Akdulum, Aslan; Siyambaş, Yusuf

This study experimentally investigates the machinability of AISI 316L austenitic stainless steel under dry and air-assisted cooling during rough and finish milling. A high-feed milling cutter was employed for roughing and a solid carbide end mill for finishing. Cutting temperature, surface roughness, and tool wear were evaluated using thermal imaging, profilometry, and scanning electron microscopy (SEM) with energy dispersive X-ray spectroscopy (EDS). The results demonstrate that air-assisted cooling significantly reduces thermal loads and tool wear. Specifically, during rough milling, air cooling lowered average cutting temperatures by approximately 27% (from 150°C to 110°C) and maximum temperatures by 24% (from 267°C to 204°C). In finish milling, average and maximum temperatures decreased by 33% (from 194°C to 129°C) and 35% (from 272°C to 177°C), respectively. Surface quality improved notably under air cooling, with reductions in average roughness (Ra) by 21%, maximum height (Rz) by 35%, and total height (Rt) by 46%. SEM/EDS analyses revealed that air cooling minimized coating delamination, adhesion, and oxidative wear. A hybrid multi-criteria decision-making (MCDM) approach integrating the CRITIC and ARAS methods was applied to optimize the process parameters. The optimum condition – air cooling, 4375 rpm spindle speed, and 500 mm/min feed – achieved the highest relative utility (Ki = 0.895), balancing material removal rate, surface finish, and thermal performance. This study concludes that air-assisted cooling is a sustainable and effective strategy for enhancing surface integrity and tool life in the milling of difficult-to-machine materials such as AISI 316L.

Siyambaş, Yusuf; Turgut, Yakup

Machining of hardfacing-welded surfaces is a significant engineering challenge in reference to controlling cutting forces and achieving the desired surface quality. The heterogeneity and hardness variations in the material structure after welding can lead to fluctuating cutting forces and irregular surface roughness during the turning process. Therefore, optimizing cutting parameters, tool geometry, and cooling conditions is a critical requirement for enhancing process efficiency and improving the quality of the finished surface. It is important to use both experimental and analytical methods to model cutting forces and surface roughness in order to figure out what factors affect the process and what the best machining conditions are. This study examined the surface roughness (Ra) and cutting force (FR) produced when three different hardfacing welding metals with varying hardness levels were turned. Three different filler materials, three different feed rates, and three different cutting speeds were used in the machining experiments. The experimental results have demonstrated that the hardness of the filler metal significantly affects FR and Ra. The lowest FR values (66 N) were observed during the machining of the FCH 330 hardfacing material, while the lowest Ra values (0.35 µm) were obtained during the machining of the FCH 355 hardfacing material. Furthermore, the cutting speed of 180 m/min and the feed rate of 0.05 mm/rev produced the lowest values in both machining outputs. FR and Ra were optimized using the Taguchi-based MOORA method. As a consequence of the optimization, the optimal conditions were determined as the FCH 355 filler material, a feed rate of 0.05 mm/rev, and a cutting speed of 180 m/min. ANOVA analysis revealed that FR was affected by both the filler material and feed rate, while Ra was affected only by the feed rate. The developed models showed strong agreement with experimental data and provided accurate predictions.

Güneş, Mehmet D; İmamoğlu Karabaş, Neslişah; Deveci, Hamza A; Tanoğlu, Gamze; Tanoğlu, Metin

In this study, a new fatigue life prediction and optimization strategy utilizing the Failure Tensor Polynomial in Fatigue (FTPF) model with exponential fitting and numerical bisection method for fiber reinforced polymer composites has been proposed. Within the experimental stage, glass/epoxy composite laminates with (Formula presented.), (Formula presented.), and (Formula presented.) lay-up configurations were fabricated, quasi-static and fatigue mechanical behavior of GFRP composites was characterized to be used in the FTPF model. The prediction capability of the FTPF model was tested based on the experimental data obtained for multidirectional laminates of various composite materials. Fatigue life prediction results of the glass/epoxy laminates were found to be better as compared to those for the linear fitting predictions. The results also indicated that the approach with exponential fitting provides better fatigue life predictions as compared to those obtained by linear fitting, especially for glass/epoxy laminates. Moreover, an optimization study using the proposed methodology for fatigue life advancement of the glass/epoxy laminates was performed by a powerful hybrid algorithm, PSA/GPSA. So, two optimization scenarios including various loading configurations were considered. The optimization results exhibited that the optimized stacking sequences having maximized fatigue life can be obtained in various loading cases. It was also revealed that the tension-compression loading and the loadings involving shear loads are critical for fatigue, and further improvement in fatigue life may be achieved by designing only symmetric lay-ups instead of symmetric-balanced and diversification of fiber angles to be used in the optimization.