SIGNAL-BASED SURFACE QUALITY ASSESSMENT IN MANUFACTURING

Ismail BOGREKCI, Pinar DEMIRCIOGLU

Abstract


This study investigates surface quality assessment in machining through signal processing techniques, focusing on Fast Fourier Transform (FFT) and Wavelet Transform (WT). The signals, extracted from confocal microscope images through the utilization of Matlab's signal processing toolbox. This study explored the effectiveness of FFT and WT for evaluating surface roughness, employing coarse, medium, and fine surfaces as sample materials. The roughness signals from these surfaces were subjected to FFT and WT processing, revealing the feasibility of using these techniques for surface roughness assessment. A performance evaluation compared these methods to determine the most suitable technique for discerning surface topography. The findings enhance the comprehension of surface quality assessment, providing valuable insights for selecting optimal methodologies tailored to specific applications.

Full Text:

PDF

References


P. Demircioglu, “3.17 Topological Evaluation of Surfaces in Relation to Surface Finish,” in Comprehensive Materials Finishing, Elsevier, 2017, pp. 243–260. doi: 10.1016/B978-0-12-803581-8.09179-7.

P. Demircioglu and M. N. Durakbasa, “Investigations on machined metal surfaces through the stylus type and optical 3D instruments and their mathematical modeling with the help of statistical techniques,” Measurement, vol. 44, no. 4, pp. 611–619, May 2011, doi: 10.1016/j.measurement.2010.12.001.

I. Bogrekci, M. N. Durakbasa, and P. Demircioglu, “Comparison between 3D Digital and Optical Microscopes for the Surface Measurement by Computer Vision,” at - Automatisierungstechnik, vol. 61, no. 3, pp. 195–202, Mar. 2013, doi: 10.1524/auto.2013.0024.

M. N. Durakbasa, P. H. Osanna, and P. Demircioglu, “The factors affecting surface roughness measurements of the machined flat and spherical surface structures – The geometry and the precision of the surface,” Measurement, vol. 44, no. 10, pp. 1986–1999, Dec. 2011, doi: 10.1016/j.measurement.2011.08.020.

R. Windecker, “Optical roughness measurements using extended white-light interferometry,” Opt. Eng, vol. 38, no. 6, p. 1081, Jun. 1999, doi: 10.1117/1.602154.

S. Nouhi and M. Pour, “Prediction of surface roughness of various machining processes by a hybrid algorithm including time series analysis, wavelet transform and multi view embedding,” Measurement, vol. 184, p. 109904, Nov. 2021, doi: 10.1016/j.measurement.2021.109904.

I. Bogrekci and P. Demircioglu, “3.18 Evaluation of Surface Finish Quality Using Computer Vision Techniques,” in Comprehensive Materials Finishing, Elsevier, 2017, pp. 261–275. doi: 10.1016/B978-0-12-803581-8.09180-3.

M. Pour, “Determining surface roughness of machining process types using a hybrid algorithm based on time series analysis and wavelet transform,” Int J Adv Manuf Technol, vol. 97, no. 5–8, pp. 2603–2619, Jul. 2018, doi: 10.1007/s00170-018-2070-2.

T. Misaka et al., “Prediction of surface roughness in CNC turning by model-assisted response surface method,” Precision Engineering, vol. 62, pp. 196–203, Mar. 2020, doi: 10.1016/j.precisioneng.2019.12.004.

Y. Yang, W. Wu, and L. Sun, “Prediction of mechanical equipment vibration trend using autoregressive integrated moving average model,” in 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai: IEEE, Oct. 2017, pp. 1–5. doi: 10.1109/CISP-BMEI.2017.8302110.

K. Kurşun, F. Güven, and H. Ersoy, “Utilizing Piezo Acoustic Sensors for the Identification of Surface Roughness and Textures,” Sensors, vol. 22, no. 12, p. 4381, Jun. 2022, doi: 10.3390/s22124381.

A. C. Seckin, P. Demircioglu, I. Bogrekci, and G. Ozer, “Capacitive NDT of Impact/Press Induced Structural Degradation in Agricultural Machinery,” in 22nd International Conference of Nonconventional Technologies, Bistrița, Romania, Nov. 2023.

P. Demircioglu, A. Seckin, J. Torgersen, I. Bogrekci, and N. Durakbasa, “Metal Surface Texture Classification with Gabor Filter Banks and Xai,” in DAAAM International Scientific Book, 1st ed., vol. 22, B. Katalinic, Ed., DAAAM International Vienna, 2023, pp. 033–050. doi: 10.2507/daaam.scibook.2023.03.


Refbacks

  • There are currently no refbacks.


JOURNAL INDEXED IN :