A Hybrid Deep Learning Model as the Digital Twin of Ultra-Precision Diamond Cutting for In-Process Prediction of Cutting-Tool Wear
Source abstract
Abstract
Diamond cutting-tool wear has a direct impact on the processing accuracy of the machined surface in ultra-precision diamond cutting. It is difficult to monitor the tool’s condition because of the slight wear amount. This paper proposed a hybrid deep learning model for tool wear state prediction in ultra-precision diamond cutting. The cutting force was accurately estimated and the wear state of the diamond tool was predicted by using the hybrid deep learning model with the motion displacement, velocity, and other signals in the machining process. By carrying out machining experiments, this method can classify diamond-tool wear condition with an accuracy of more than 85%. Meanwhile, the effectiveness of the proposed method was verified by comparing it with a variety of machine learning models.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced machining processes and optimization · Tunneling and Rock Mechanics · Advanced Surface Polishing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
The State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
Funding
National Key R&D Program of China · Ministry of Industry and Information Technology’s Manufacturing High-quality Development Project · National Natural Science Foundation of China · Science Fund for Creative Research Groups of National Natural Science Foundation of China · Zhejiang Provincial Key R&D Program of China
References (29)
Show source-provided references
1. A review of machine-tool vibration and its influence upon surface generation in ultra-precision machining10.1016/j.ijmachtools.2015.01.005 ↗
2. A review of surface roughness generation in ultra-precision machining10.1016/j.ijmachtools.2015.02.001 ↗
3. Diamond tool wear in ultra-precision machining10.1007/s00170-016-8751-9 ↗
4. Tool wear classification using time series imaging and deep learning10.1007/s00170-019-04090-6 ↗
5. A review on deep learning in machining and tool monitoring: Methods, opportunities, and challenges10.1007/s00170-021-07325-7 ↗
6. Wu, D.Z., Jennings, C., Terpenny, J., and Kumara, S. (2016, January 5–8). Cloud-Based Machine Learning for Predictive Analytics: Tool Wear Prediction in Milling. Proceedings of the IEEE International Conference on Big Data (Big Data), Washington, DC, USA.10.1109/BigData.2016.7840831 ↗
7. On-line and indirect tool wear monitoring in turning with artificial neural networks: A review of more than a decade of research10.1006/mssp.2001.1460 ↗
8. Wu, L., Leng, J., and Ju, B. (2021). Digital Twins-Based Smart Design and Control of Ultra-Precision Machining: A Review. Symmetry, 13.10.3390/sym13091717 ↗
9. Nano precision on-machine profiling of curved diamond cutting tools using a white-light interferometer10.1504/IJSURFSE.2007.016695 ↗
10. Diamond Tool Wear in the Ultra-Precision Cutting of Large Electroless Nickel Coated Molding Dies10.20965/ijat.2011.p0283 ↗
11. Novel auto-regressive measurement of diamond tool wear in ultra-precision raster milling10.1007/s12541-012-0218-9 ↗
12. The relation between chip morphology and tool wear in ultra-precision raster milling10.1016/j.ijmachtools.2014.02.005 ↗
13. Nanometer edge profile measurement of diamond cutting tools by atomic force microscope with optical alignment sensor10.1016/j.precisioneng.2005.11.008 ↗
14. Diamond tool wear during ultra-high precision machining of rapidly solidified aluminium RSA 905
15. Performance of diamond inserts in ultra-high precision turning of Cu-Cr-Zr alloy
16. Tool life monitoring during the diamond turning of electroless Ni-P
17. Some observations on the wear of diamond tools in ultra-precision cutting of single-crystal silicon
18. Development of monitoring system on the diamond tool wear10.1016/S0890-6955(97)00076-X ↗
19. Analysis for the wear resistance anisotropy of diamond cutting tools in theory and experiment10.1016/j.jmatprotec.2010.01.018 ↗
20. Wear monitoring in turning operations using vibration and strain measurements10.1006/mssp.2000.1364 ↗
21. Tool wear monitoring in diamond turning by fuzzy pattern-recognition10.1115/1.2901934 ↗
22. Intelligent monitoring and diagnostics using a novel integrated model based on deep learning and multi-sensor feature fusion10.1016/j.measurement.2020.108086 ↗
23. Digital Twins-based Remote Semi-Physical Commissioning of Flow-Type Smart Manufacturing Systems10.1016/j.jclepro.2021.127278 ↗
24. Digital twins-based flexible operating of open architecture production line for individualized manufacturing10.1016/j.aei.2022.101676 ↗
25. Self-sensing of cutting forces in diamond cutting by utilizing a voice coil motor-driven fast tool servo10.1016/j.precisioneng.2021.03.009 ↗
26. Wang, Z.G., and Oates, T. (2015, January 25–31). Imaging Time-Series to Improve Classification and Imputation. Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI), Buenos Aires, Argentina.
27. Precision machining of microstructures on electroless-plated NiP surface for molding glass components10.1016/j.jmatprotec.2008.12.008 ↗
28. Cloud-edge orchestration-based bi-level autonomous process control for mass individualization of rapid printed circuit boards prototyping services10.1016/j.jmsy.2022.03.008 ↗
29. A loosely-coupled deep reinforcement learning approach for order acceptance decision of mass-individualized printed circuit board manufacturing in Industry 4.010.1016/j.jclepro.2020.124405 ↗