SCM440 steel is a commonly used material for making plastic injection molds and components such as gears, transmission shafts, rolling pins, etc. Surface roughness has a direct influence on the workability and durability of the parts and/or components, while the Material Removal Rate (MRR) is a parameter that is used to evaluate the productivity of the machining process. Furnished products with small surface roughness and large MRR is the desired result by all milling processes. In this paper, the determination of the values of input parameters is studied in order to ensure that during the process of milling SCM440 steel, it will have the smallest surface roughness and the largest MRR. There are five parameters that are required to be determined, namely the cutting insert material, the tool nose radius, the cutting speed, the feed rate, and the cutting depth. The Taguchi method was applied to design the experimental matrix with a total of 27 experiments. Result analysis determined the influence of the input parameters on surface roughness and MRR. The Data Envelopment Analysis-based Ranking (DEAR) method was applied to determine the optimal value of the input parameters, which were used to conduct the milling experiments to re-evaluate their suitability.

In this study, a milling experiment was performed, with 3x13 steel selected as the experimental material along with TiAlN coated inserts. The Box-Behnken method was used to design the experimental matrix with a total of eighteen experiments. Cutting speed, feed amount, and depth of cut were selected as the input parameters. Three regression models of surface roughness have been established, one using the experimentally measured surface texture, one using the Johnson transform to convert the surface texture data, and one using Box-Cox transformation to convert the surface texture data. A comparison of the accuracy of the three models was performed. The results show that the model using the Box-Cox transformation has the highest accuracy, followed by the model using the Johnson transformation. In addition, the influence of cutting parameters on surface roughness is also discussed in detail.

Milling is a commonly used method in mechanical machining. This is considered to be the method for the highest productivity among cutting methods. Moreover, the quality of the machined surface is increasingly improved as well as the machining productivity is increasingly enhanced thanks to the development of machine tool and cutting tool manufacturing technology. Therefore, in each specific processing condition (about machine, tool and part material, and other conditions), specific studies are required to determine the value of technological parameters in order to improve productivity and machining accuracy. Only in this way can we take full advantage of the capabilities of modern equipment. The process parameters in the milling method in particular and in the machining and cutting methods in general can be easily adjusted by the machine operator as the parameters of the cutting parameters or the change of tool types. In this article, the combination of Taguchi and Proximity Indexed Value (PIV) methods is presented for multi-criteria decision making in milling. An experimental matrix was designed according to Taguchi method with five input parameters, including the insert materials (TiN, TiCN, and TiAlN), nose radius, cutting velocity, feed rate and depth of cut. The total number of experiments that were performed was twenty-seven. The workpiece used during the experiment was SCM440 steel. At each experiment, the surface roughness was measured and the Material Removal Rate (MRR) was calculated. The weights of these two parameters have been chosen by the decision maker on the basis of consultation with experts. The PIV method was applied to determine the experiment at which the minimum surface roughness and the maximum MRR were simultaneously guaranteed. In addition, the influence of input parameters on surface roughness was also found in this study.

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