Abstract
This paper describes a multitooth tool diagnosis application to be used in the car industry. The main focus is on the optimal variable selection (noise, vibration, temperature, and tool drives electrical power consumption) of the tool’s environment, and the signal processing by means of the segmentation of the electrical power consumption signals into groups of inserts according to the type of tools studied. The fault detection algorithms are based on statistical analysis of the spindle tool power consumption. Different statistical parameters are used in a change detection algorithm, while keeping in mind the need for a reliable and low-cost fault diagnosis system. Multitooth tools add an important degree of difficulty to the fault detection problem as opposed to simple tools because of the complexity introduced by the high number of inserts that the workpiece is machining at the same time, for different kinds of finishing and operations.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Du R, Elbestawi MA et al (1995) Automated monitoring of manufacturing processes, part 1: monitoring methods. J Eng Ind 117:121–132
Altintas Y (2000) Manufacturing automation: metal cutting mechanics, machine tool vibrations and CNC design. Cambridge University Press, Cambridge
Astakhov VP (2004) The assessment of cutting tool wear. Int J Mach Tools Manuf (44):637–647
Liang SY, Hecker RL, Landers RG (2004) Machining process monitoring and control: the state of the art. J Manuf Sci Eng 126(2):297–310
Frankowiak M et al (2005) A review of the evolution of microcontroller-based machine and process monitoring. Int J Mach Tools Manuf 45:573–582
Jemielniak K (1999) Commercial tool condition monitoring systems. Int J Adv Manuf Technol 15:711–721
Romero-Troncoso RJ et al (2003) Driver current analysis for sensorless tool breakage monitoring of cnc milling machines. Int J Mach Tools Manuf 43:1529–1534
Altintas Y (1992) Prediction of cuttings forces and tool breakage in milling from feed current measurements. J Eng Ind 114:386–292
Mesina OS, Langari R (2001) A neuro-fuzzy system for tool condition monitoring in metal cutting. J Manuf Sci Eng 123:312–318
Kamarthi SV, Kumara SRT, Cohen PH (2000) Flank wear estimation in turning through wavelet representation of acoustic emission signals. J Manuf Sci Eng 122:12–19
Scheffer C et al (2003) Development of a tool wear-monitoring system for hard turning. Int J Mach Tools Manuf 43:973–985
Tlusty G (2000) Manufacturing processes and equipment. Prentice Hall, Englewood Cliffs
Altintas Y, Shamoto E et al (1999) Analytical prediction of stability lobes in ball end milling. J Manuf Sci Eng 121:586–592
Stein JL, Huh K (2002) Monitoring cutting forces in turning: a model-based approach. J Manuf Sci Eng 124(1):26–31
Mahfouz IA (2003) Drilling wear detection and classification using vibration signals and artificial neural network. Int J Mach Tools Manuf 43:707–720
Wu Y, Escande P, Du R (2001) A new method for real-time tool condition monitoring in transfer machining stations. J Manuf Sci Eng 123:339–347
Zahra NH, Yu G (2003) Gradual wear monitoring of turning inserts using wavelet analysis of ultrasound waves. Int J Mach Tools Manuf 43:337–343
Li X et al (2000) Feed cutting force estimation from the current measurement with hybrid learning. Int J Adv Manuf Technol 16:859–862
Wang L et al (2003) A method for sensor selection in reconfigurable process monitoring. J Manuf Sci Eng 125(1):95–99
Basseville M, Nikiforov I (1993) Detection of abrupt changes: theory and application. Prentice Hall, Englewood Cliffs
Gusstafson F (2000) Adaptive filtering and change detection. Wiley, New York
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Reñones, A., Rodríguez, J. & de Miguel, L.J. Industrial application of a multitooth tool breakage detection system using spindle motor electrical power consumption. Int J Adv Manuf Technol 46, 517–528 (2010). https://doi.org/10.1007/s00170-009-2119-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-009-2119-3