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Surface roughness monitoring application based on artificial neural networks for ball-end milling operations

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Abstract

Surface roughness plays an important role in the performance of a finished part. The roughness is usually measured off-line when the part is already machined, although in recent years the trend seems to have been to focus on online monitoring. Measuring and controlling the machining process is now possible thanks to improvements and advances in the fields of computers and sensors. The aim of this work was to develop a reliable surface roughness monitoring application based on an artificial neural network approach for vertical high speed milling operations. Experimentation was carried out to obtain data that was used to train the artificial neural network. Geometrical cutting factors, dynamic factors, part geometries, lubricants, materials and machine tools were all considered. Vibration was captured on line with two piezoelectric accelerometers placed following the X and Y axes of the machine tool.

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Abbreviations

Ae:

Radial depth of cut (mm)

Ap:

Axial depth of cut (mm)

C.L.A.:

Center line average

Cs:

Cutting section (mm2)

f :

Feed rate (mm/ min)

ft:

Tooth passing frequency (Hz)

fs:

Sampling frequency (Hz)

fz:

Feed per tooth (mm/z)

h :

Surface crest height (mm)

H :

Material hardness (HRC)

hx, hy:

High frequency vibration amplitude in X and Y axes

K :

Power coefficient

Lm:

Length of measurement

lx, ly:

Low frequency vibration amplitude in X and Y axes

MQL:

Minimum quantity of lubricant

MRR:

Material removal rate (mm3/min)

mx, my:

Medium frequency vibration amplitude in X and Y axes

N :

Spindle speed (rpm)

O :

Cutting tool overhang (mm)

P :

Power required (P)

R :

Cutter radius (mm)

Ra:

Roughness average (μm)

Rat:

Theoretical roughness average (μm)

tdx, tdy:

Temporal domain vibration amplitude in X and Y axes

tpx, tpy:

Tooth passing frequency vibration amplitude in X and Y axes

Vc:

Cutting speed (m/min)

W :

Tool wear

y :

Vertical deviation from the nominal surface

Z :

Number of teeth

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Authors and Affiliations

  1. ASCAMM Technology Centre, Barcelona, Spain

    G. Quintana

  2. Mechanical Engineering and Industrial Construction Department, University of Girona, Girona, Spain

    M. L. Garcia-Romeu & J. Ciurana

Authors
  1. G. Quintana
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  2. M. L. Garcia-Romeu
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  3. J. Ciurana
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Correspondence to J. Ciurana.

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Quintana, G., Garcia-Romeu, M.L. & Ciurana, J. Surface roughness monitoring application based on artificial neural networks for ball-end milling operations. J Intell Manuf 22, 607–617 (2011). https://doi.org/10.1007/s10845-009-0323-5

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  • DOI: https://doi.org/10.1007/s10845-009-0323-5

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