Application of Machine Learning Algorithms for Tool Condition Monitoring in Milling Chipboard Process.

In this article, we present a novel approach to tool condition monitoring in the chipboard milling process using machine learning algorithms. The presented study aims to address the challenges of detecting tool wear and predicting tool failure in real time, which can significantly improve the efficiency and productivity of the manufacturing process. A combination of feature engineering and machine learning techniques was applied in order to analyze 11 signals generated during the milling process. The presented approach achieved high accuracy in detecting tool wear and predicting tool failure, outperforming traditional methods. The final findings demonstrate the potential of machine learning algorithms in improving tool condition monitoring in the manufacturing industry. This study contributes to the growing body of research on the application of artificial intelligence in industrial processes. In conclusion, the presented research highlights the importance of adopting innovative approaches to address the challenges of tool condition monitoring in the manufacturing industry. The final results provide valuable insights for practitioners and researchers in the field of industrial automation and machine learning.

Improved Drill State Recognition during Milling Process Using Artificial Intelligence.

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.

Comparison Study of PVD Coatings: TiN/AlTiN, TiN and TiAlSiN Used in Wood Machining.

In this paper, we analyze the possibilities of the protection of tools for wood machining with PVD (Physical Vapor Deposition) hard coatings. The nanolayered TiN/AlTiN coating, nanocomposite TiAlSiN coatings, and single layer TiN coating were analyzed in order to use them for protection of tools for wood machining. Both nanostructured coatings were deposited in an industrial magnetron sputtering system on the cutting blades made of sintered carbide WC-Co, while TiN single layer coating was deposited by evaporation using thermionic arc. In the case of TiN/AlTiN nanolayer coatings the thickness of the individual TiN and AlTiN layer was in the 5-10 nm range, depending on the substrate vertical position. The microstructure and chemical composition of coatings were studied by scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) method. Additionally, in the case of the TiN/AlTiN coating, which was characterized by the best durability characteristics, the transmission electron microscope (TEM) and X-ray photoelectron spectroscopy (XPS) methods were applied. The coatings adhesion to the substrate was analyzed by scratch test method combined with optical microscopy. Nano-hardness and durability tests were performed with uncoated and coated blades using chipboard. The best results durability characteristics were observed for TiN/AlTiN nanolayered coating. Performance tests of knives protected with TiN and TiAlSiN hard coatings did not show significantly better results compared to uncoated ones.

Influence of Different Types of Cemented Carbide Blades and Coating Thickness on Structure and Properties of TiN/AlTiN and TiAlN/a-C:N Coatings Deposited by PVD Techniques for Machining of Wood-Based Materials.

The influence of different types of cemented carbide blades and thickness of TiAlN/a-C:N and TiN/AlTiN protective coatings used in the wood industry on cutting performance has been studied. Three types of WC-Co cemented carbide blades with different cobalt content were used in the study. The thicknesses of both types of coatings were ~2 and ~5 µm. The structure, chemical and phase composition were studied using transmission and scanning electron microscopy (TEM, SEM), X-ray dispersion spectroscopy (EDX) and X-ray diffraction (XRD), respectively. The adhesion was evaluated by scratch test. Nanohardness and durability tests of uncoated and coated blades were performed. We found that the blades covered with 5 µm TiN/AlTiN coatings exhibited the best durability characteristic. The cutting distances were within the range ~6700-~7080 depending on the substrates in comparison with pure substrates (~4300-~4900) and 2 µm TiN/AlTiN coatings (~5400-~6600). The presence of a thin and soft outer a-C:N layer aggravates the nanohardness and durability of the coated blades.