A new model based on principal component regression-random forest for analyzing and predicting the physical and mechanical properties of particleboard

Abstract

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.

Investigation of Bonding Strength of Medium Density Fiberboard by Using Ultrasonic Testing

“…Haftkhani et al [11] used the PI theorem theory (Buckingham's pi-theorem) to predict the IB of particleboard based on parameters such as particle size and bonding ratio, and the prediction result still had an error of 18.17%. Tiryaki et al [12] established an artificial neural network model to predict the bonding strength of oriental beech based on parameters such as adhesive content, hot-pressing time, and hot-pressing temperature, and the results showed an average error of 2.54%.Yang et al [13] established a mathematical model based on principal component regression analysis and random forest (RF) to predict the mechanical properties of particleboard, such as internal bonding strength and bending strength, using 23 production process parameters such as moisture content and adhesive usage. They also determined the relationship between production process parameters and mechanical properties of particleboard.…”

Optimization of production parameters of particle gluing on internal bonding strength of particleboards using machine learning technology

“…However, the ANN model training is time-consuming and easy to fall into locally optimal solution [14]. Yang et al [15] established a mathematical model between the mechanical properties of PB and 23 production process parameters of PB such as moisture content and glue amount based on principal component regression analysis and random forest (RF), and determined the relationship between the production process parameters of PB and the mechanical properties, such as IB and bending strength. However, under the coupling effect of various factors, excessive multidimensionality would lead to excessive fitting of the model, and it could limit the upper limit of prediction accuracy [16].…”

Türkiye Yongalevha Endüstrisinin Üretim Problemleri Ve Çözüm Önerilerinin İncelenmesi

“…Yongalevhaların fiziksel özellikleri ve üretim süreci ile ilgili daha fazla araştırma ve analiz yapılması, üretim süreçlerinin iyileştirilmesi ve kalite kontrolü için faydalı olmaktadır. Kaliteyi, üretim verimliliğini artırmanın yanı sıra ekonomik faydalar sağlama açısından da incelemek gerekmektedir [5].…”