Technical Brief

Determination of Force Parameters for Milling Simulations by Combining Optimization and Simulation Techniques

[+] Author and Article Information
Dennis Freiburg

Institute of Machining Technology,
TU Dortmund University,
Baroper Straße 303,
Dortmund 44227, Germany
e-mail: freiburg@isf.de

Rouven Hense

Institute of Machining Technology,
TU Dortmund University,
Baroper Straße 303,
Dortmund 44227, Germany
e-mail: hense@isf.de

Petra Kersting

Junior Professor
Institute of Machining Technology,
TU Dortmund University,
Baroper Straße 303,
Dortmund 44227, Germany;
Institute of Process Technology and Quality Management,
Otto von Guericke University Magdeburg,
Magdeburg 39106, Germany
e-mail: petra.kersting@isf.de

Dirk Biermann

Institute of Machining Technology,
TU Dortmund University,
Baroper Straße 303,
Dortmund 44227, Germany
e-mail: biermann@isf.de

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received May 13, 2015; final manuscript received August 11, 2015; published online October 27, 2015. Assoc. Editor: Tony Schmitz.

J. Manuf. Sci. Eng 138(4), 044502 (Oct 27, 2015) (6 pages) Paper No: MANU-15-1229; doi: 10.1115/1.4031336 History: Received May 13, 2015; Revised August 11, 2015

Milling is a machining process in which material removal occurs due to the rotary motion of a cutting tool relative to a typically stationary workpiece. In modern machining centers, up to and exceeding six degrees of freedom for motion relative to the tool and workpiece are possible, which results in a very complex chip and force formation. For the process layout, simulations can be used to calculate the occurring process forces, which are needed, e.g., for the prediction of surface errors of the workpiece, or for tool wear and process optimization examinations. One limiting factor for the quality of simulation results is the parametrization of the models. The most important parameters for milling simulations are the ones that calibrate the force model, as nearly every modeled process characteristic depends on the forces. This article presents the combination of a milling simulation with the Broyden–Fletcher–Goldfarb–Shanno (BFGS) optimization algorithm for the fast determination of force parameters that are valid for a wide range of process parameters. Experiments were conducted to measure the process forces during milling with different process parameters. The measured forces serve as basis for tests regarding the quality of the determined force parameters. The effect of the tool runout on the optimization result is also discussed, as this may have significant influence on the forces when using tools with more than one tooth. The article ends with a conclusion, in which some notes about the practical application of the algorithm are given.

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Fig. 4

Simulated forces Fx (solid line), Fy (dashed line), and Fz (dotted line) with different SNRs. Force parameters: kc = 600 N/mm2, mc = 0.2, kn = 300 N/mm2, mn = 0.4, kt = 400 N/mm2, mt = 0.1.

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Fig. 3

Experimental setup in the machine tool (a) and process parameters used for the force measurements (b)

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Fig. 2

Flow chart of simulation and optimization process

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Fig. 7

Simulated (solid line) and measured (dashed line) force Fx with a runout error of r=0.007 mm

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Fig. 1

Schema of the force calculation used in the milling simulation. Left: Milling tool with casted rays. View A: Discrete cutting edges of the tool depending on the casted rays. View B: Workpiece with cross section of undeformed chip (width of cut ae=0.5·d).

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Fig. 5

Boxplot showing the influence of the number of engagements used for the optimization

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Fig. 6

Comparison of measured forces (solid line) of experiment two with simulated forces with force parameters determined with experiments one (dashed line) and three (dotted line)




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