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A New Method for Multi-objective Optimal Design of Milling Parameters by Considering Chatter Vibrations
ISSN: 0148-7191, e-ISSN: 2688-3627
Published May 13, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Event: Automotive Technical Papers
The desired milling process with high material removal rate (MRR) and low surface roughness of the product can be achieved only if machining chatter is absent. Incorporating chatter into the optimal selection of the machining parameters leads to a complex problem. Therefore, the approach of selecting conservative intervals for the machining parameters is usually employed instead. In this paper, a practical approach is proposed to specify the optimal machining parameters (depth of cut and spindle speed) in order to maximize MRR and minimize forced vibrations by considering machining chatter. Firstly, the worst-case scenario-based optimization problem in terms of the surface quality is solved to find the critical time at which maximal amplitude vibrations occur. Then, the time dependency of the problem is eliminated. Secondly, the multi-objective optimization is conducted to achieve the Pareto Optimal Front (POF). The Stability Lobe Diagram (SLD) is obtained independently through well-established analytical methods. Optimal machining parameters on the obtained POF are mapped into the SLD to represent optimal results for the cases at which machining chatter is absent. Finally, these optimal results are sorted by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision-making method and displayed on the combined POF-SLD diagram which can be used by the machining operator for determination of the process parameters. A case study is considered, illustrating the efficiency of the proposed method.
CitationJafarzadeh, E., Khodaygan, S., and Sohani, A., "A New Method for Multi-objective Optimal Design of Milling Parameters by Considering Chatter Vibrations," SAE Technical Paper 2019-01-5043, 2019, https://doi.org/10.4271/2019-01-5043.
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