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Improving milling performances is an effective solution to decrease the costs required. This paper addressed a multi-response optimization to simultaneously decrease the machining power consumed Pm, arithmetical roughness Ra, and ten-spot roughness Rz. The Grey-Response Surface Method-Multi Island Genetic Algorithm (GRMA) consisting of grey relational analysis (GRA), response surface method (RSM), and multi-island genetic algorithm (MA) was proposed to predict the optimal parameters and yield optimum milling performances. The experimental trials were conducted with the support of a CNC milling center. The influences of spindle speed (S), depth of cut (ap), feed rate (fz), and tip radius (r) were explored using GRA. The nonlinear relationship between machining parameters and grey grade (GG) model was developed using RSM. Finally, two optimization techniques, including desirability approach (DA) and MA were performed to observe the optimal values. The results indicated that the machining power was greatly affected by processing factors and the radius has a significant impact on the roughness criteria. The measured reductions using optimal parameters of Pm, Ra, and Rz are approximately 77.05%, 50.00%, and 58.02%, respectively, as compared to initial settings. The GRMA can be considered as an effective approach to generate reliable values of processing conditions and technological performances in the milling process.

Wire arc additive manufacturing (WAAM) is nowadays gaining much attention from both the academic and industrial sectors for the manufacture of medium and large dimension metal parts because of its high deposition rate and low costs of equipment investment. In the literature, WAAM has been extensively investigated in terms of the shape and dimension accuracy of built parts. However, limited research has focused on the effects of welding parameters on the microstructural characteristics of parts manufactured by this process. In this paper, the effects of welding current in the WAAM process on the shape and the microstructure formation of built thin-walled low-carbon steel components were studied. For this purpose, the thin-walled low-carbon steel samples were built layer-by-layer on the substrates by using an industrial gas metal arc welding robot with different levels of welding current. The shape, microstructures and mechanical properties of built samples were then analyzed. The obtained results show that the welding current plays an important role in the shape stability, but does not significantly influence on the microstructure formation of built thin-walled samples. The increase of the welding current only leads to coarser grain size and resulting in decreasing the hardness of built materials in each zone of the built sample. The mechanical properties (hardness and tensile properties) of the WAAM-built thin-walled low-carbon steel parts are also comparable to those of wrought low-carbon steel, and to be adequate with real applications.

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