Friction and wear experiments were performed on carbon fiber-reinforced polymer (CFRP) composites, and the tribological behavior of these materials under boundary lubrication (based on the 5100 4T 10 W-30 engine oil with TiO₂ Degussa P25 nanoparticles) was investigated. Experiments were carried out in two directions: one at a different normal load from 6 to 16 N and one at a low sliding speed of 110 mm/min under boundary lubrication conditions. The obtained results reveal the stick-slip effect and the static and dynamic coefficient of friction decreased slightly with increasing normal applied load on the carbon fiber reinforced polymer composite pairs. The second direction highlights through experimental tests on the pin on disc tribometer that the friction coefficient increases with the increase in normal load (20–80 N) and sliding velocity (0.4–2.4 m/s). On the other hand, it is found that the friction coefficient is slightly lower than in the stick-slip phase. During the running-in process, the friction coefficient of the CFRP pair increases steadily as the rubbing time increases, and after a certain rubbing period, it remains constant regardless of the material of the counter face. The obtained results show that for the observed interval, the influence of normal load and sliding velocity have relatively small fraction coefficients and low wear depths. A 3D analysis of the profile demonstrated the texture of wear marks and tracks of these engineering composite materials. Furthermore, the height variations of wear marks and the morphologies of the worn surfaces of specimens under boundary lubrication conditions were analyzed.

This study explores the experimental and theoretical optimization of process parameters to improve the quality of 3D-printed parts produced using the Fused Deposition Modeling technique. To ensure the cost-effective production of high-quality components, advancements in printing strategies are essential. This research identifies optimal 3D printing strategies to enhance the quality of finished products. Form and dimensional tolerances were assessed using a 3D Coordinate Measuring Machine, and the resulting data were analyzed via Design Expert software version 9.0.6.2. Design Expert for experimental design was utilized and an Analysis of Variance was conducted to validate the models’ accuracy. The results indicate that a 45° raster angle, combined with internal raster values between 0.5048 and 0.726, minimizes flatness, cylindricity, and dimensional deviations by optimizing deposition patterns and thermal dynamics. Internal raster values below 0.308 resulted in insufficient support and greater deviations, while higher values enhanced stability through improved interlayer adhesion. Experimental validation confirmed these parameter settings as optimal for producing precise and consistent 3D-printed parts.

Artificial intelligence (AI) is becoming more and more integrated into manufacturing processes, revolutionizing conventional production, like CNC (Computer Numerical Control) machining. This study analyzes how large language models (LLMs), exemplified by ChatGPT, behave when tasked with G-code optimization for improving surface quality and productivity of automotive metal parts, with emphasis on systematically documenting failure modes and limitations that emerge when general-purpose AI encounters specialized manufacturing domains. Even if software programming remains essential for highly regulated sectors, free AI tools will be increasingly used due to advantages like cost-effectiveness, adaptability, and continuous innovation. The condition is that there is sufficient technical expertise available in-house. The experiment carried out involved milling three identical parts using a Haas VF-3 SS CNC machine. The G-code was generated by SolidCam and was optimized using ChatGPT considering user-specified criteria. The aim was to improve the quality of the part’s surface, as well as increase productivity. The measurements were performed using an ISR C-300 Portable Surface Roughness Tester and Axiom Too 3D measuring equipment. The experiment revealed that while AI-generated code achieved a 37% reduction in cycle time (from 2.39 to 1.45 min) and significantly improved surface roughness (Ra—arithmetic mean deviation of the evaluated profile—decreased from 0.68 µm to 0.11 µm—an 84% improvement), it critically eliminated the pocket-milling operation, resulting in a non-conforming part. The AI optimization also removed essential safety features including tool length compensation (G43/H codes) and return-to-safe-position commands (G28), which required manual intervention to prevent tool breakage and part damage. Critical analysis revealed that ChatGPT failures stemmed from three factors: (1) token-minimization bias in LLM training leading to removal of the longest code block (31% of total code), (2) lack of semantic understanding of machining geometry, and (3) absence of manufacturing safety constraints in the AI model. This study demonstrates that current free AI tools like ChatGPT can identify optimization opportunities but lack the contextual understanding and manufacturing safety protocols necessary for autonomous CNC programming in production environments, highlighting both the potential, but also the limitation, of free AI software for CNC programming.