The purpose of this study is to realize the multi-objective optimization for MRR and surface roughness in micro-milling of Inconel 718.
Taguchi method has been applied to conduct experiments, and the cutting parameters are spindle speed, feed per tooth and depth of cut. The first-order models used to predict surface roughness and MRR for micro-milling of Inconel 718 have been developed by regression analysis. Genetic algorithm has been utilized to implement multi-objective optimization between surface roughness and MRR for micro-milling of Inconel 718.
This paper implemented the multi-objective optimization between surface roughness and MRR for micro-milling of Inconel 718. And some conclusions can be summarized. Depth of cut is the major cutting parameter influencing surface roughness. Feed per tooth is the major cutting parameter influencing MRR. A number of cutting parameters have been obtained along with the set of pareto optimal solu-tions of MRR and surface roughness in micro-milling of Inconel 718.
There are a lot of cutting parameters affecting surface roughness and MRR in micro-milling, such as tool diameter, depth of cut, feed per tooth, spindle speed and workpiece material, etc. However, to the best our knowledge, there are no published literatures about the multi-objective optimization of surface roughness and MRR in micro-milling of Inconel 718.
The research is supported by the National Natural Science Foundation of China under Grant No. 51875080 and the Fundamental Research Fund for the Central Universities under project number: DUT17JC22. The financial contributions are gratefully acknowledged.
Lu, X., Wang, F., Xue, L., Feng, Y. and Liang, S.Y. (2019), "Investigation of material removal rate and surface roughness using multi-objective optimization for micro-milling of inconel 718", Industrial Lubrication and Tribology, Vol. 71 No. 6, pp. 787-794. https://doi.org/10.1108/ILT-07-2018-0259
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