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This study experimentally investigated the effects of selected factors on the bending moment capacity (BMC) of case furniture joints. The main aim was to explore mixed applications of wood-based materials and fasteners in manufacturing case furniture to reduce material costs. The study examined the effects of the face member material—particle board (PB), plywood (PL), and block board (BB)—edge member material (PB, PL, and BB), and joint shape (T-shape and L-shape) on BMC. Additionally, the study evaluated the effects of joint type (two eccentrics (TE), two dowels (TD), and one eccentric and one dowel (ED)), and material type (PB, PL, and BB) on BMC for L-shaped joints. The results showed that joint shape and face member material significantly affected the BMC of case furniture joint. The BMCs of T-shaped joints were significantly greater than those of L-shaped joints, regardless of the material of the face and edge members, except when the face member was made of PL. For L-shaped joints with PL face members, the BMCs were significantly higher compared to others. Joints constructed with TE exhibited significantly higher BMC compared to ED and TD for the same material type. For PB, TE joints exhibited an increase of approximately 3.0 Nm and 2.0 Nm compared to TD and ED, respectively. For PL, TE showed an increase of 9.1 Nm and 4.1 Nm compared to ED and TD, respectively. For BB, the increases were 7.0 Nm and 6.6 Nm compared to ED and TD. The BMC of joints made with PL and constructed with TE and ED was significantly greater than those of BB, followed by PB. However, for joints assembled with TD, there was no significant difference among the three materials. The ratios of BMC for joints constructed with ED compared to the half-sum of TE and TD were 0.73, 1.04, and 0.79 for PB, PL, and BB, respectively. These results suggest that the face member material predominantly influences the BMC of case furniture joints, indicating the potential to reduce costs by combining different materials and joint types. [ABSTRACT FROM AUTHOR]

In recent years, 3D printing technology has become very important in many fields of science, manufacturing, design, medicine, aviation, sports, etc. Furniture design and manufacturing are also not left out of this trend. In this study, the results of bending moments and stiffness of joints of thin structural elements connected by 3D printing with polylactic acid (PLA) connectors are given. The connectors are newly developed, and information on their strength characteristics is lacking in the literature. Ten joints were investigated, made with 9 and 12 mm plywood and 6 mm MDF. The tested joints constructed by 3D-printed connecting elements show a high strength under arm compression bending load, between 44.16 and 24.02 N·m. The stiffness coefficients of joints with 3D-printed connecting elements are between 348 and 145 N·m/rad and are higher than those of conventional detachable mitre joints but lower than those of glued ones. The type of filling of the hollow section of the connecting elements and the wall thickness influenced the joints' strength and stiffness. Reducing the width of the connecting elements from 40 to 30 mm and the inner radius between the arms from 2 to 1 mm does not significantly affect the joints' strength and stiffness coefficients. [ABSTRACT FROM AUTHOR]

The furniture manufacturing sector faces significant challenges in machining composite materials, where quality issues such as delamination can lead to substandard products. This study aims to improve the classification of drilled holes in melamine-faced chipboard using Explainable AI (XAI) techniques to better understand and interpret Convolutional Neural Network (CNN) models' decisions. We evaluated three CNN architectures (VGG16, VGG19, and ResNet101) pretrained on the ImageNet dataset and fine-tuned on our dataset of drilled holes. The data consisted of 8526 images, divided into three categories (Green, Yellow, Red) based on the drill's condition. We used 5-fold cross-validation for model evaluation and applied LIME and Grad-CAM as XAI techniques to interpret the model decisions. The VGG19 model achieved the highest accuracy of 67.03% and the lowest critical error rate among the evaluated models. LIME and Grad-CAM provided complementary insights into the decision-making process of the model, emphasizing the significance of certain features and regions in the images that influenced the classifications. The integration of XAI techniques with CNN models significantly enhances the interpretability and reliability of automated systems for tool condition monitoring in the wood industry. The VGG19 model, combined with LIME and Grad-CAM, offers a robust solution for classifying drilled holes, ensuring better quality control in manufacturing processes. [ABSTRACT FROM AUTHOR]

