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Dynamic precision control in single-grit scratch tests using acoustic emission signals

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Abstract

Acoustic emission (AE) is very sensitive to minuscule molecular changes which allow it to be used in a dynamic control manner. The work presented here specifically investigates approaching grit and workpiece interaction during grinding processes. The single grit (SG) tests used in this work display that the intensities from air, occurring in between the grit and workpiece, show an increasing intensity as the grit tends towards the workpiece with 1-μm increments. As the grit interacts with the workpiece, a scratch is formed; different intensities are recorded with respect to a changing measured depth of cut (DOC). In the first instance, various AE were low tending towards high signal to noise ratios which is indicative of grit approaching contact; when contact is made, frictional rubbing is noticed, then ploughing with low DOC and, finally, actual cutting with a higher associated DOC. Dynamic control is obtained from the AE sensor extracting increasing amplitude significant of elastic changing towards greater plastic material deformation. Such control methods can be useful for grinding dressing ratios as well as achieving near optimal surface finish when faced with difficult to cut geometries. Two different materials were used for the same SG tests (aerospace alloys: CMSX4 and titanium-64) to verify that the control regime is robust and not just material dependent. The AE signals were then classified using neural networks (NNs) and classification and regression trees (CART)-based rules. A real-time simulation is provided showing such interactions allowing dynamic micro precision control. The results show clear demarcation between the extracted synthesized signals ensuring high accuracy for determining different phenomena: 3–1 μm approaching touch, touch, slight plastic deformation and, increasing plastic deformation. In addition to dressing ratios, the results are also important for micron accuracy set-up considerations.

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References

  1. Hatano H, Chaya T, Watanabe S, Jinbo K (1998) Reciprocity calibration of impulse response of acoustic emission transducers. IEEE Trans Ultrason, Ferro Elec Freq Control 45(5 [12]):1221–1228

    Article  Google Scholar 

  2. Griffin J, Chen X (2014) Real-time simulation of neural network classifications from characteristics emitted by acoustic emission during horizontal single grit scratch tests. J Intell Manuf. Online, ISSN 1572–8145

  3. Deshpande A and Pieper R Legacy machine monitoring using power signals analysis. Procs ASME Int Manuf Sci Eng Conf, MSEC., Oregon, USA, pp. 1–8, 2011.

  4. Burke L, Rangwala S (1991) Tool condition monitoring in metal cutting: a neural network approach. J Intell Manuf 2:269–280

    Article  Google Scholar 

  5. Jemielniak K, Kwiatkowski L, Wrzosek P (1998) Diagnosis of tool wear based on cutting forces and acoustic emission measures as inputs to a neural network. J Intell Manuf 9:447–455

    Article  Google Scholar 

  6. Xiaoli L, Yingxue Y, Zhejun Y (1997) Online tool condition monitoring system with wavelet fuzzy neural network. J Intell Manuf 8:271–276

    Article  Google Scholar 

  7. Venkatesh K, Zhou M, Caudill R (1997) Design of a neural network for tool wear monitoring. J Intell Manuf 9:281–287

    Google Scholar 

  8. Sharma V, Dhiman S, Sehgal R, Sharma S (2008) Estimation of cutting forces and surface roughness for hard turning using neural networks. J Intell Manuf 8:215–226

    Google Scholar 

  9. Ren Q, Balazinski M, Baron L (2012) Fuzzy identification of cutting acoustic emission with extended subtractive cluster analysis. Nonlinear Dyn 67(4):2599–2608

    Article  Google Scholar 

  10. Ren Q, Balazinski M, Jemielniak K, Baron L, Achiche S (2013) Experimental and fuzzy modelling analysis on dynamic cutting force in micro milling. Soft Comput 17:1687–1697

    Article  Google Scholar 

  11. Ren Q, Baron L, Balazinski M, Jemielniak K, Botez R, Achiche S (2014) Type-2 fuzzy tool condition monitoring system based on acoustic emission in micromilling. Inf Sci 255:121–134

