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Due to their outstanding properties, bamboo-based fiber composites are gaining significant traction in the fields of construction and decoration. Among the crucial process steps in their production, hot pressing stands out as a pivotal one. Temperature, being a key parameter in hot pressing, and its stability of control exert a profound impact on the finished mechanical properties and associated indices of bamboo-based fiber composites. In this investigation, we introduce an active disturbance rejection control (ADRC) methodology specifically tailored for the hot-pressing temperature of bamboo-based fiber composites. A mathematical model encompassing the motor, steam regulating valve, and, ultimately, the hot-pressing temperature is formulated, with the transfer functions at each level being precisely determined through parameter identification techniques. The simulation outcomes reveal that in the absence of signal interference, sinusoidal signal interference, or random signal interference, the ADRC method outperforms the traditional PID algorithm in the realm of hot-pressing temperature control for bamboo-based fiber composites. This approach effectively reduces the temperature fluctuations of the PID algorithm, thereby averting issues such as premature curing or board bursting. In summary, this study holds significant implications for enhancing the mechanical properties of bamboo-based fiber composites. [ABSTRACT FROM AUTHOR]

Bamboo-based fiber composites, known as recombinant bamboo, have emerged as a new material in the construction and decoration industry. With its excellent mechanical and ornamental properties, recombinant bamboo is gaining popularity. However, its high hardness and abrasion resistance pose challenges in the milling process. To address this, we conducted an experimental study to investigate the milling machinability of recombinant bamboo. We studied the impact of various factors—fiber angle, feed rate, and spindle speed—on the tangential and normal roughness of milled surfaces. Our findings indicated that increasing the spindle speed within an acceptable range can effectively mitigate issues such as carbonization and endface cracking on a milled surface. Additionally, we developed a prediction model to assess the probability of end splitting in recombinant bamboo. This research aimed to enhance the milling quality of recombinant bamboo, improve control over surface roughness, reduce the likelihood of end splitting, and, ultimately, expand application possibilities. [ABSTRACT FROM AUTHOR]

The surface roughness of wood has a great influence on its performance and is a very important indicator in processing and manufacturing. In this paper, we use the central composite design experiment (CCD experiment) and artificial neural network (ANN) model to study the changing pattern of surface roughness during the high-speed milling process of pine wood. In the CCD experiments, the spindle speed, feed speed, and depth of cut are used as the influencing factors, and the surface roughness is used as the index to analyze the variation law and fit the surface roughness parameter equation. By measuring the chip size in each group in the CCD experiment, the ANN model is used to predict the surface roughness under this machining parameter by measuring the chip size in each test group. The experimental results showed that the mean error of the surface roughness prediction values in the CCD experiment (12.2%) was larger than that of the ANN model (7.8%), and the mean squared error (MSE) of the ANN model was 0.025, the mean absolute percentage error(MAPE) was 0.01, and the coefficient of determination R2 was 0.95. Compared with the CCD experiment, the ANN model had a higher prediction accuracy. The results of this paper can provide some guidance for the prediction of surface roughness during wood processing. [ABSTRACT FROM AUTHOR]