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首页> 外文期刊>Journal of Tropical Forest Science >WOOD VENEER SURFACE MANUFACTURING DEFECTS - PREVALENCE IN MALAYSIAN INDUSTRY AND HUMAN BASELINE DEFECT DETECTION PERFORMANCE
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WOOD VENEER SURFACE MANUFACTURING DEFECTS - PREVALENCE IN MALAYSIAN INDUSTRY AND HUMAN BASELINE DEFECT DETECTION PERFORMANCE

机译：木板面表面制造缺陷 - 马来西亚工业普遍存在和人为基线缺陷检测性能

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摘要

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.
机译：木制品被认为是优质产品。因此，可见表面缺陷是不希望的。木材产品目前的缺陷检测是通过手动视觉检查。缺陷有很少的研究数据，滥用下游木业。本文确定了在马来西亚制造厂生产的组装木板饰面室内门的这种缺陷的程度，专注于美国红橡树（栎属SPP），黄杨（LirioIDENDR TULIPIFERA）和枫树（ACER SPP。）种类。工业随机采样缺陷数据被分为七种缺陷类别。 Pareto分析表明，处理缺陷 - 特别是划痕/凹痕和刀片 - 是最普遍的，构成所有缺陷的30％。使用SPARCS（Spaeth / Richman对比度敏感性）方法测试人体眼科生理学和缺陷检测能力之间的关系，这被发现是一种良好的低对比度预测因子。还确定了造成误报的几种常见错误。使用行业人员和非专家之间的统计T检验以及在性别之间的比较表明，检测性能没有差异。总之，人类的识别是检测缺陷失败的主要原因，特别是那些对比度的缺陷。聚集在本研究中的人类行为结果可用作未来自动化研究的基准。

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