Thermosetting polymers offer a wide range of applications in modern industries, including coatings, the automotive and aerospace industries, and furniture manufacturing. The curing process, which is a key step in the application of such materials, has become a research hotspot. More specifically, significant research efforts have been devoted toward shortening the curing cycle and reducing curing energy consumption without affecting product quality. Two common curing methods, namely, infrared (IR) curing and hot air convection curing, have been commonly employed. IR curing technology demonstrates certain advantages, such as high energy efficiency and adaptability, compared to traditional convection curing technology. Moreover, it has achieved desirable results in engineering applications and research. In this review, the current research on IR curing technology is presented in detail based on two fundamental aspects: the heat transfer process and the curing process. This article provides a comprehensive overview of the IR curing heat transfer process in terms of IR heating equipment, heat transfer monitoring devices, heat transfer models, and heat transfer simulation methods. Moreover, it summarizes and compares the basic research methods, equipment, and theoretical models involved in the curing process. In addition, it describes the non-thermal effect and its impact on the properties of cured products. This study describes the author's perspective and opinions on the research direction in IR radiation-based curing technology. This literature review concludes that IR curing technology has strong research value and application prospects, particularly in fields requiring low-temperature rapid curing of thermosetting polymers. [ABSTRACT FROM AUTHOR]

The effective and comprehensive utilization of forest resources has become the theme of the global "dual-carbon strategy". Forestry restructured wood is a kind of wood-based panel made of wood-based fiber composite material by high-temperature and high-pressure restructuring–molding, and has become an important material in the field of construction, furniture manufacturing, as well as derivative processing for its excellent physical and mechanical properties, decorative properties, and processing performance. Taking Medium Density Fiberboard (MDF) as the recombinant material as the research object, an event-triggered synergetic control mechanism based on interventional three-way decision making is proposed for the viscoelastic multi-field coupling-distributed agile control of the "fixed thickness section" in the MDF continuous flat-pressing process, where some typical quality control problems of complex plate shape deviations including thickness, slope, depression, and bump tend to occur. Firstly, the idea of constructing the industrial event information of continuous hot pressing based on information granulation is proposed, and the information granulation model of the viscoelastic plate shape process mechanism is established by combining the multi-field coupling effect. Secondly, an FMEA-based cyber granular method for diagnosing and controlling the plate thickness diagnosis and control failure information expression of continuous flat pressing is proposed for the problems of plate thickness control failure and plate thickness deviation defect elimination that are prone to occur in the continuous flat-pressing process. The precise control of the plate thickness in the production process is realized based on event-triggered control to achieve the intelligent identification and processing of the various types of faults. The application test is conducted in the international mainstream production line of a certain type of continuous hot-pressing equipment for the production of 18 mm plate thickness; the synergistic effect is basically synchronized after 3 s, the control accuracy reaches 30%, and the average value of the internal bond strength is 1.40, which ensures the integrity of the slab. Practical tests show that the method in the actual production is feasible and effective, with detection and control accuracy of up to ±0.05 mm, indicating that in the production of E0- and E1-level products, the rate of superior products can reach more than 95%. [ABSTRACT FROM AUTHOR]

Product design and manufacturing leverage 3D scanning for various applications. This study aims to investigate the effectiveness of 3D scanning in furniture production by surveying the literature and showcasing four real-world case studies. The literature review reveals that 3D data acquired from real-world objects have applications in research, rapid prototyping, restoration, and preservation of antique furniture, optimizing CNC machining processes, and measuring furniture components for quality control. The case study descriptions demonstrated the circumstances, rationale, and methodology for 3D scanning. All the case studies analyzed stem from the collaboration between the Laboratory for Product Development and Design at the Faculty of Mechanical Engineering at the University of Sarajevo and various furniture production enterprises from Bosnia and Herzegovina. The conclusions highlight that 3D scanning in the furniture sector is advantageous for developing computer-aided design models from early-stage design prototypes, validating the dimensional accuracy of manufactured components by comparing with CAD models, safeguarding and reconstructing vintage furniture, and remanufacturing formerly produced goods that lack complete technical records (reverse engineering). [ABSTRACT FROM AUTHOR]