    Article  Google Scholar 

  12. Warnecke G, Kluge R (1998) Control of tolerances in turning by predictive control with neural networks. J Intell Manuf 9:281–287

    Article  Google Scholar 

  13. Barbezat M, Brunner AJ, Flueler P, Huber C, Kornmann X (2004) Acoustic emission sensor properties of active fibre composite elements compared with commercial acoustic emission sensors. Sensors Actuators 114:13–20

    Article  Google Scholar 

  14. Godin N, Huguet S, Gaertner R, Salmon L (2004) Clustering of acoustic emission signals collected using tensile tests on unidirectional glass/polyester composite using supervised and unsupervised classifiers. NDT&T Int 37(4):253–264

    Article  Google Scholar 

  15. Li X, Zhejun Y (1998) Tool wear monitoring with wavelet packet transform-fuzzy clustering method. Wear 219(2):145–154

    Article  Google Scholar 

  16. Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. CRC Press

  17. Hartigan J (1985) Statistical theory in clustering. J Classif 2(1):63–76

    Article  MATH  MathSciNet  Google Scholar 

  18. Coppersmith D, Hong SJ, Hosking JR (1999) Partitioning nominal attributes in decision tree. Data Min Knowl Disc 3(2):197–217

    Article  Google Scholar 

  19. Sick B (2002) On-line and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research. Mech Syst Signal Process 16(4):487–546

    Article  Google Scholar 

  20. Ozel T, Karpat Y (2005) Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. Int J Mach Tools Manuf 45(4):467–479

    Article  Google Scholar 

  21. Griffin J, Chen X (2014) Real-time fuzzy-clustering and CART rules classification of the characteristics of emitted acoustic emission during horizontal single-grit scratch tests. Int. J Advan Manuf Tech. 1–22

  22. Chen X, Griffin J, Liu Q (2007) Mechanical and thermal behaviours of grinding acoustic emission. Int JManuf Tech Manag 12(1–39):184–199

    Google Scholar 

  23. Griffin J, Chen X (2009) Characteristics of the acoustic emission during horizontal single grit scratch tests part I. Characteristics and identification. Int. J Abrasive Tech Special Issue Micro/Meso Mech Manuf (M4 Process) 1(4)

  24. Boczar T, Marcin L (2006) Time-frequency analysis of the calibrating signals generated in the Hsu-Nielsen system. Phys Chem Solid State 7(3):585–588

    Google Scholar 

  25. Hwang TW, Whitenton EP, Hsu NN, Blessing GV, Evans CJ (2000) Acoustic emission monitoring of high speed grinding silicon nitride. Ultrasonics 38(1):614–619

    Article  Google Scholar 

  26. Kalpakjian S, Schmid SR (2003) Manufacturing process for engineering materials. Prentice Hall, ISBN 0-13-040871-9, pp. 510–520

  27. Opoz TT, Chen X (2010) An investigation of the rubbing and ploughing in single grain grinding using finite element method, in: Proceedings of the 8th International Conference on Manufacturing Research, Durham, UK, pp. 256–261

  28. Matsuo S, Toyoura E, Oshima Y, Ohbuchi Y (1989) Effect of grain shape on cutting force in super abrasive single-grit tests. CIRP Annals—Manufacturing Technology 38:323–326

    Article  Google Scholar 

  29. Park HW, Liang SY, Chen R (2007) Micro grinding force predictive modelling based on microscale single grain interaction analysis. Int J Manuf Technol Manag 12:25–38

    Google Scholar 

  30. Gilbert D, Stoesslein M, Axinte D, Butler-Smith P, Kell J (2014) A time based method for predicting the workpiece surface micro-topography under pulsed laser ablation. J Mater Process Technol 214(12):3077–3088

    Article  Google Scholar 

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Correspondence to James Marcus Griffin.

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Griffin, J.M., Torres, F. Dynamic precision control in single-grit scratch tests using acoustic emission signals. Int J Adv Manuf Technol 81, 935–953 (2015). https://doi.org/10.1007/s00170-015-7081-7

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