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Technical Brief

Identification of the Actual Process Parameters for Finishing Operations in Peripheral Milling

[+] Author and Article Information
E. Leal-Muñoz

Departamento de Ingeniería Mecánica,
Universidad de La Frontera,
Temuco 4811230, Chile;
Departamento de Ingeniería Mecánica,
Universidad Politécnica de Madrid,
Madrid 28006, España

E. Diez

Departamento de Ingeniería Mecánica,
Universidad de La Frontera,
Temuco 4811230, Chile
e-mail: eduardo.diez@ufrontera.cl

H. Perez

Departamento de Ingenierías Mecánica,
Informática y Aeroespacial,
Universidad de León,
León 24071, España

A. Vizan

Departamento de Ingeniería Mecánica,
Universidad Politécnica de Madrid,
Madrid 28006, España

Manuscript received December 19, 2017; final manuscript received April 3, 2018; published online May 21, 2018. Assoc. Editor: Guillaume Fromentin.

J. Manuf. Sci. Eng 140(8), 084502 (May 21, 2018) (7 pages) Paper No: MANU-17-1792; doi: 10.1115/1.4039917 History: Received December 19, 2017; Revised April 03, 2018

The evolution of the manufacturing industry has favored the use of new technologies that increase the level of autonomy in production systems. The work presented shows a methodology that allows for online estimation of cutting parameters based on the analysis of the cutting force signal pattern. The dynamic response of the tool is taken into account through a function that relates the response time to the input variables in the process. The force signal is obtained with a dynamometric platform based on piezoelectric sensors. The final section of the paper shows the experimental validation where machining tests with variable machining conditions were carried out. The results reveal high precision in the estimation of depths of cut in end milling.

