Particleboard Surface-Roughness Classification System Modeling, Simulation, and Bench Testing

[+] Author and Article Information
P. Radziszewski, B. Picard

Department of Mechanical Engineering, McGill University, 817 Sherbrooke Street West, Montréal, PQ H3A 2K6, Canada

A.-M. Bourret

BRCDT,  École Polytechnique, C.P. 6079, Succ. Centre-ville Montréal, PQ H3C 3A7, Canada

J.-P. Brunet

 Bioptic Vision, Inc., 1927 3 av, Val d’Or, Québec, PQ J9P 4N7, Canada

M. Tétreault

Département de Génie de la Production Automatisée,  École de Technologie Supérieure, 1100 rue Notre-Dame Ouest, Montréal, PQ H3C 1K3, Canada

K. Zaras

Département des Sciences Administratives,  Université du Québec en Abitibi-Témiscamingue, 445, Boulevard de l’Université, Rouyn-Noranda, PQ J9X 5E4, Canada

M. Cheriet

Département de Génie de la Production Automatisée, École de Technologie Supérieure, 1100 rue Notre-Dame Oust, Montréal, PQ H3C 1K3, Canada

J. Ouellet

Department of Mining, Metals and Materials,  McGill University, 3450 University Street, Montréal, PQ H3A 2A7, Canada

J. Manuf. Sci. Eng 127(3), 677-686 (Aug 01, 2004) (10 pages) doi:10.1115/1.1954795 History: Received December 02, 2003; Revised August 01, 2004

Particleboard panels are widely utilized as a raw material in the wood processing industry. It ends up as furniture, cabinets, and other industrial products. One of the problems particleboard mills face concerns the surface quality of their boards. As the demands of customers become more precise, very thin overlays are becoming more popular. Thus the problem of surface quality control and classification is clearly identified. In this paper, a particleboard surface-roughness classification system is modeled, simulated, and implemented. The particleboard model is based on the characterization of surface anomalies (pinholes, sander streaks, and grooves). Furthermore, an optical stylus surface-roughness measurement system is also modeled in order to determine whether it can be used to characterize a particleboard “on-ine.” A classification algorithm is proposed to serve as an aid to the quality control operator. Simulation results are presented illustrating the change of surface roughness with increasing amounts of surface anomalies. A classification algorithm is used to sort the simulated panels into different classes. A trial bench test using 225 panels is made to determine the applicability of this system to the industrial context.

Copyright © 2005 by American Society of Mechanical Engineers
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Figure 12

Average roughness index for panels with grooves and sander streaks

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Figure 13

Location of the laser stylus system in the particleboard production process

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Figure 11

Average roughness index for panels with pinholes

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Figure 10

Variation of d and df

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Figure 9

Straight-line receiver’s trajectory

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Figure 8

Receiver’s trajectory (x,y:ft)

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Figure 7

Particleboard with surface anomalies

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Figure 6

Particleboard surface roughness simulator

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Figure 5

Sander streak sample

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Figure 2

Industrial-quality sample

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Figure 1

Particleboard process




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