Toward a Process Monitoring and Control of a CNC Wood Router: Development of an Adaptive Control System for Routing White Birch

Authors

  • Piotr Iskra
  • Roger E. Hernández

Keywords:

White birch, routing, adaptive control system, cutting process, optimization, surface roughness prediction

Abstract

An adaptive optimization system for routing white birch was developed to control feed speed to produce a desired degree of surface roughness. The system consisted of an interconnected adaptive controller linked to a numerically controlled router. Online estimation of surface roughness was made based on directionalized measurement of sound pressure. By knowing the surface roughness at a given moment and its permissible limit, the adaptive controller was able to take decisive action to adjust the feed speed. The adaptive control system was able to detect changes in the surface quality and, as a result, vary the feed speed. Therefore, surface roughness was maintained below a predetermined level and productivity could be increased. Comparison with the resultant surface roughness when machining the same sample at constant feed speed confirmed the applicability of the adaptive control system for wood machining.

References

Cyra G, Tanaka C (2000) The effects of wood-fiber directions on acoustic emission in routing. Wood Sci Technol 34(3):237-252.nCyra G, Tanaka C, Nakao T (1996) On-line control of router feed speed using acoustic emission. Forest Prod J 46(11/12):27-32.nDelio T, Tlusty J, Smith S (1992) Use of audio signals for chatter detection and control. J Eng Ind Trans ASME 114(2):146-157.nDenaud LÉ, Bléron L, Ratle A, Marchal R (2007) Online control of wood peeling process: Acoustical and vibratory measurements of lathe checks frequency. Ann Sci 64(5):569-575.nGurau L, Mansfield-Williams H, Irle M (2007) Separation of processing roughness from anatomical irregularities and fuzziness to evaluate the effect of grit size on sanded European oak. Forest Prod J 57(1/2):110-115.nIskra P, Hernández RE (2009) The influence of cutting parameters on the surface quality of routed white birch and surface roughness prediction modeling. Wood Fiber Sci 41(1):28-37.nIskra P, Hernández RE (2010) Towards a process monitoring of CNC wood router. Sensor selection and surface roughness prediction. Wood Sci Technol (submitted).nIskra P, Tanaka C (2006) A comparison of selected acoustic signal analysis techniques to evaluate wood surface roughness produced during routing. Wood Sci Technol 40(3):247-259.nLabVIEW (2009) PID and fuzzy logic toolkit user manual. National Instruments, Austin, TX. 126 pp.nLemaster RL, Lu L, Jackson S (2000) The use of process monitoring techniques on a CNC wood router. Part 2. Use of a vibration accelerometer to monitor tool wear and workpiece quality. Forest Prod J 50(9):59-64.nOsai (1998) 10 Series CNC mini DNC Ethernet. Osai s.r.l, Barone canavese, Torino, Italy. 386 pp.nOsai (2001) 10 Series CNC mini DNC Ethernet. Function libraries. Osai s.r.l, Barone canavese, Torino, Italy. 216 pp.nSitkei G, Magoss E (2003): Optimum surface roughness of solid woods affected by internal structure and woodworking operations. Pages 366-371 in Proc 16th International Wood Machining Seminar, Matsue, Japan.n

Downloads

Published

2010-10-11

Issue

Section

Research Contributions