In-process endpoint detection of weld seam removal in robotic abrasive belt grinding process

  • Vigneashwara Pandiyan
  • Tegoeh Tjahjowidodo


This paper proposes a novel approach for in-process endpoint detection of weld seam removal during robotic abrasive belt grinding process using discrete wavelet transform (DWT) and support vector machine (SVM). A virtual sensing system is developed consisting of a force sensor, accelerometer sensor and machine learning algorithm. This work also presents the trend of the sensor signature at each stage of weld seam evolution during its removal process. The wavelet decomposition coefficient is used to represent all possible types of transients in vibration and force signals generated during grinding over weld seam. “Daubechies-4” wavelet function was used to extract features from the sensors. An experimental investigation using three different weld profile conditions resulting from the weld seam removal process using abrasive belt grinding was identified. The SVM-based classifier was employed to predict the weld state. The results demonstrate that the developed diagnostic methodology can reliably predict endpoint at which weld seam is removed in real time during compliant abrasive belt grinding.


Abrasive belt grinding DWT Surface finish/integrity SVM Weld seam removal 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Islam MU, Xue L, McGregor G (2001) Process for manufacturing or repairing turbine engine or compressor components. U.S. Patent 6,269,540Google Scholar
  2. 2.
    Heitman PW, Hammond SN, Brown LE (1991) Method for joining single crystal turbine blade halves. U.S. Patent 5,071,059Google Scholar
  3. 3.
    Axinte D, Kritmanorot M, Axinte M, Gindy N (2005) Investigations on belt polishing of heat-resistant titanium alloys. J Mater Process Technol 166(3):398–404CrossRefGoogle Scholar
  4. 4.
    Ren X, Cabaravdic M, Zhang X, Kuhlenkötter B (2007) A local process model for simulation of robotic belt grinding. Int J Mach Tools Manuf 47(6):962–970CrossRefGoogle Scholar
  5. 5.
    (2011) Automated weld seam removal system promises removal rates over 20 times faster than manual grinding. Industrial Robot: An International Journal 38 (4). doi: 10.1108/ir.2011.04938daa.002
  6. 6.
    Ito Y (2014) In-process measurement for machining states: sensing technology in noisy space. In: Thought-evoking approaches in engineering problems. Springer International Publishing, Cham, p 17–40. doi: 10.1007/978-3-319-04120-9_2
  7. 7.
    Chen JC, Huang L, Lan A, Lee S (1999) Analysis of an effective sensing location for an in-process surface recognition system in turning operations. J Ind Technol 15(3):1–6Google Scholar
  8. 8.
    Chen X, Chen S, Lin T, Lei Y (2006) Practical method to locate the initial weld position using visual technology. Int J Adv Manuf Technol 30(7):663–668. doi: 10.1007/s00170-005-0104-z CrossRefGoogle Scholar
  9. 9.
    Luo H, Chen X (2005) Laser visual sensing for seam tracking in robotic arc welding of titanium alloys. Int J Adv Manuf Technol 26(9):1012–1017. doi: 10.1007/s00170-004-2062-2 CrossRefGoogle Scholar
  10. 10.
    Luo RC, Kay MG (1990) A tutorial on multisensor integration and fusion. In: Industrial electronics society. IECON'90., 16th Annual Conference of IEEE, 1990. IEEE, p 707–722Google Scholar
  11. 11.
    Dornfeld D (1986) Acoustic emission process monitoring for untended manufacturing. In: Proc. Japan-USA Symposium on Flexible Automation, p 831–836Google Scholar
  12. 12.
    Pandiyan V, Tjahjowidodo T, Samy MP (2016) In-process surface roughness estimation model for compliant abrasive belt machining process. Procedia CIRP 46:254–257CrossRefGoogle Scholar
  13. 13.
