The weight of gears is an important issue for high performance of the machine elements. Thus, the design of the gear pairs with respect to the minimum weight is an important and attractive subject to study. Various analytical and heuristic approaches were utilized to optimize the weight of gears in previous studies. In this study, the problem of gear optimization as per the minimum weight was solved with Grey Wolf Optimizer (GWO). GWO is a meta-heuristic optimization method that stands out with its successful performance in engineering applications. In this study, the performance of GWO was investigated in the case of constituting a spur gear with respect to minimum weight for the first time. Compared with the previous studies, the results obtained in the study show that it is possible to design a lighter gear with GWO.

This study aims to investigate the mechanical properties of Functionally Graded Lattice Structures (FGLSs) and to determine their industrial application possibilities through additive manufacturing. For this purpose, lattice structures with uniform and horizontal, vertical and radially graded configurations are designed using auxetic unit cells were fabricated with RGD720 photopolymer resin using Material Jetting. FGLSs are compared with uniform structures in regards with deformation behavior, structural strength and energy absorption. The results showed that the most significant deviation in the strut diameters of the uniform lattice structures was seen in the rotation lattice structure at 8.2%. The lowest deviation was seen in the chiral structure, which deviated by 5.4%. The lowest deviations (between 3.4% and 9%) in FGLSs were obtained in chiral structures. The highest relative density value (0.3049 g/cm³) among all configurations was observed in the vertically graded chiral structure. The lowest relative density value (0.1865 g/cm³) was obtained in uniform re-entrant structures. According to the compression test results, the highest compressive stress (2.61513 MPa) and elastic modulus (84.63192 MPa) were formed in the rotation structure. The maximum energy absorption capacity value (19.381 KJ) and the maximum specific energy absorption value (3649.905 KJ/kg) were obtained in the uniform chiral structure.

Stainless steels with unique corrosion resistance are used in applications with a wide range of fields, especially in the medical, food, and chemical sectors, to maritime and nuclear power plants. The low heat conduction coefficient and the high mechanical properties make the workability of stainless steel materials difficult and cause these materials to be in the class of hard-to-process materials. In this study, suitable cutting tools and cutting parameters were determined by the Taguchi method taking surface roughness and cutting tool wear into milling of Custom 450 martensitic stainless steel. Four different carbide cutting tools, with 40, 80, 120, and 160 m/min cutting speeds and 0.05, 0.1, 0.15, and 0.2 mm/rev feed rates, were selected as cutting parameters for the experiments. Surface roughness values and cutting tool wear amount were determined as a result of the empirical studies. ANOVA was performed to determine the significance levels of the cutting parameters on the measured values. According to ANOVA, while the most effective cutting parameter on surface roughness was the feed rate (% 50.38), the cutting speed (% 81.15) for tool wear was calculated.

The need for precise mechanical and tribological properties of the hydrostatic bearings has made them an interesting study topic for optimisation studies. In this paper, power-loss minimization problems of hydrostatic thrust bearings were solved through Grey Wolf Optimizer (GWO). Grey Wolf Optimizer is a meta-heuristic optimization method standing out with its successful applications in engineering design problems. Power-loss minimization problem of hydrostatic thrust bearings was applied on Grey Wolf Optimizer (GWO) for the first time. The results obtained were evaluated together with the previous studies conducted and a detailed comparison was made. The most significant innovation of the study is the innovation made in the mathematical model of the GWO. A new model (Enhanced GWO, EGWO) that increases the variety of valid solutions is proposed. The comparisons made both with GWO and other studies in the literature show that EGWO got the known best fitness value with the highest success rate. The consistency and statistical performance of the EGWO show that this method can be used in the optimization of machine elements.