Diagnostic System of Drill Condition in Laminated Chipboard Drilling Process

Abstract

Abstract. The paper presents an on-line automatic system for recognition of the drill condition in a laminated chipboard drilling process. Two states of the drill are considered: the sharp enough (still able to drill holes acceptable for processing quality) and worn out (excessive drill wear, not satisfactory from the quality point of view of the process). The automatic system requires defining the diagnostic features, which are used as the input attributes to the classifier. The features have been generated from 5 registered signals: feed force, cutting torque, noise, vibration and acoustic emission. The statistical parameters defined on the basis of the auto regression model of these signals have been used as the diagnostic features. The sequential step-wise feature selection is applied for choosing the most discriminative set of features. The final step of recognition is done by support vector machine classifier working in leave one out mode. The results of numerical experiments have confirmed good quality of the proposed diagnostic system.

The Review of New Scientific Developments in Drilling in Wood-Based Panels with Particular Emphasis on the Latest Research Trends in Drill Condition Monitoring

“…A detailed overview of all the results of the scientific research obtained in the framework of the new approach to drill condition monitoring in wood-based panels machining [27][28][29][30][31][32][33][34][35][36][37][38] seems to be beyond the scope of the article. Besides, it is not necessary because all the original publications are widely available.…”

“…Generally, tool condition monitoring in the field of woodworking has also been popular for a long time [24][25][26]. Therefore, at the end of this introductory (and as concisely as possible) overview of the latest research trends, it is also worth noting the new and quite spectacular approach to drill condition monitoring in wood-based panels machining [27][28][29][30][31][32][33][34][35][36][37][38].…”

Investigation into the effects of machining parameters on the edge quality of particleboards - Modelling and Optimization

“…A most relevant characteristic of the vibration signal was the visible amplitude in the spectrum power for the narrow frequency band of this signal, associated with the rotational speed of the tool blades (600 Hz). Other researchers have examined methodical attempts to identify unambiguous and trustworthy views of tool conditions during the machining process and the characteristics of the most valuable signals (Świderski et al 2017;Kurek et al 2016). The work of Eschelbacher et al (2019) included the presentation of milling results for six distinct types of wood and wood-based materials.…”

Tool Wear Classification in Chipboard Milling Processes Using 1-D CNN and LSTM Based on Sequential Features

“…The literature has many studies on similar challenges [12,13], but the approach to using signals depends on the specific task. It is important to evaluate how useful the signals are in identifying tool conditions during different machining stages [3,[14][15][16][17][18]. Despite existing research, there is a need for an automated solution that can easily be integrated into production and offers both accuracy and ease of implementation.…”