In this work, the current status, technical capabilities, and development trends of the Chinese intelligent furniture industry were in focus. Based on combining a literature review with field investigations and analysis of major scientific research projects in Zhejiang Province, China, an in-depth overview and discussion about previous experience, features, technologies, products and control methods in the intelligent furniture industry in China were conducted. The key technologies in current Chinese intelligent furniture industry include embedded systems, sensors, short-range wireless communication, artificial intelligence and intelligent interaction techniques. This work also mentions the challenges and opportunities for the industry, pointing out how to innovate and dominate the furniture market in the era of transformation from the traditional furniture industry to intelligent upgrading. The results are proposed to help readers from all over the world to understand the development trend and progress of the Chinese intelligent furniture industry.

Enhancing energy efficiency is the key to realizing green manufacturing. One major area of interest in this regard is the improvement of energy efficiency of machine tools during the production of building materials. This project focuses on energy efficiency during the spiral milling of wood plastic composites. To this end, a response surface method was adopted to develop a model and establish the relationship between energy efficiency and milling conditions. Analysis of variance based on individual factors as well as two-factor interactions was performed to gauge their effects on energy efficiency. It was found that milling depth was positively correlated to power efficiency, while spiral angle and feed per tooth displayed non-monotonic behavior. An attempt was made to predict milling conditions that will yield the greatest material removal rate and power efficiency. For wood plastic composites subjected to up-milling, it was determined that a feed per tooth of 0.1 mm, milling depth of 1.5 mm, and spiral angle of 70° were ideal. Considering the potential improvements in energy efficiency and surface quality that these process parameters will bring, it is strongly recommended for use in the industrial machining of wood plastic composites.

In the intelligent manufacturing of furniture, the power signal has the characteristics of low cost and high accuracy and is often used as a tool wear condition monitoring signal. However, the power signal is not very sensitive to tool wear conditions. The present work addresses this issue by proposing a novel woodworking tool wear condition monitoring method that employs a limiting arithmetic average filtering method and particle swarm optimization (PSO)-back propagation (BP) neural network algorithm. The limiting arithmetic average filtering method was used to process the power signal and extracted the features of the woodworking tool wear conditions. The spindle speed, depths of milling, features and tool wear conditions were used as sample vectors. The PSO-BP neural network algorithm was used to establish the monitoring model of the woodworking tool wear condition. Experiments show that the proposed limiting arithmetic average filtering method and PSO-BP neural network algorithm can accurately monitor the woodworking tool wear conditions under different milling parameters.

Bleaching is a common modification method widely used in the industrialization of wood dyeing. Bleaching can regulate the color of the wood, and it also has great effects on the subsequent wood dyeing properties. In this work, three woods, Ayous, Linden, and Poplar, were processed by using the NaOH/H₂O₂/Na₂SiO₃ bleach pretreatment method. Then, the pretreated wood and untreated wood were stained separately by means of water bath dyeing with three dyestuffs: blue anthraquinone and red and yellow double-azo dyestuffs. The study mainly focused on the effect of the bleach pretreatment on the color control and dyeing properties of the wood by analyzing the color difference, staining rate, and final dyeing rate of the dyed wood. The results were as follows: For the color difference, the L* and h* values showed increasing tends, while the a*, b*, and c* values showed decreasing trends. For the staining rate, Ayous reached an equilibrium staining rate at 3~4 h, but for the others, this was not obvious. For the dyeing rate, red and blue Linden veneers and blue Ayous veneers were similar to the unbleached ones, while the final dyeing rate of the other veneers was less than that of the original wood, and the staining rate of the Ayous red and yellow veneer, Linden yellow veneer, and Poplar veneer was less than that of the original wood. The final dyeing rate of the Ayous red and yellow veneers, Linden yellow veneer, and Poplar red, yellow and blue veneers decreased by 4.54%, 2.91%, 5.45%, 10.75%, 2.66%, and 9.55%, respectively. In general, the bleach pretreatment made the staining color of the material surface uniform. The dyeing rate increased due to the increase of the veneers’ permeability. Thus, the veneers quickly reached the equilibrium staining rate, but the equilibrium staining rate of some test pieces decreased. This work provides scientific support for the dyeing process.

A new design adhesive mixed with flame retardant was developed by an optimized and modified dedicated flame retardant and selected at a suitable proportion between the adhesive and flame retardant as well as the coating amount of the adhesive. The new design adhesive was applied to ecological board production, and the flame-retardant properties of products were examined. The dipping and peeling properties, surface bonding strength, and formaldehyde emission reached the national standard GB/T 34722-2017, the flame retardancy meets the requirements of GB/T 8626-2017, GB/T 20284-2006, GB/T20285-2006, and it also reaches the B1-C level (the nonflammable level in the flame retardant level). This study not only has theoretical guidance but also has strong practical value to provide a basis and data support for the research and development of flame-retardant ecological boards.