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Nitriding is usually used to improve the surface properties of steel materials. In this way, the wear resistance of steels is improved. We conducted a series of studies in order to investigate the microstructural, mechanical and tribological properties of salt bath nitrided AISI 4140 steel. The present study has two parts. For the first phase, the tribological behavior of the AISI 4140 steel which was nitrided in sulfinuz salt bath (SBN) was compared to the behavior of the same steel which was untreated. After surface characterization using metallography, microhardness and sliding wear tests were performed on a block-on-cylinder machine in which carbonized AISI 52100 steel discs were used as the counter face. For the examined AISI 4140 steel samples with and without surface treatment, the evolution of both the friction coefficient and of the wear behavior were determined under various loads, at different sliding velocities and a total sliding distance of 1000 m. The test results showed that wear resistance increased with the nitriding process, friction coefficient decreased due to the sulfur in salt bath and friction coefficient depended systematically on surface hardness. For the second part of this study, four artificial neural network (ANN) models were designed to predict the weight loss and friction coefficient of the nitrided and unnitrided AISI 4140 steel. Load, velocity and sliding distance were used as input. Back-propagation algorithm was chosen for training the ANN. Statistical measurements of R², MAE and RMSE were employed to evaluate the success of the systems. The results showed that all the systems produced successful results.

Electrical discharge machining (EDM) of a stack allows achieving high precision and quality of cut surfaces and, therefore, this method is indispensable for state-of-the-art mechanical engineering. The procedure of EDM is carried out with wire-cutting machines. The characterization of constructive parameters of a stack of material and applying of an efficient cutting regime are the most important preconditions providing high precision of EDM. The goal of this work is the improvement of quality and efficiency of wire electrical discharge machining (WEDM) technology by theoretical and experimental studies of the WEDM process. The subsequent development of theoretical and empirical models allowing for the calculation of the quality parameters of treated surfaces is realized. It is shown that the main characteristics of cut surfaces are the roughness, size precision, error profile and structure of a surface layer. For the first time, the regression dependencies between the main parameters of the WEDM process (pulse $on$-time $t_{\mathrm{\text{on}}}$, off-time $t_{\mathrm{\text{off}}}$, the height of the stack and the physicomechanical properties of the cut materials are obtained. The experimental study of WEDM confirms the results of mathematical modeling. It is proved experimentally that at an interlayer gap higher than 0.1mm, the cutting process stability is decreasing whereas the probability of the electrode fracture is increasing. However, it is found that at $t_{\mathrm{\text{on}}}=21\mu$s and $t_{\mathrm{\text{off}}}=60\mu$s, a stable cutting regime leading to bundling of the stock materials made from steel 65Γ can be realized.

This paper aims to develop a predictive model and optimize the performance of the abrasive water jet machining (AWJM) during machining of carbon fiber-reinforced plastic (CFRP) epoxy laminates composite through a unique approach of artificial neural network (ANN) linked with the nondominated sorting genetic algorithm-II (NSGA-II). Initially, 80 AWJM experimental runs were carried out to generate the data set to train and test the ANN model. During the experimentation, the stand-off distance (SOD), water pressure, traverse speed and abrasive mass flow rate (AMFR) were selected as input AWJM variables and the average surface roughness and kerf width were considered as response variables. The established ANN model predicted the response variable with mean square error of 0.0027. Finally, the ANN coupled NSGA-II algorithm was applied to determine the optimum AWJM input parameters combinations based on multiple objectives.

This research investigates the impact of dry machining AA4015/B4C MMCs (metal matrix composites) utilizing an uncoated B4C insert on Flank wear (VBc) and surface roughness (Ra). This work attempts to close the knowledge gap on the effects of cutting parameters (feed, depth of cut, and speed) on tool wear and surface quality in machining technology. The primary rationale lies in optimizing machining processes for MMCs, known for their challenging machinability due to their tough metallic matrix and hard ceramic reinforcement. The study’s significance is underscored by the exploration of optimal processing parameters to minimize Flank wear and improve surface roughness, crucial factors influencing component quality and lifespan. Specifically, the research identifies v1-f1-d3 (VBc) and v3-f1-d3 (Ra) as the best process parameter combinations, significantly reducing both VBc and Ra. The obtained mathematical models for VBc and Ra provide statistically significant insights into the relationships between cutting variables and performance characteristics. The employment of Taguchi’s L9 orthogonal array proves invaluable in achieving these optimized process parameters efficiently. The Taguchi method’s advantage lies in its ability to systematically explore numerous variables and their interactions with minimal experiments. By reducing the number of trials required, this methodology streamlines the optimization process, saving time, resources, and costs while delivering enhanced machining performance for MMCs. This research, through its systematic approach and emphasis on optimized parameters, contributes to the advancement of machining techniques for MMCs, holding implications for various industrial applications demanding high-performance materials.