This research work relates to the soldering process observation in reflow ovens. The need for full continuous monitoring of reflow processes is more and more in the electronics technology. Today process monitoring of reflow soldering is based on temperature curve as a minimum requirement. A real time video observation functionality which allows control the flow of product through the reflow oven, this feature can make a very powerful control system. Based on this need for full process control, it has been developed a prototype system for visual monitoring of soldering process in an inline reflow oven during operation. The developed reflow monitoring system (called RMS) is able to pass through the reflow oven whilst observe and record video of solder joint formation on the printed circuit board assembly (PCBA). The RMS continuously can transfer data through WIFI connection to mobile phone as well.

There has been an increasing trend in using ceramics in the application areas where high hardness and thermal resistance are necessary, especially in energy production branch where gas turbines are coated with ceramics. The paper introduces experiments and results of ceramics machining using regular cutting tool geometry. Two machining strategies were analysed (wave form and cycloid), the number of critical process parameters were reduced significantly and remarkable tool life extension was reached using the test machine build by the authors.

This paper presents the results of numerical simulation, used for the welding technology development of a 13 m long chassis made of high strength steel. Finite element modeling aimed to describe the welding distortions, and the deformations resulted by using flame straightening of the much critical edges of the product. In FEM work SimuFact and MARC software were used. Physical simulation was also applied in order to validate the FEM results. Simulations focused mainly to the effect of the welding direction along the free displacement edges.

The paper presents the detailed analysis of the Radial Basis Function (RBF) neural network application for diagnostics of analog systems. In most cases RBF networks are used in the approximation tasks. In this work the network is used as the fault classifier. Because RBF networks are known to be dependent on the size of training data, the procedure to minimize the amount of the learning examples was proposed. The classifier is tested on the model of the electronic filter, which, was also diagnosed by alternative methods, such as Multilayered Perceptron and Support Vector Machine. Experimental results show what are advantages and drawbacks of the RBF classifier compared to other solutions.

In this paper we describe a proposal of distributed power control system and a demand estimation model for suppressing total power usage. The main components of the system are smart power outlets (SPOs) which can suppress the total power usage under the contract or saving target power and also can avoid excess circuit current to prevent a breaker trip by using mutual communication. The method to suppress the total power is by stochastic way incorporating standby rate, which is calculated by using an estimation model of total power demand. We implemented SPOs with defining communication format, and verified that the method is effective as a distributed power usage control system.