A non-contact method for part-based process performance monitoring in end milling operations

Surface response to excitation (SuRE) method was originally developed for structural health monitoring (SHM) applications. SuRE was used to evaluate the performance of completed milling operations. The method generates surface waves on the plate and studies the spectrum changes at selected points to detect defects and change of compressive forces. In this study, the length, depth, and width of a slot were changed step by step. The surface of the aluminum plate was excited in the 20–400 kHz range with a piezoelectric element. A laser scanning vibrometer was used to monitor the vibrations at the predetermined grid points after the dimensions of the slot were changed methodically. The frequency spectrums of measured vibrations were calculated by using the Fast Fourier Transformation (FFT). The sums of the squares of the differences (SSD) of the spectrums were calculated to evaluate the change of the spectrums. The SuRE method was able to determine if the dimensions were changed in each case at all the selected points. The scanning laser vibrometer is not feasible to be used at the shop floor. However, the study demonstrated that a piezoelectric element attached to any of the grid points would be able to evaluate the completed machining process.

[1]  S. Sampathkumar,et al.  An experimental investigation on monitoring of crater wear in turning using ultrasonic technique , 2009 .

[2]  Christian Brecher,et al.  Use of NC kernel data for surface roughness monitoring in milling operations , 2011 .

[3]  Potsang B. Huang An intelligent neural-fuzzy model for an in-process surface roughness monitoring system in end milling operations , 2014, Journal of Intelligent Manufacturing.

[4]  Ibrahim N. Tansel,et al.  Part based process performance monitoring (PbPPM) , 2013 .

[5]  Yean-Ren Hwang,et al.  Analysis of signals for monitoring of nonlinear and non-stationary machining processes , 2010 .

[6]  Franci Cus,et al.  Real-Time Cutting Tool Condition Monitoring in Milling , 2011 .

[7]  Somkiat Tangjitsitcharoen,et al.  Advance in chatter detection in ball end milling process by utilizing wavelet transform , 2015, J. Intell. Manuf..

[8]  Ming-Chyuan Lu,et al.  Study of high-frequency sound signals for tool wear monitoring in micromilling , 2012 .

[9]  Ibrahim N. Tansel,et al.  Investigation of the computational efficiency and validity of the surface response to excitation method , 2015 .

[10]  Alan Hase,et al.  The relationship between acoustic emission signals and cutting phenomena in turning process , 2014 .

[11]  Joaquim Ciurana,et al.  Surface roughness monitoring application based on artificial neural networks for ball-end milling operations , 2011, J. Intell. Manuf..

[12]  Dragos Axinte,et al.  An automated monitoring solution for avoiding an increased number of surface anomalies during milling of aerospace alloys , 2011 .

[13]  Abolfath Nikranjbar,et al.  Online model-based milling process condition monitoring , 2013 .

[14]  Yaguo Lei,et al.  Chatter identification in end milling process using wavelet packets and Hilbert–Huang transform , 2013 .

[15]  Marc Thomas,et al.  Indicators for monitoring chatter in milling based on instantaneous angular speeds , 2014 .

[16]  Ming-Chyuan Lu,et al.  Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling , 2011, The International Journal of Advanced Manufacturing Technology.

[17]  Miron Zapciu,et al.  Envelope dynamic analysis: a new approach for milling process monitoring , 2012 .

[18]  Yean-Ren Hwang,et al.  A cutter tool monitoring in machining process using Hilbert–Huang transform , 2010 .

[19]  Abderrazak El Ouafi,et al.  An ANN Based Multi-Sensor Integration Approach for in-Process Monitoring of Product Quality in Turning Operations , 2014 .

[20]  Saeed Behbahani,et al.  A reliability-based manufacturing process planning method for the components of a complex mechatronic system , 2013 .