Parameter Selection to Ensure Multi-Criteria Optimization of the Taguchi Method Combined with the Data Envelopment Analysis-based Ranking Method when Milling SCM440 Steel


  • N. V. Cuong Faculty of Mechanical Engineering, University of Transport and Communications, Vietnam
  • N. L. Khanh Faculty of Mechanical Engineering, University of Transport and Communications, Vietnam
Volume: 11 | Issue: 5 | Pages: 7551-7557 | October 2021 |


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.


milling, SCM440 steel, surface roughness, MRR, optimization, Taguchi, DEAR


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How to Cite

N. V. Cuong and N. L. Khanh, “Parameter Selection to Ensure Multi-Criteria Optimization of the Taguchi Method Combined with the Data Envelopment Analysis-based Ranking Method when Milling SCM440 Steel ”, Eng. Technol. Appl. Sci. Res., vol. 11, no. 5, pp. 7551–7557, Oct. 2021.


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