This paper presents an improved method for recognizing the drill state on the basis of hole images drilled in a laminated chipboard, using convolutional neural network (CNN) and data augmentation techniques. Three classes were used to describe the drill state: red - for drill that is worn out and should be replaced, yellow - for state in which the system should send a warning to the operator, indicating that this element should be checked manually, and green - denoting the drill that is still in good condition, which allows for further use in the production process. The presented method combines the advantages of transfer learning and data augmentation methods to improve the accuracy of the received evaluations. In contrast to the classical deep learning methods, transfer learning requires much smaller training data sets to achieve acceptable results. At the same time, data augmentation customized for drill wear recognition makes it possible to expand the original dataset and to improve the overall accuracy. The experiments performed have confirmed the suitability of the presented approach to accurate class recognition in the given problem, even while using a small original dataset.

Recently, many studies have been conducted on the impact of various elements of the natural environment, including forests, on human physical and mental health. However, little is known about the level of health benefits resulting from contact with forests depending on the type of physical activity undertaken. Therefore, in order to measure the impact of physical activity on the level of mental relaxation, a randomized experiment was conducted, which took into account three types of human physical activity: walking, cycling, and passive (without movement) observation of the forest. The study was carried out in the same forest and at the same time. Forty young people studying in Warsaw took part in the study. Four psychological questionnaires were used in the project before and after the experiment (Profile of Mood States, Schedule of Positive and Negative Affects, Recovery Scale, Subjective Vitality Scale). A pre-test was also performed in a university classroom. Research has shown that staying in the forest, regardless of the type of physical activity, brings positive health benefits in the form of an increase in positive feelings while reducing negative feelings. The results indicate that people who walk have the broadest range of benefits (cumulative benefits), in the form of less tension, reduced anger, fatigue, depression, increased concentration and greater vigor. Cyclists experienced significant benefits only in the form of reduced depression and greater vigor. The group passively observing the forest achieved statistically significant benefits only in terms of reducing fatigue and improving concentration. However, overall, the between-group results showed no statistically significant differences between the restorative effects of walking, cycling, and viewing the forest landscape. Each analyzed form of contact with the forest has a regenerating/regenerating effect (ROS scale) and contributes to the increase in vitality (SVS scale).

In this paper we introduce the enhanced drill wear recognition method, based on classifiers ensemble, obtained using transfer learning and data augmentation methods. Red, green and yellow classes are used to describe the current drill state. The first one corresponds to the case when drill should be immediately replaced. The second one denotes a tool that is still in a good condition. The final class refers to the case when a drill is suspected of being worn out, and a human expert evaluation would be required. The proposed algorithm uses three different, pretrained network models and adjusts them to the drill wear classification problem. To ensure satisfactory results, each of the methods used was required to achieve accuracy above 90% for the given classification task. Final evaluation is achieved by voting of all three classifiers. Since the initial data set was small (242 instances), the data augmentation method was used to artificially increase the total number of drill hole images. The experiments performed confirmed that the presented approach can achieve high accuracy, even with such a limited set of training data.

The main goal of this paper was to examine whether image processing can be an effective tool in automatic quality control systems for furniture manufacturing, at least in cases where delamination is a key problem. Image processing turned out to be a very effective way of delamination factor monitoring. Moreover, the research analyzed the quality effect of up or down milling of melamine-faced MDF. The delamination factor increased significantly with increasing tool wear. However, the increase was not very regular. The experimental data showed that up milling had a significant advantage over down milling.

Drilling into melamine-faced-wood-based panels is one of the most common processes in modern furniture manufacturing. Delamination is usually the main and the most troublesome quality defect in this case. A lot of scientific studies draw the conclusion that the progress of tool wearing during the cutting of wood-based materials is the key problem. Therefore, tool condition monitoring and the replacement of worn tools at the right time is the most useful and common (in the industrial practice) way to reduce delamination. However, the automation of this process is still a problem due to various issues. There is yet no commercial (even prototypical) offer for the furniture industry in this regard. For this reason, it is considered advisable to try to use the multilayer perceptron (MLP) algorithm to automatically identify a drill’s condition during drilling in a laminated chipboard. It has been established that, for practical purposes, it is important to distinguish between the three different classes of tool conditions, which can be conventionally described as “Green” (keep working), “Red” (implicitly stop and replace) and “Yellow” (warning signal—stop and replace if you want to avoid deterioration in cutting quality). To register the signals generated in the cutting zone and those constituting the basis for the identification of the tool condition in the “on-line” mode, the following elements were used: contact sensor of acoustic emission, accelerometer for vibration, two-component force gauge and a microphone. The classification effects (with an overall accuracy above 70%) were ultimately fairly decent but slightly worse than those of the classification algorithms tested earlier (i.e., “nearest neighbors” or “support vector machine” algorithms). The most troublesome, however, is the fact that serious errors (mistakes between “Green” and “Red” classes) were occasionally noted (for about 1% of the analyzed cases).

Drilling is still one of the basic cutting processes that are of particular interest to wood science and technology professionals. As a result, considerable (and very diverse thematically) research has been recently carried out on drilling. The article focuses on the new and quite spectacular approach to drill condition monitoring in wood-based panels machining. One of the most innovative elements in the analyzed research trend is the adoption of the new general methodological assumptions that allow one to define the drill condition monitoring problem as a standard three-class classification. The general effectiveness of the tested monitoring systems (accuracy of classification ACC [%]), ranged between 67% and 82%. The critical classification error (CCE [%]) ranged between 0% and 1.6%. These results seem very promising, yet are still not good enough to develop a commercial monitoring system. A more useful form of obtaining diagnostic data and more effective classification strategies (algorithms) are likely to be required.