Learning). almohreraladbi.
2. Brion, D. A., & Pattinson, S. W. (2022). Generalisable 3D printing error detection and correction via multi-head neural networks. Nature communications, 13(1), 4654.
3. Bühlmann, P., & Van De Geer, S. (2011). Statistics for high-dimensional data: methods, theory and applications. Berlin, Germany: Springer Science & Business Media.
4. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20, 273-297.
5. Decker, N., & Huang, Q. (2019, June). Geometric accuracy prediction for additive manufacturing through machine learning of triangular mesh data. In International Manufacturing Science and Engineering Conference (Vol. 58745, p. V001T02A043). American Society of Mechanical Engineers.
6. Ero, O., Taherkhani, K., & Toyserkani, E. (2023). Optical tomography and machine learning for in-situ defects detection in laser powder bed fusion: A self-organizing map and U-Net based approach. Additive Manufacturing, 78, 103894.
7. Feng, W., Mao, Z., Yang, Y., Ma, H., Zhao, K., Qi, C., ... & Liu, S. (2022). Online defect detection method and system based on similarity of the temperature field in the melt pool. Additive Manufacturing, 54, 102760.
8. Francis, J., & Bian, L. (2019). Deep learning for distortion prediction in laser-based additive manufacturing using big data. Manufacturing Letters, 20, 10-14.

9. Gibson, I. G. (2015). Additive manufacturing technologies 3D printing, rapid prototyping, and direct digital manufacturing. 1–498. https://doi.org/10.1007/978-1-4939-2113-3/COVER.
10. Grasso, M., & Colosimo, B. M. (2017). Process defects and in situ monitoring methods in metal powder bed fusion: a review. Measurement Science and Technology, 28(4), 044005.
11. Gui, Y., Aoyagi, K., Bian, H., & Chiba, A. (2022). Detection, classification and prediction of internal defects from surface morphology data of metal parts fabricated by powder bed fusion type additive manufacturing using an electron beam. Additive Manufacturing, 54, 102736.
12. Gunasegaram, D. R., Barnard, A. S., Matthews, M. J., Jared, B. H., Andreaco, A. M., Bartsch, K., & Murphy, A. B. (2024). Machine learning-assisted in-situ adaptive strategies for the control of defects and anomalies in metal additive manufacturing. Additive Manufacturing, 104013.
13. Hsu, C. W., & Lin, C. J. (2002). A comparison of methods for multiclass support vector machines. IEEE transactions on Neural Networks, 13(2), 415-425.
14. Huang, C., Wang, G., Song, H., Li, R., & Zhang, H. (2022). Rapid surface defects detection in wire and arc additive manufacturing based on laser profilometer. Measurement, 189, 110503.
15. James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). Statistical learning. In An Introduction to Statistical Learning: with Applications in Python (pp. 15-67). Chamnitzer, Germany: Springer International Publishing.
16. Jin, Z., Zhang, Z., Ott, J., & Gu, G. X. (2021). Precise localization and semantic segmentation detection of printing conditions in fused filament fabrication technologies using machine learning. Additive Manufacturing, 37, 101696.
17. Kawalkar, R., Dubey, H. K., & Lokhande, S. P. (2022). A review for advancements in standardization for additive manufacturing. Materials Today: Proceedings, 50, 1983-1990.
18. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.
19. Li, R. (2022). Defect Detection for Additive Manufacturing with Machine Learning and Markov Decision Process.
20. Mahesh, M., Wong, Y. S., Fuh, J. Y. H., & Loh, H. T. (2004). Benchmarking for comparative evaluation of RP systems and processes. Rapid Prototyping Journal, 10(2), 123-135.
21. Minetola, P., Khandpur, M. S., Iuliano, L., Calignano, F., Galati, M., & Fontana, L. (2022). In-situ monitoring for open low-cost 3D printing. In Recent Advances in Manufacturing Engineering and Processes: Proceedings of ICMEP 2021 (pp. 49-56). Springer Singapore.
22. Patel, J. (2019). Data-Driven Modeling for Additive Manufacturing of Metals. Proceedings of a ... - National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences.
23. Petsiuk, A. L., & Pearce, J. M. (2020). Open source computer vision-based layer-wise 3D printing analysis. Additive Manufacturing, 36, 101473.
24. Petsiuk, A., & Pearce, J. M. (2022). Towards smart monitored AM: Open source in-situ layer-wise 3D printing image anomaly detection using histograms of oriented gradients and a physics-based rendering engine. Additive Manufacturing, 52, 102690.
25. Qi, X., Chen, G., Li, Y., Cheng, X., & Li, C. (2019). Applying neural-network-based machine learning to additive manufacturing: current applications, challenges, and future perspectives. Engineering, 5(4), 721-729.
26. Qin, J., Hu, F., Liu, Y., Witherell, P., Wang, C. C., Rosen, D. W., ... & Tang, Q. (2022). Research and application of machine learning for additive manufacturing. Additive Manufacturing, 52, 102691.
27. Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1, 81-106.
28. Westphal, E., & Seitz, H. (2021). A machine learning method for defect detection and visualization in selective laser sintering based on convolutional neural networks. Additive Manufacturing, 41, 101965.
29. Xu, X., Zuo, L., Li, X., Qian, L., Ren, J., & Sun, Z. (2018). A reinforcement learning approach to autonomous decision making of intelligent vehicles on highways. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(10), 3884-3897.