Robustness is an important success factor for production networks in which the operation of enterprises is subjected to an uncertain environment. In this paper, the robustness of networks is studied as a function of network size. The study is performed through a simulation experiment in which the uncertain environment is modelled by introducing perturbations in demand. The decision-making model mimics the behaviour of socially connected human subjects. The results show how robustness and production rate are affected by system size and social network structure, and how this is relevant for the design and operation of future manufacturing systems.
COBISS.SI-ID: 13971739
In applications digital image correlation based algorithms often present a basis for analysis of move- ment/deformation of bodies. The sequence of the obtained images is analyzed for this purpose. Especially, in cases when the body's movement/deformation between two successive images is signi fi cant, the initial guess can have a major in fl uence on the execution speed of the algorithm. In the worst case it can even cause the divergence of the algorithm. This was the inspiration to develop a new and unique approach for an accurate and reliable determination of an initial guess for each image pixel. Kalman fi lter has been used for this purpose. It uses past measurements of observed variable(s) for calculations. Beside that it also incorporates state space model of the actual system. This is one of the most important advantages provided by Kalman fi lter. The determined initial guess by the proposed method is actually close to the true one and it enables fast convergence. Even more important property of this approach is the fact that it is not path-dependant because each image pixel, which is de fi ned in ROI, is tracked through the sequence of images based on its own past measurements and general state space model. Consequently, the proposed method can be used to analyze tasks where discontinuities between image pixels are present. The applied method can be used to predict an initial guess where reference and deformed subsets are related by translational and rotational motion. The advantages mentioned above are veri fi ed with numerical and real experiments. The experimental validations are performed by NR (Newton % Raphson) approach which is the most widely used. Beside NR method the presented algorithm is applicable for other registration methods as well. It is used as an addition for calculation of initial guesses in a sequence of deformed images.
COBISS.SI-ID: 14176283
In this study we determine the optimal parameters for surface modification using the laser surface melting of powder-metallurgy processed, vanadium-rich, cold-work tool steel. A combination of steel pre-heating, laser surface melting and a subsequent heat treatment creates a hardened and morphologically modified surface of the selected high-alloy tool steel. The pre-heating of the steel prior to the laser surface melting ensures a crack- and pore-free modified surface. Using a pre-heating temperature of 350 °C, the extremely fine microstructure, which typically evolves during the laser-melting, became slightly coarser and the volume fraction of retained austenite was reduced. In the laser-melted layer the highest values of microhardness were achieved in the specimens where a subsequent heat treatment at 550 °C was applied. The performed thermodynamic calculations were able to provide a very valuable assessment of the liquidus temperature and, especially, a prediction of the chemical composition as well as the precipitation and dissolution sequence for the carbides.
COBISS.SI-ID: 13845275
We report on a concept of a fiber MOPA based quasi-CW laser working at high modulation bandwidths up to 40 MHz capable of producing arbitrary pulse durations at arbitrary repetition rates. An output power of over 100 W was achieved and an on-off contrast of 25 dB. The laser features a dual-channel (dual-wavelength) seed source, a double stage YDF amplifier and a volume-Bragg-grating-based signal de-multiplexer. Minimization of transients was conducted through experiment and model analysis.
COBISS.SI-ID: 14385947
We implemented monitoring of injection molding process of polymer materials with acoustic emission (AE) sensors. For tool integrity prediction frequency analysis and a measure of AE signal amplitude probability distribution is implemented. Research offered us connection of multidimensional feature vectors with individual process steps and tool integrity, based on pattern recognition and neural network techniques. Research also proved usefulness of less expensive resonant sensors for monitoring of injection molding process.
COBISS.SI-ID: 20356405