The efficient use of municipal waste is becoming increasingly important for sustainable environmental quality. An integral part of the process of recycling waste material is its quality sorting. Nowadays, both manual and automated sorting methods, especially optical sorting, are used worldwide for sorting mixed waste. This article presents the application of the NIR/VIS optical sorting system through a description of this processing equipment integrated into a specific plastic waste processing line. The aim of the article was to select the optimal mode of operation generally applicable to mixed waste, and, specifically, to compare the quality of sorting of plastic waste in automated and manual sorting modes. In order to compare the efficiency of the sorting system, significant parameters of the sorting process were proposed. The measurement of the plastic waste sorting purity parameter was based on data obtained by weighing selected control samples, and the evaluation of the results of these measurements showed, in particular, the dependence of the concerned parameter not only on the sorting mode, i.e., automated and manual, but also on the conveyor belt speed/waste material flow, on the pre-sorting methods and, in the case of manual sorting, on the operating shift. The subsequent economic assessment of both sorting processes clearly declared the investment in automated sorting systems in the optical system operation mode not only as profitable, but also as extremely advantageous both for the production economy and the environment.

Austenitic stainless steel belongs to the best oxidation-resistant alloys, which must function effectively and reliably when used in a corrosion environment. Their attractive combination of properties ensures their stable position in the steel industry. They belong to a group of difficult-to-cut materials, and the abrasive water jet cutting technology is often used for their processing. Samples made of stainless steel AISI 304 has been used as the experimental material. Data generated during experiments were used to study the effects of AWJ process parameters (high-pressure water volume flow rate, the diameter of the abrasive nozzle, the distance of the nozzle from the material surface, cutting head feed rate, abrasive mass flow, and material thickness) on surface roughness. Based on the analysis and interpretation of all data, a prediction model was created. The main goal of the long-term research was to create the simplest and most usable prediction model for the group of austenitic steels, based on the evaluation of the practical results obtained in the company Watting Ltd. (Budovateľská 3598/38, Prešov, Slovakia) during 20 years of operation and cooperation with customers from industrial practice. Based on specific customer requirements from practice, the publication also contains specific recommendations for practice and a proposal for the classification of the predicted cut quality.

The aim of the paper is to examine the requirements of producers in post-communist countries with lower economic level. The first requirement was how to overcome the limitations of conventional PFMEA to propose measures effectively. The second requirement solved the economic effect of failure modes. The aim of the paper was to create a new hybrid PFMEA–DEMATEL–ERPN model to manage failure modes to resolve the requirements. The DEMATEL model overcame the limitations of PFMEA. DEMATEL data were used to estimate the functionality of the proposed models. Criteria such as the occurrence of defective products and the probability of their occurrence (O and RPN) were monitored. ERPN also overcame the limitations of PFMEA. Internal and external costs arise as effects of failure modes. The costs were included in the economic evaluation of the models. We validated the models in a transfer pressing process. The estimation of models’ functionality proved to be correct. The economic evaluation refined the research results and resolved the second requirement of the manufacturers. The DEMATEL and ERPN models (compared to PFMEA) proved their validity when the use of PFMEA was limited. By using DEMATEL, we registered the lowest number of defective products and the lowest costs.

The production of parts by pressing and subsequent welding is commonly used in the automotive industry. The disadvantage of this method of production is that inaccuracies arising during pressing significantly affect the final dimension of the part. However, this can be corrected by the choice of the technological parameters of the following operation—welding. Suitably designed parameters make it possible to partially eliminate inaccuracies arising during pressing and thus increase the overall applicability of this technology. The paper is focused on the upper arm geometry of a car produced in this manner. There have been two neural networks proposed in which the optimal welding parameters are determined based on the stamped dimensions and the desired final dimensions. The Levenberg–Marquardt back-propagation algorithm and the Bayesian regularised back-propagation algorithm were used as the learning algorithm for ANNs in multi-layer feed-forward networks. The outputs obtained from the neural networks were compared with a linear prediction model based on a on the design of experiment methodology. The mean absolute percentage error of the linear regression model on the entire dataset was 3 × 10⁻³%. A neural network with Levenberg–Marquardt back-propagation learning algorithm had a mean absolute percentage error of 4 × 10⁻³. Similarly, a neural network with a Bayesian regularised back-propagation learning algorithm had a mean absolute percentage error of 3 × 10⁻³%.