This paper presents a multi-objective optimization study for the micro-milling process with adaptive data modeling based on the process simulation. A micro-milling machining process model was developed and verified through our previous study. Based on the model, a set of simulation data was generated from a factorial design. The data was converted into a surrogate model with adaptive data modeling method. The model has three input variables: axial depth of cut, feed rate and spindle speed. It has two conflictive objectives: minimization of surface location error (which affects surface accuracy) and minimization of total tooling cost. The surrogate model is used in a multi-objective optimization study to obtain the Pareto optimal sets of machining parameters. The visual display of the non-dominated solution frontier allows an engineer to select a preferred machining parameter in order to get a lowest cost solution given the requirement from tolerance and accuracy. The contribution of this study is to provide a streamlined methodology to identify the preferred best machining parameters for micro-milling.
- Manufacturing Engineering Division
Multi-Objective Optimization for the Micro-Milling Process With Adaptive Data Modeling
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Liu, X, Zhu, W, & Zaloom, V. "Multi-Objective Optimization for the Micro-Milling Process With Adaptive Data Modeling." Proceedings of the ASME 2011 International Manufacturing Science and Engineering Conference. ASME 2011 International Manufacturing Science and Engineering Conference, Volume 2. Corvallis, Oregon, USA. June 13–17, 2011. pp. 365-372. ASME. https://doi.org/10.1115/MSEC2011-50144
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