Plunge milling time optimization via mixed-integer nonlinear programming

Abstract : Plunge milling is a recent and efficient production mean for machining deep workpieces, notably in aeronautics. This paper focuses on the minimization of the machining time by optimizing the values of the cutting parameters. Currently, neither Computer-Aided Manufacturing (CAM) software nor standard approaches take into account the tool path geometry and the control laws driving the tool displacements to propose optimal cutting parameter values, despite their significant impact. This paper contributes to plunge milling optimization through a Mixed-Integer NonLinear Programming (MINLP) approach, which enables us to determine optimal cutting parameter values that evolve along the tool path. It involves both continuous (cutting speed, feed per tooth) and, in contrast with standard approaches, integer (number of plunges) optimization variables, as well as nonlinear constraints. These constraints are related to the Computer Numerical Control (CNC) machine tool and to the cutting tool, taking into account the control laws. Computational results, validated on CNC machines and on representative test cases of engine housing, show that our methodology outperforms standard industrial engineering know-how approaches by up to 55% in terms of machining time.
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Sonia Cafieri, Frederic Monies, Marcel Mongeau, Christian Bes. Plunge milling time optimization via mixed-integer nonlinear programming. Computers and Industrial Engineering, Elsevier, 2016, 98, pp.434-445. ⟨10.1016/j.cie.2016.06.015⟩. ⟨hal-01337391⟩

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