Abstract. The paper presents an automatic approach to recognition of the drill condition in a standard laminated chipboard drilling process. The state of the drill is classified into two classes: "useful" (sharp enough) and "useless" (worn out). The case "useless" indicates symptoms of excessive drill wear, unsatisfactory from the point of view of furniture processing quality. On the other hand the "useful" state identifies tools which are still able to drill holes acceptable due to the required processing quality. The main problem in this task is to choose an appropriate set of diagnostic features (variables), based on which the recognition of drill state ("useful" versus "useless") can be made. The features have been generated based on 5 registered signals: feed force, cutting torque, noise, vibration and acoustic emission. Different statistical parameters describing these signals and also their Fourier and wavelet representations have been used for defining the features. Sequential feature selection is applied to detect the most class discriminative set of features. The final step of recognition is done by using three types of classifiers, including support vector machine, ensemble of decision trees and random forest. Six standard drills of 12 mm diameter with tungsten carbide tips were used in experiments. The results have confirmed good quality of the proposed diagnostic system. Key words: diagnostic expert systems, neural networks, wavelet packets, wear monitoring.Developing automatic recognition system of drill wear in standard laminated chipboard drilling process [5,7].The key issue in diagnostics of cutting tool state is the selection of sensors. Usually force sensors, electrical power, acoustic emission, vibration and acoustic pressure are applied in TCM [4,8]. The best results in metal working are achieved using force sensors.The disadvantage of this kind of measurement in normal production is that the force or torque transducers are relatively expensive and there are difficulties in mounting these sensors to the cutting tool or work piece. Therefore vibration sensors -easy to install, but at the same time less accurate because of background noise -are most commonly used in wood industry [5]. Vibration measurement is easy, since an accelerometer can be mounted close to the spindle bearing and no modifications of machine tools are needed [9].Quite similar to vibration is the sound signal, which can also be used in assessment of drill state. Mechanical vibration of the cutting tool, machine holder and drill are partly transferred to airborne vibration. It means that part of the information contained in vibration can also be obtained from sound measurement. The acquisition of the sound can be done easily using a microphone. However, the sound pressure sensors (microphones), are even more susceptible to background [7,8]. The sound measurement in an extended range of frequencies from 20 kHz to 80 kHz is usually applied [4]. This range is called the acoustic emission. The effectiveness of acoustic emission...

An analytical, two‐dimensional, stationary model of heat flow in edge‐emitting laser is revisited. In the work, we show how to use this approach to be able to calculate the temperature of the entire device including the most susceptible for thermal runaway region, namely the vicinity of the joint of mirrors and an active layer. Numerical tools based on our considerations are implemented in Matlab code and available at the software developer's web site. A procedure of investigation of surface recombination at facets with the help of our model combined with mirror temperature measurements is proposed. To get an insight in reasonableness of our theoretical research, we calculated temperature profiles and surface recombination velocities for example devices and successfully confronted them with results found in the literature.

The following article presents the results on the impact of encryption algorithms and the cryptographic hash function on the QoS (Quality of Service) transmission in a computer network. A network model supporting data encryption using the AES algorithm and the MD5 and SHA hash functions used in VPN tunnels was designed and tested. The influence of different data length on the quality of transmission in a secured network was studied. The measurements and tests of networks were performed according to two methodologies ITU-T Y.1564 and RFC 2544. The impact of the data encryption mechanism on bandwidth, data loss and maximum delays was examined. The secured network tests were performed with different combinations of encryption algorithms and hash functions of the VPN tunnel in the ESP (Encapsulating Security Payload) transport mode.

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