Laser cutting of steel is among the most advanced and competitive technologies in metal processing, widely employed in the automotive, aerospace, construction, and electronics industries, as well as in the production of precision components. Its advantages over traditional methods stem from its high precision, cutting speed, and ability to process complex geometries without requiring any subsequent finishing operations. This study presents research on the influence of fiber laser control parameters on the surface quality of S355J2C+N steel cuts. The primary objective is to identify which laser cutting parameters significantly affect the surface roughness of the cut. The response surface methodology (RSM) was employed for modeling. The Sq roughness parameter was selected as the primary metric to assess surface quality. The investigated process control parameters affecting surface roughness included feed rate, focal point position, and peak power. A 2-level split-plot experimental design was used for data analysis, with the focal point position designated as a hard-to-change factor due to the mechanical complexity of optical realignment. A mathematical model of the cutting process was generated. Based on analysis of variance (ANOVA), the model was found to fit the experimental data with an accuracy exceeding 98%. The results indicated that achieving low surface roughness (Sq) requires cutting with the lowest possible feed rate, lowest focal point position, and minimal peak power. The use of RSM facilitates the optimization of control parameter selection, contributing to improved surface quality and process efficiency.

This article presents research on the evaluation of the influence of various fiber laser parameters on the quality of the surface after the cutting process. The study was conducted using a 10 mm thick S355J2C+N steel sheet. The cutting process was performed on a Fiber Laser VFl530 cutting machine with a power of 4000 W, manufactured by Otinus. To develop a model of the laser cutting process, the response surface methodology (RSM) was applied. A second-degree polynomial equation was selected to construct the model. The study found that the parameter Rc exhibited a predicted coefficient of determination of 99.89% for the variance of laser settings in steel cutting, in relation to the roughness of the upper part of the intersection surface. The results demonstrated that the value of Rc is significantly influenced by cutting speed, the interaction between speed and peak power, the interaction between focus and speed, as well as the peak power parameter. Surface topography analysis revealed that as the laser beam power increased in relation to the cross-cutting speed, additional material melting occurred, leading to an increase in surface roughness. However, when the cross-cutting speed was increased while maintaining constant power, surface roughness decreased.

In this work, a mid infrared thermography was used to study thermal behavior of solid oxide fuel cell (SOFC) with a circular shape and a diameter of 90 mm. The emissivity of the anodic surface of the fuel cell was determined to be from 0.95 to 0.46 in the temperature range 550-1200 K and the profile and temperature distribution of the anodic surface of the unloaded cell was given. The surface temperature of the cell was determined during operation and the polarity changes from open circuit voltage (OCV) to 0.0 V. It was found that the cell self-heating effect decreases with increasing temperature of the cell.

This paper presents a dimensional analysis of die castings made from AlSi12Cu1(Fe) alloy. The machining process was optimized to minimize downtime and equipment usage while maintaining product quality and production efficiency. The castings, shaped as hydraulic valves, were produced using a four-cavity die-casting mold. Due to difficulties in achieving the required geometric dimensions, each casting from a specific cavity was machined on separate Computerized Numerical Control (CNC) machines. This work focuses on the dimensional analysis of castings from each mold cavity and the optimization of the production process. Based on the analysis, it was observed that the castings could be divided into two distinct groups: the first group contained castings that, after machining, exhibited similar measurement values and remained within the specified dimensional tolerances, while the second group failed to meet the required tolerances. As a result of the conducted analysis, a new machining strategy can be proposed—assigning castings from each group to different CNC machines. This would eliminate the need for frequent machine retooling and significantly reduce production downtime. The findings point to a potential solution for optimizing the machining process and improving overall manufacturing efficiency.