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References

Liu, Y. , and Xu, X. , 2017, “Industry 4.0 and Cloud Manufacturing: A Comparative Analysis,” ASME J. Manuf. Sci. Eng., 139(3), p. 034701. [CrossRef]
Esmaeilian, B. , Behdad, S. , and Wang, B. , 2016, “The Evolution and Future of Manufacturing: A Review,” J. Manuf. Syst., 39, pp. 79–100. [CrossRef]
Wu, D. , Jennings, C. , Terpenny, J. , Kumara, S. , and Gao, R. X. , 2018, “Cloud-Based Parallel Machine Learning for Tool Wear Prediction,” ASME J. Manuf. Sci. Eng., 140(4), p. 041005. [CrossRef]
Singh, K. K. , Karkit, V. , and Singh, R. , 2017, “Modeling of Dynamic Instability Via Segmented Cutting Coefficients and Chatter Onset Detection in High-Speed Micromilling of Ti6Al4V,” ASME J. Manuf. Sci. Eng., 139(5), p. 051005. [CrossRef]
Giorgio Bort, C. M. , Leonesio, M. , and Bosetti, P. , 2016, “A Model-Based Adaptive Controller for Chatter Mitigation and Productivity Enhancement in CNC Milling Machines,” Robot. Comput. Integr. Manuf., 40, pp. 34–43. [CrossRef]
Mansour, S. Z. , and Seethaler, R. , 2017, “Feedrate Optimization for Computer Numerically Controlled Machine Tools Using Modeled and Measured Process Constraints,” ASME J. Manuf. Sci. Eng., 139(1), p. 011012 . [CrossRef]
Zhao, Z. Y. , Wang, C. Y. , Zhou, H. M. , and Qin, Z. , 2007, “Pocketing Toolpath Optimization for Sharp Corners,” J. Mater. Process. Technol., 192–193, pp. 175–180. [CrossRef]
Tajima, S. , and Sencer, B. , 2016, “Kinematic Corner Smoothing for High Speed Machine Tools,” Int. J. Mach. Tools Manuf., 108, pp. 27–43. [CrossRef]
Huang, N. , Lynn, R. , and Kurfess, T. , 2017, “Aggressive Spiral Toolpaths for Pocket Machining Based on Medial Axis Transformation,” ASME J. Manuf. Sci. Eng., 139(5), p. 051011. [CrossRef]
Otkur, M. , and Lazoglu, I. , 2007, “Trochoidal Milling,” Int. J. Mach. Tools Manuf., 47(9), pp. 1324–1332. [CrossRef]
Shixiong, W. , Wei, M. , Bin, L. , and Chengyong, W. , 2016, “Trochoidal Machining for the High-Speed Milling of Pockets,” J. Mater. Process. Technol., 233, pp. 29–43. [CrossRef]
Yan, R. , Li, H. , Peng, F. , Tang, X. , Xu, J. , and Zeng, H. , 2017, “Stability Prediction and Step Optimization of Trochoidal Milling,” ASME J. Manuf. Sci. Eng., 139(9), p. 091006. [CrossRef]
Altintas, Y. , and Yellowley, I. , 1987, “The Identification of Radial Width and Axial Depth of Cut in Peripheral Milling,” Int. J. Mach. Tools Manuf., 27(3), pp. 367–381. [CrossRef]
Tarn, J. H. , and Tomizuka, M. , 1989, “On-Line Monitoring of Tool and Cutting Conditions in Milling,” ASME J. Eng. Ind., 111(3), pp. 206–212.
Choi, J.-G. , and Yang, M.-Y. , 1999, “In-Process Prediction of Cutting Depths in End Milling,” Int. J. Mach. Tools Manuf., 39(5), pp. 705–721. [CrossRef]
Hwang, J. H. , Oh, Y. T. , Kwon, W. T. , and Chu, C. N. , 2003, “In-Process Estimation of Radial Immersion Ratio in Face Milling Using Cutting Force,” Int. J. Adv. Manuf. Technol., 22(3–4), pp. 313–320. [CrossRef]
Yang, L. , DeVor, R. E. , and Kapoor, S. G. , 2005, “Analysis of Force Shape Characteristics and Detection of Depth-of-Cut Variations in End Milling,” ASME J. Manuf. Sci. Eng., 127(3), pp. 454–462. [CrossRef]
Tarng, Y. S. , and Shyur, Y. Y. , 1993, “Identification of Radial Depth of Cut in Numerical Control Pocketing Routines,” Int. J. Mach. Tools Manuf., 33(1), pp. 1–11. [CrossRef]
Prickett, P. W. , Siddiqui, R. A. , and Grosvenor, R. I. , 2011, “The Development of an End-Milling Process Depth of Cut Monitoring System,” Int. J. Adv. Manuf. Technol., 52(1–4), pp. 89–100. [CrossRef]
Gaja, H. , and Liou, F. , 2016, “Automatic Detection of Depth of Cut During End Milling Operation Using Acoustic Emission Sensor,” Int. J. Adv. Manuf. Technol., 86(9–12), pp. 2913–2925. [CrossRef]

Figures

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Fig. 1

(a) Diagram of the cutting operation, (b) advance of the cutting flute, (c) measured force signal, and (d) characteristic time instants

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Fig. 2

Time parameters defined on a simulated force

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Fig. 3

Comparison between measured and simulated signals

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Fig. 4

Diagram of the system dynamics

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Fig. 5

Evolution of retard time for different speeds: (a) ae and (b) ap (fz = 0.12 mm; D = 10 mm)

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Fig. 6

Diagram for time calculation from the cutting force signal

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Fig. 7

Results for variable ap experiment: (a) workpiece, (b) force signal, and (c) estimated values (D = 10 mm, fz = 0.80 mm, ap = variable 5–13 mm, ae = 0.5 mm, n = 2400 rpm, workpiece material AA7075.)

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Fig. 8

Results for variable ap and ae experiment: (a) workpiece, (b) force signal, (c) scale expansion of force signal, (d) estimated values of ap and (e) estimated values of ae. (D = 8 mm; fz = 0.12 mm, ap = variable 8–16 mm, ae= variable 0.3–1 mm, n = 1200 rpm, workpiece material AA7075).

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