Investigation of the machinability characteristics in turning of hardfacing welds: Modeling and optimization

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 (F R ) 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 F R and Ra. The lowest F R 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. F R 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 F R 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.