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Effect of wood surface roughness on prediction of structural timber properties by infrared spectroscopy using ANFIS, ANN and PLS regression
European Journal of Wood and Wood Products ( IF 2.6 ) Pub Date : 2020-11-08 , DOI: 10.1007/s00107-020-01621-x
Samuel Ayanleye , Vahid Nasir , Stavros Avramidis , Julie Cool

Predicting the properties of structural timber using a rapid and reliable non-destructive method is a critical quality control task in production. This study investigates using infrared spectroscopy for predicting the density, moduli of elasticity (MOE) and rupture (MOR) of two structural softwoods. Because the produced timber is sometimes planed during manufacturing resulting in a smooth surface finish, the effect of wood surface roughness on prediction accuracy of timber properties was also investigated. Accordingly, infrared spectroscopy experiments were carried out on Douglas-fir and Western hemlock specimens having a rough and smooth surface. In addition, the effect of the infrared spectroscopy range on the predictive models was studied. Data in the visible infrared (VIS), near-infrared (NIR), and the combined VIS and NIR range were used for properties prediction. The acquired infrared data were processed using principal component analysis (PCA) for data reduction and feature selection. The output of PCA was then fed into an adaptive neuro-fuzzy inference system (ANFIS), multilayer perceptron (MLP) neural network (NN) and partial least square (PLS) regression model. The results showed that the wood surface finish, range of infrared data, and the type of machine learning model impact the prediction accuracy. ANFIS showed superior performance to MLP NN and PLS model for properties prediction. In addition, NIR data acquired from rough surface resulted in better prediction accuracy, which suggests that an infrared spectroscopy test should be performed prior to surface planing. The proposed models yield better accuracy for predicting the MOE and MOR than wood density.



中文翻译:

木材表面粗糙度对使用ANFIS,ANN和PLS回归的红外光谱预测结构木材性能的影响

使用快速,可靠的非破坏性方法预测结构木材的性能是生产中至关重要的质量控制任务。这项研究调查使用红外光谱预测两种结构性软木的密度,弹性模量(MOE)和断裂强度(MOR)。由于有时会在生产过程中对所生产的木材进行刨光,从而获得光滑的表面光洁度,因此还研究了木材表面粗糙度对木材性能预测准确性的影响。因此,对具有粗糙和光滑表面的道格拉斯冷杉和西方铁杉样品进行了红外光谱实验。此外,研究了红外光谱范围对预测模型的影响。可见红外(VIS),近红外(NIR),结合使用VIS和NIR范围进行属性预测。使用主成分分析(PCA)处理获取的红外数据,以进行数据缩减和特征选择。然后将PCA的输出输入到自适应神经模糊推理系统(ANFIS),多层感知器(MLP)神经网络(NN)和偏最小二乘(PLS)回归模型中。结果表明,木材表面光洁度,红外数据范围和机器学习模型的类型会影响预测精度。对于属性预测,ANFIS显示出优于MLP NN和PLS模型的性能。此外,从粗糙表面获取的NIR数据可以提高预测精度,这表明应该在表面平整之前进行红外光谱测试。

更新日期:2020-11-09
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