A New Model Based on Principal Component Regression-Random Forest for Analyzing and Predicting the Physical and Mechanical Properties of Particleboard

Cuiping Yang, Cong Xu, Jilai Su, Wei He, Zhenhua Gao


The physical and mechanical properties are key indexes for determining the quality of particleboards. For this reason, a study on evaluating the physical and mechanical properties of particleboard via a new method has considerable value. Thus, a method based on principal component regression (PCR) analysis and random forest (RF) is proposed in this paper. First, the problems requiring resolution are described after analyzing the production process parameters as well as the physical and mechanical properties of particleboard. Then, an analysis and prediction models based on the PCR and RF method is established. On the basis of the PCR method, the key process parameters that affect various physical and mechanical properties are determined. Based on the RF method, the analysis and prediction model are built from the previously determined process parameters of the physical and mechanical properties. Finally, through experimental analysis, the effectiveness of the analysis and prediction models based on the PCR and RF method are verified. This work was able to determine the relationship between the process parameters and the physical and mechanical properties, which can help improve practical industrial manufacturing effectivity.


Particleboard; Physical and mechanical properties; Process parameters; Property analyzing; Model prediction

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