QUANTITATIVE ASSESSMENT AND CHARACTERIZATION OF TOOL WEAR PHENOMENA IN ADVANCED MANUFACTURING PROCESSES

TUYBOYOV OYBEK VALIJONOVICH

Admin bo'lib kirish (www.admin.slib.uz)

Tizim sinov (TEST) rejimida ishlamoqda! Murojat uchun @slib_support

Eski talqinga o'tish link

24 апрел 2024

Asosiy til:Ingliz

QUANTITATIVE ASSESSMENT AND CHARACTERIZATION OF TOOL WEAR PHENOMENA IN ADVANCED MANUFACTURING PROCESSES

Fan yo'nalishi:

6628d3cde5636.pdf

PDF

Technical science and innovation

Maqola chop etish see more

MAQOLA ANNOTATSIYASI

This paper explores the quantitative assessment and characterization of tool wear phenomena in advanced manufacturing processes, employing a multifaceted approach encompassing traditional measurements, image processing, machine learning, and predictive modeling. The study emphasizes the intricate dynamics of tool wear and its direct impact on cutting tool performance, addressing challenges in real-time monitoring and optimization of machining operations. Traditional methods like VBmax measurement are juxtaposed with advanced techniques such as the improved conditional generative adversarial net with a high-quality optimization algorithm (CGAN-HQOA), efficient channel attention destruction and construction learning (ECADCL), and shape descriptors based on contour, moments, orientations, and texture. Artificial intelligence algorithms, including support vector machine (SVM), random forest (RF), decision tree (DT), and artificial neural network (ANN), are applied for tool condition monitoring. A novel wear stage division-based tool wear prediction method (WSDTWP) utilizing symmetrized dot pattern (SDP) and multi-covariance Gaussian process regression (MCGPR) is proposed for enhanced predictive accuracy. Results are presented through visualizations, including 3D surface plots, providing insights into the relationships between cutting conditions and various wear parameters. The discussion underscores the pivotal role of tool wear assessment in optimizing manufacturing efficiency, while the findings contribute to a holistic understanding of tool wear dynamics.

MUALIFLAR

Science ID: FNV-0825-0007

Teglar

# image processing# Tool wear# advanced manufacturing processes# VBmax# machine learning# predictive modeling# CGAN-HQOA# ECADCL

Maqolani baholang

0

0 ta

Maqola idintifikatorlari

ROI:

https://eroi.uz/11.0008/A0424-0022

DOI:

Mavjud emas

Foydalanilgan adabiyotlar

1. Jaffery, S. I., & Mativenga, P. T. (2009). Assessment of the machinability of Ti-6Al-4V alloy using the wear map approach. The International Journal of Advanced Manufacturing Technology, 40, 687-696. 2. Hrechuk, A., Bushlya, V., Ståhl, J. E., & Kryzhanivskyy, V. (2021). Novel metric “Implenarity” for characterization of shape and defectiveness: the case of CFRP hole quality. Composite Structures, 265, 113722. 3. Li, C., Xu, J., & Chen, M. (2023). Quantitative evaluation method of tool wear based on morphological characteristics of machined surfaces. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 237(1-2), 81-90. 4. Sultan, A. Z., Sharif, S., & Kurniawan, D. (2015). Effect of machining parameters on tool wear and hole quality of AISI 316L stainless steel in conventional drilling. Procedia Manufacturing, 2, 202-207.

5. Shankar, S., Mohanraj, T., & Rajasekar, R. (2019). Prediction of cutting tool wear during milling process using artificial intelligence techniques. International Journal of Computer Integrated Manufacturing, 32(2), 174-182. 6. Čuš, F., & Župerl, U. (2011). Real-time cutting tool condition monitoring in milling. Strojniški vestnikJournal of Mechanical Engineering, 57(2), 142-150. 7. Chuangwen, X., Jianming, D., Yuzhen, C., Huaiyuan, L., Zhicheng, S., & Jing, X. (2018). The relationships between cutting parameters, tool wear, cutting force and vibration. Advances in Mechanical Engineering, 10(1), 1687814017750434. 8. Chiadamrong, N. (2003). The development of an economic quality cost model. Total Quality Management & Business Excellence, 14(9), 999-1014. 9. Luna, G. G., Axinte, D., & Novovic, D. (2020). Influence of grit geometry and fibre orientation on the abrasive material removal mechanisms of SiC/SiC Ceramic Matrix Composites (CMCs). International Journal of Machine Tools and Manufacture, 157, 103580. 10. Astakhov, V. P. (2007). Effects of the cutting feed, depth of cut, and workpiece (bore) diameter on the tool wear rate. The International Journal of Advanced Manufacturing Technology, 34, 631-640.

11. Silva, R. G., Wilcox, S. J., & Reuben, R. L. (2006). Development of a system for monitoring tool wear using artificial intelligence techniques. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 220(8), 1333-1346. 12. Yang, J., Duan, J., Li, T., Hu, C., Liang, J., & Shi, T. (2022). Tool wear monitoring in milling based on finegrained image classification of machined surface images. Sensors, 22(21), 8416. 13. Fillot, N., Iordanoff, I., & Berthier, Y. (2007). Wear modeling and the third body concept. Wear, 262(7- 8), 949-957. 14. Zhou, Y., & Sun, W. (2020). Tool wear condition monitoring in milling process based on current sensors. IEEE Access, 8, 95491-95502. 15. Kong, D., Chen, Y., & Li, N. (2018). Gaussian process regression for tool wear prediction. Mechanical systems and signal processing, 104, 556-574.

16. Paro, J., Hänninen, H., & Kauppinen, V. (2001). Tool wear and machinability of X5 CrMnN 18 18 stainless steels. Journal of Materials Processing Technology, 119(1- 3), 14-20. 17. Hrechuk, A., Bushlya, V., Ståhl, J. E., & Kryzhanivskyy, V. (2021). Novel metric “Implenarity” for characterization of shape and defectiveness: the case of CFRP hole quality. Composite Structures, 265, 113722. 18. Sun, J., Rahman, M., Wong, Y. S., & Hong, G. S. (2004). Multiclassification of tool wear with support vector machine by manufacturing loss consideration. International Journal of Machine Tools and Manufacture, 44(11), 1179-1187. 19. Khanna, N., Agrawal, C., Gupta, M. K., & Song, Q. (2020). Tool wear and hole quality evaluation in cryogenic Drilling of Inconel 718 superalloy. Tribology International, 143, 106084.

20. Yang, J., Duan, J., Li, T., Hu, C., Liang, J., & Shi, T. (2022). Tool wear monitoring in milling based on finegrained image classification of machined surface images. Sensors, 22(21), 8416. 21. Benardos, P. G., & Vosniakos, G. C. (2003). Predicting surface roughness in machining: a review. International journal of machine tools and manufacture, 43(8), 833-844.