Three-layered composite materials consisting in wood-chips bonded by a resin (particleboard) were successfully produced from two by-products: plum and apple tree branches, waste biomass produced after orchard pruning; a third particleboard was made from an industrial softwood blend as a reference. After collecting the branches, the raw materials were characterized in matters of density, bark content and bulk density of particles. Various physical and mechanical properties were assessed in accordance with European Standards for wood-based panels, namely thickness swelling, water absorption, modulus of rupture, modulus of elasticity, internal bonding strength, screw withdrawal resistance, as well as density profile and machining properties during milling and drilling. Modulus of rupture was 14.4 ± 1.2 N mm⁻² for plum-based panels and 12.8 ± 2.3 N mm⁻² for apple-based panels, while modulus of elasticity was 2576 ± 140 N mm⁻² for plum-based panels and 2432 ± 345 N mm⁻², which fulfills the mechanical requirements for panels of furniture production and interior equipment purposes which of this kind of materials is modulus of rupture of 11 N mm⁻² and modulus of elasticity of 1600 N mm⁻². The results showed in general, a good performance of the composite materials elaborated from waste biomass, thus offering a potential revalorization of a rather undervalued by-product.

In this article, we present a novel approach to tool condition monitoring in the chipboard milling process using machine learning algorithms. The presented study aims to address the challenges of detecting tool wear and predicting tool failure in real time, which can significantly improve the efficiency and productivity of the manufacturing process. A combination of feature engineering and machine learning techniques was applied in order to analyze 11 signals generated during the milling process. The presented approach achieved high accuracy in detecting tool wear and predicting tool failure, outperforming traditional methods. The final findings demonstrate the potential of machine learning algorithms in improving tool condition monitoring in the manufacturing industry. This study contributes to the growing body of research on the application of artificial intelligence in industrial processes. In conclusion, the presented research highlights the importance of adopting innovative approaches to address the challenges of tool condition monitoring in the manufacturing industry. The final results provide valuable insights for practitioners and researchers in the field of industrial automation and machine learning.

This paper presents the particle-size distribution of dust created by the drilling of selected wood composites, which was carried out using a CNC machine. The particle-size distribution was studied through two methods. Two analyses were performed: the sieve analysis of samples from the whole mass of collected dust and the laser diffraction analysis of the finest fraction isolated by sieving. The results presented general information about the particle-size distribution of the dust, as well as detailed information on the content of the finest particles. This information revealed that the particles might pose a potential risk to the health of workers employed in the woodworking industry. This potential risk is due to the possibility of their dispersion in the atmosphere surrounding the workplace and their size, which allows them to be respirable. The relationship between the fineness of the dust and the type of wood composite was also tested. Most ultrafine particles are formed during the drilling of fibreboards and are especially produced in traditional wet technology.

In this paper, we analyze the possibilities of the protection of tools for wood machining with PVD (Physical Vapor Deposition) hard coatings. The nanolayered TiN/AlTiN coating, nanocomposite TiAlSiN coatings, and single layer TiN coating were analyzed in order to use them for protection of tools for wood machining. Both nanostructured coatings were deposited in an industrial magnetron sputtering system on the cutting blades made of sintered carbide WC-Co, while TiN single layer coating was deposited by evaporation using thermionic arc. In the case of TiN/AlTiN nanolayer coatings the thickness of the individual TiN and AlTiN layer was in the 5–10 nm range, depending on the substrate vertical position. The microstructure and chemical composition of coatings were studied by scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) method. Additionally, in the case of the TiN/AlTiN coating, which was characterized by the best durability characteristics, the transmission electron microscope (TEM) and X-ray photoelectron spectroscopy (XPS) methods were applied. The coatings adhesion to the substrate was analyzed by scratch test method combined with optical microscopy. Nano-hardness and durability tests were performed with uncoated and coated blades using chipboard. The best results durability characteristics were observed for TiN/AlTiN nanolayered coating. Performance tests of knives protected with TiN and TiAlSiN hard coatings did not show significantly better results compared to uncoated ones.

Particle size of wood fillers used in FDM 3D printing filaments is a topic not commonly discussed in the literature. Research on traditional wood–polymer composites (WPCs) suggests that bigger particles improve the composite’s tensile properties. Is that the case at the 3D printing scale? Five variants of composites were prepared using recycled PLA and sawdust, differentiated by filler particle size (<0.2 mm, 0.2 mm–0.4 mm, 0.4 mm–0.6 mm, 0.6 mm–0.8 mm, 0.8 mm–1 mm). Current draw during extrusion, as well as tensile strength and tensile modulus, were tested. Test results of tensile strength, ranging from 9.21 MPa to 14.28 MPa, and tensile modulus, ranging from 802 MPa to 1014 MPa, have shown no clear correlation between wood particle size and tensile properties of the composites at the 3D printing scale. A clear increase in forces needed to extrude composites containing larger particles of wood was discovered, as well as the inability to extrude composites filled with the biggest tested particle size. To further explore this topic, SEM/EDS imaging of the tested composites was performed. Based on the test results, wood particle sizes ranging from one-fifth to one-half of the nozzle size are recommended for use as fillers in wood–PLA composites intended for 3D printing.

The influence of different types of cemented carbide blades and thickness of TiAlN/a-C:N and TiN/AlTiN protective coatings used in the wood industry on cutting performance has been studied. Three types of WC-Co cemented carbide blades with different cobalt content were used in the study. The thicknesses of both types of coatings were ~2 and ~5 µm. The structure, chemical and phase composition were studied using transmission and scanning electron microscopy (TEM, SEM), X-ray dispersion spectroscopy (EDX) and X-ray diffraction (XRD), respectively. The adhesion was evaluated by scratch test. Nanohardness and durability tests of uncoated and coated blades were performed. We found that the blades covered with 5 µm TiN/AlTiN coatings exhibited the best durability characteristic. The cutting distances were within the range ~6700-~7080 depending on the substrates in comparison with pure substrates (~4300–~4900) and 2 µm TiN/AlTiN coatings (~5400–~6600). The presence of a thin and soft outer a-C:N layer aggravates the nanohardness and durability of the coated blades.