Improved Drill State Recognition during Milling Process Using Artificial Intelligence

Abstract

In this article, an automated method for tool condition monitoring is presented. When producing items in large quantities, pointing out the exact time when the element needs to be exchanged is crucial. If performed too early, the operator gets rid of a good drill, also resulting in production downtime increase if this operation is repeated too often. On the other hand, continuing production with a worn tool might result in a poor-quality product and financial loss for the manufacturer. In the presented approach, drill wear is classified using three states representing decreasing quality: green, yellow and red. A series of signals were collected as training data for the classification algorithms. Measurements were saved in separate data sets with corresponding time windows. A total of ten methods were evaluated in terms of overall accuracy and the number of misclassification errors. Three solutions obtained an acceptable accuracy rate above 85%. Algorithms were able to assign states without the most undesirable red-green and green-red errors. The best results were achieved by the Extreme Gradient Boosting algorithm. This approach achieved an overall accuracy of 93.33%, and the only misclassification was the yellow sample assigned as green. The presented solution achieves good results and can be applied in industry applications related to tool condition monitoring.

Tool State Recognition During Milling Process using Time Series Signals and Multiple Input CNN Architecture.

“…In summary, this multiple input CNN architecture is a deep learning model with 11 input branches, each containing a series of Convolutional, Batch Normalization, ReLU, and MaxPooling layers. These branches are then combined using a DepthConcatenationLayer and followed by additional Convolutional, Continuing the assumption based on previous research made in chosen subject (Kurek et al [2023]), when connecting different classifiers into ensemble and using voting method to obtain final classification improved overall solution, similar approach was used for final solution presented in this paper. While initial experiments obtained some acceptable results, there were still not satisfactory, and additional work to improve overall results was required.…”

Combining Artificial Neural Network and Response Surface Methodology to Optimize the Drilling Operating Parameters of MDF Panels

“…To optimize the drilling process, considerable research was carried out on drilling to increase the hole quality, which in most cases is classified by delamination factor and surface roughness [2]. Moreover, topics such as cutting force, drill deflection, and tool condition monitoring are presented in the literature [2][3][4]. Cutting forces affect the energy consumption, tool wear, and the quality of the surface [2].…”

“…The adhesive layer negatively influences the accuracy of the position and the angle of the holes during plywood drilling [3]. Kurek et al [4] proposed a new approach that can be used to predict the drill bit condition during drilling of wood and wood-based materials. The approach is based on the physical parameters of the drilling system, namely, noise levels, current/voltage values, and vibrations.…”

“…Ahmed et al [31] used acoustic emissions and a residual network (ResNets), which is a specific type of neural network, to monitor the tool health during the milling of wood. Kurek et al [4] classified drill wear by means of classification algorithms (random forest RF, decision tree, support vector machine SVM, extreme gradient boosting XGBoost, etc.). The best result was obtained in the case of the XGBoost extreme gradient boosting classification algorithms.…”