WOOD VENEER SURFACE MANUFACTURING DEFECTS—PREVALENCE IN MALAYSIAN INDUSTRY AND HUMAN BASELINE DEFECT DETECTION PERFORMANCE

Wood products are perceived as premium products. Therefore, visible surface defects are undesirable. The current defect detection in wood products is by manual visual inspection. There is scant research data available on the defects plaguing the downstream wood industry. This paper determined the extent of such defects in assembled wooden veneered interior doors produced in a Malaysian manufacturing plant, focusing on American red oak (Quercus spp.), yellow poplar (Liriodendron tulipifera) and maple (Acer spp.) species. Industrial random sampling defect data was classified into seven defect categories. Pareto analysis showed that handling defects––particularly scratches/dents and knife marks––were the most prevalent, constituting 30% of all defects. The relationship between human ocular physiology and defect detection ability was tested using SPARCS (Spaeth/Richman Contrast Sensitivity) methodology, which was found to be a good low-contrast ability predictor. Several common errors causing false positives were also identified. Comparisons using statistical t-tests between industry personnel and non-experts, and between genders showed that there was no difference in detection performance. In conclusion, human fallibility was the main cause of failure in detecting defects, particularly those with low contrast. Human behavioural results gathered in this study can be utilised as benchmarks for future automation studies.