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.
Highlights
Computer numerical control (CNC) milling machines, which stable and efficient operation can produce huge economic value, are the most widely used automatic production equipment in modern manufacturing industry
support vector machine (SVM) are suitable for model training with small datasets, they are invalid for sample missing as samples associated with some tool wear conditions are often missing due to the complex conditions encountered in the machining process
This paper proposed a feasible tool condition monitoring (TCM) method for obtaining various samples of tool wear condition by numerical simulation based on J-C model to overcome the problem of sample missing and sample insufficiency in real experiments
Summary
Computer numerical control (CNC) milling machines, which stable and efficient operation can produce huge economic value, are the most widely used automatic production equipment in modern manufacturing industry. With the development of artificial intelligence (AI) algorithms, more and more scholars have applied AI algorithms in TCM, including support vector machine (SVM) [6,18], random forest (RF) [24, 32, 41], decision tree (DT) [3, 26], artificial neural network (ANN) [1, 9, 12, 22, 23, 28, 34] While these AI methods have yielded encouraging achievements in TCM applications, achieving good wear state prediction performance using these methods relies heavily on large datasets of monitoring signals that are associated with all possible tool wear conditions for model training [14, 45], which is costly and time-consuming for machining processes under different cutting conditions. The software DEFORM is used in this paper to simulate the end milling process and obtain the missing wear samples
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