    Kanish TC, Kuppan P, Narayanan S, Ashok SD (2014) A fuzzy logic based model to predict the improvement in surface roughness in magnetic field assisted abrasive finishing. Procedia Eng 97(Complete):1948–1956. doi: 10.1016/j.proeng.2014.12.349 CrossRefGoogle Scholar
  14. 14.
    Caesarendra W, Kosasih B, Tieu AK, Moodie CAS (2014) Circular domain features based condition monitoring for low speed slewing bearing. Mech Syst Signal Process 45(1):114–138. doi: 10.1016/j.ymssp.2013.10.021 CrossRefGoogle Scholar
  15. 15.
    Kasashima N, Mori K, Ruiz GH, Taniguchi N (1995) Online failure detection in face milling using discrete wavelet transform. CIRP Ann Manuf Technol 44(1):483–487. doi: 10.1016/S0007-8506(07)62368-3 CrossRefGoogle Scholar
  16. 16.
    Xiaoli L (1999) On-line detection of the breakage of small diameter drills using current signature wavelet transform. Int J Mach Tools Manuf 39(1):157–164CrossRefGoogle Scholar
  17. 17.
    Learned RE, Willsky AS (1995) A wavelet packet approach to transient signal classification. Appl Comput Harmon Anal 2(3):265–278CrossRefMATHGoogle Scholar
  18. 18.
    Nurprasetio P, Bagiasna K, Suharto D, Tjahjowidodo T (2010) The development of a novel fault identification technique by combining minimum-distance pattern-recognition and discrete wavelet transform. In: Proceedings of international conference on intelligent unmanned systemsGoogle Scholar
  19. 19.
    Tansel IN, Mekdeci C, Mclaughlin C (1995) Detection of tool failure in end milling with wavelet transformations and neural networks (WT-NN). Int J Mach Tools Manuf 35(8):1137–1147CrossRefGoogle Scholar
  20. 20.
    Saravanan N, Ramachandran K (2010) Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN). Expert Syst Appl 37(6):4168–4181CrossRefGoogle Scholar
  21. 21.
    Azouzi R, Guillot M (1997) On-line prediction of surface finish and dimensional deviation in turning using neural network based sensor fusion. Int J Mach Tools Manuf 37(9):1201–1217CrossRefGoogle Scholar
  22. 22.
    Pöyhönen S, Arkkio A, Jover P, Hyötyniemi H (2005) Coupling pairwise support vector machines for fault classification. Control Eng Pract 13(6):759–769CrossRefGoogle Scholar
  23. 23.
    Çaydaş U, Ekici S (2012) Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel. J Intell Manuf 23(3):639–650. doi: 10.1007/s10845-010-0415-2 CrossRefGoogle Scholar
  24. 24.
    Schölkopf B, Smola AJ (2001) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press Cambridge, MAGoogle Scholar
  25. 25.
    Noble WS (2006) What is a support vector machine? Nat Biotechnol 24(12):1565–1567CrossRefGoogle Scholar
  26. 26.
    Zhang X, Kuhlenkötter B, Kneupner K (2005) An efficient method for solving the Signorini problem in the simulation of free-form surfaces produced by belt grinding. Int J Mach Tools Manuf 45(6):641–648CrossRefGoogle Scholar
  27. 27.
    Khellouki A, Rech J, Zahouani H (2007) The effect of abrasive grain’s wear and contact conditions on surface texture in belt finishing. Wear 263(1):81–87CrossRefGoogle Scholar
  28. 28.
    Zhu Z, Yan R, Luo L, Feng Z, Kong F (2009) Detection of signal transients based on wavelet and statistics for machine fault diagnosis. Mech Syst Signal Process 23(4):1076–1097CrossRefGoogle Scholar
  29. 29.
    Shukla KK, Tiwari AK (2013) Filter banks and DWT. In: Efficient algorithms for discrete wavelet transform: With applications to denoising and fuzzy inference systems. Springer London, London, pp 21–36. doi: 10.1007/978-1-4471-4941-5_2
  30. 30.
    Vapnik V (1998) The support vector method of function estimation. In: Suykens JAK, Vandewalle J (eds) Nonlinear modeling: Advanced black-box techniques. Springer US, Boston, MA, pp 55–85. doi: 10.1007/978-1-4615-5703-6_

Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  1. 1.School of Mechanical and Aerospace EngineeringNanyang Technological UniversitySingaporeSingapore

Personalised recommendations