RESEARCH PAPER
A tool wear condition monitoring approach for end milling based on numerical simulation
1
College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, China
2
School of Mechatronics and Transportation, Jiaxing Nanyang Polytechnic Institute, Jiaxing, China
Publication date: 2021-06-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(2):371-380
HIGHLIGHTS
- A numerical simulation model is proposed to overcome sample missing and insufficiency.
- Model parameters are optimized by orthogonal experiment and KL divergence.
- The optimized model provide effectively missing samples and expand sample size.
- Experimental results show the proposed method improves notably the performance of TCM.
KEYWORDS
ABSTRACT
As an important research area of modern manufacturing, tool condition monitoring (TCM)
has attracted much attention, especially artificial intelligence (AI)- based TCM method.
However, the training samples obtained in practical experiments have the problem of sample
missing and sample insufficiency. A numerical simulation- based TCM method is proposed
to solve the above problem. First, a numerical model based on Johnson-Cook model is established, and the model parameters are optimized through orthogonal experiment technology,
in which the KL divergence and cosine similarity are used as the evaluation indexes. Second,
samples under various tool wear categories are obtained by the optimized numerical model
above to provide missing samples not present in the practical experiments and expand sample size. The effectiveness of the proposed method is verified by its application in end milling
TCM experiments. The results indicate the classification accuracies of four classifiers (SVM,
RF, DT, and GRNN) can be improved significantly by the proposed TCM method.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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