Experimental data are presented on the effects of short time-scale events on measured cutting force and acoustic emission during face milling. The events of interest are those that occur within a few revolutions of the cutting tool and are associated with non-continuous degradation such as insert edge chipping.
A processing scheme is suggested whereby the events are recognized and distinguished from each other using a neural network simulation applied to the peaks of the r.m.s. acoustic emission records. It is found that acoustic emission is a more suitable description of such events than is cutting force.
Finally, a record of the network simulation acting as a breakage detector in real time on a computer numerical control (CNC) milling machine is presented.
References
1.
YakinoY.In-process detection of tool breakage by monitoring acoustic emission. Proceedings of an International Conference on Cutting tool materials, Ft Michael, Kentucky, 15–17 September 1980, pp. 25–38.
2.
InasakiI.YonetsuS.In-process detection of cutting tool damage by acoustic emission measurement. Proceedings of the Twenty-second MTDR Conference, 1981, pp. 261–268.
3.
MoriwakiT.Detection of cutting tool fracture by acoustic emission measurement. Ann. CIRP, 1980, 29(1), 35–39.
4.
DieiE. N.DornfeldD. A.A model of tool fracture generated acoustic emission during machining. Trans. ASME, J. Engng for Industry, August 1987, 109, 227–233.
5.
LanM. S.DornfeldD. A.In-process tool fracture detection. Trans. ASME, J. Engng Mater. Technol., April 1984, 106, 111–118.
6.
AltintasY.YellowleyI.In-process detection of tool failure in milling using cutting force models. Trans. ASME, J. Engng for Industry, May 1989, 111, 149–157.
7.
LanM. S.NaerheimY.In-process detection of tool breakage in milling. Trans. ASME, J. Engng for Industry, August 1986, 108, 191–197.
8.
VenkatasubramanianV.VaidyanathanR.YamamotoY.Process fault detection and diagnosis using neural networks I. Steady-state processes. Computers in Chan. Engng, 1990, 14(7), 699–712.
9.
RangwalaS.DornfeldD. A.Sensor integration using neural networks for intelligent tool condition monitoring. Trans. ASME, J. Engng for Industry, August 1990, 112, 219–228.
10.
WilcoxS. J.ReubenR. L.RaptiA.PsonisS.KavvadasD.Progressive tool wear monitoring and breakage detection in rough face milling operations using both expert system and neural network approaches. Proceedings of the International CIRP/VDI Conference on Cutting materials and tooling, Dûsseldorf, September 1993.
11.
WilcoxS. J.ReubenR. L.BorthwickW. K. D.Acoustic emission detection of edge chipping on a multi-point milling tool during a face milling test. Proceedings of the Fourth World Meeting on Acoustic Emission and First International Conference on Acoustic emission in manufacturing, Boston, Mass., September 1991, pp. 293–300.
12.
WilcoxS. J. PhD thesis, Heriot-Watt University, 1992.
13.
FrieselM.CarpenterS. H.Determination of the sources of acoustic emission generated during the deformation of titanium. Metall. Trans. A, October 1984, 15A, 1849–1853.
14.
McBrideR.CarolanT. A.BartonJ. S.WilcoxS. J.BorthwickW. K. D.JonesJ. D. C.Detection of acoustic emission in cutting processes by fibre optic interferometry. Measmt Sci. Technol., 1993 (in press).
15.
RogetJ.Essais non destructifs—l‘émission acoustique, 1988 (AFNOR-CETIM, Paris).
16.
WilkinsonP.ReubenR. L.HandD. P.CarolanT. A.BartonJ. S.JonesJ. D. C.Measurement of surface profile and acoustic emission by optical fibre interferometry. Proceedings of International Conference on Laser metrology and machine performance, Southampton, 1993.
17.
WilcoxS. J.ReubenR. L.BorthwickW. K. D.CarolanT. A.McBrideR.BartonJ. S.JonesJ. D. C.High frequency analogue recording of acoustic emission and its use in tool wear monitoring. Proceedings of Fourth International Conference on Condition monitoring and diagnostic engineering management, Senlis, France, 1992.