This paper tackles the issue of moisture variation in wood-based materials, explicitly focusing on melamine-coated particleboard (hereafter referred to as melamine) and medium-density fiberboard (MDF) used in the third phase of wood industry transformation. The approach involves a comprehensive strategy for predicting moisture content variation, incorporating numerical simulation, experimental testing, and the application of artificial neural network (ANN) technology to enhance accuracy in furniture manufacturing. The developed ANN models are tailored to predict moisture content changes under specific thermal comfort conditions. Remarkably, these models demonstrate high precision, with an average error margin of only 1.40% for 8% moisture content (MC) and 2.85% for 12% MC in melamine, as well as 1.42% for 8% MC and 2.25% for 12% MC in MDF. These levels of precision surpass traditional models, emphasizing this study's novelty and practical relevance to the industrial context. The findings indicate that ANN models adapt to diverse environmental conditions, presenting a robust tool for optimizing moisture management in wood-based materials. This research contributes valuable insights for improving the reliability and efficiency of moisture content predictions in the wood industry. [ABSTRACT FROM AUTHOR]

In today's world, a situational awareness of sustainability is becoming increasingly important. Leaving a better world for future generations is becoming the main interest of many studies. It also puts pressure on managers to change production methods in most industries. Reducing carbon emissions in industry today is crucial to saving our planet. Theoretical research and practical industry requirements diverge, even though numerous researchers have tackled various strategies to handle carbon emission problems. Therefore, this work considers the carbon emission problem of the furniture manufacturing industry in Hosur, Tamilnadu, India. The case study company has a manufacturing system that resembles a hybrid flow shop (HFS) environment. As the HFS scheduling problems are NP-hard in nature, exact solution techniques could not be used to solve the problems. Hence, a sequential hybrid optimization algorithm (SHOA) has been developed in this paper to minimize the carbon footprint. In the SHOA, the pigeon-inspired optimization algorithm (PIOA) is hybridized sequentially with the firefly algorithm (FA). A computational experimental design is proposed to analyze the efficiency of the introduced strategy, and the solutions indicate that the developed approach could reduce the carbon footprint by up to 9.82%. The results motivate us to implement the proposed algorithm in the manufacturing industry to reduce the carbon footprint. [ABSTRACT FROM AUTHOR]

Plywood is a wood-based composite with many applications in construction, shipbuilding, and furniture production. One of the basic plywood processing and mandatory operations is drilling. Up to now, considerable and very diverse thematic research has been recently carried out on drilling, but little of that deals with modeling of the drilling process of plywood. Therefore, in this work, the artificial neural network modeling technique and response surface methodology were applied to model and optimize the drilling process of plywood. Two artificial neural network models were developed to predict the thrust force and the drilling torque based on drill tip angle, tooth bite, and drill type. The developed ANN models were used to complete the value of responses in the experimental design, which was requested by the response surface methodology. The trust force during the drilling of plywood is significantly influenced by the drill type (helical or flat). The most significant factor that affects the drilling torque during the drilling of plywood is the tooth bite. A helical drill assures a lower minimum thrust force and drilling torque than a flat drill. The proposed method could be used as an optimization tool during the design phase of the furniture manufacturing process. [ABSTRACT FROM AUTHOR]

Green supplier selection is always one of the most important challenges in all of supply chain management, especially for production companies. The purpose is to have reliable suppliers which can fulfill all requests and be flexible in any supply chain stage. The aim of this paper is to create an adequate and strong MCDM (multicriteria decision making) model for the evaluation and selection of suppliers in a real environment. The main contribution of this study is proposing a novel fuzzy–rough MCDM model containing extension stepwise weight assessment ratio analysis (SWARA) and additive ratio assessment (ARAS) methods with fuzzy–rough numbers (FRN). The integrated FRN SWARA–FRN ARAS model was implemented in a case study of eco-friendly material production. The FRN SWARA method was used to calculate the weights of 10 green criteria, while using FRN ARAS, 6 suppliers were evaluated. The results of the applied model show that supplier S3 received the highest ranking, followed by supplier S2, while supplier S5 performed the poorest. In order to verify the strengths of the developed fuzzy–rough approach, we created a comparative analysis, sensitivity analysis, and dynamic matrix, which confirm the robustness of our model. [ABSTRACT FROM AUTHOR]

Machine vision technology was integrated into the manufacturing workshop, to achieve an effective and high-quality production mode for furniture manufacturing. Machine vision can be used for information collecting, quality detecting, positioning, automatic sorting, intelligent monitoring, etc., which largely make up for the shortcomings of poor quality, low precision, low efficiency, and high labor intensity of manual operation. In this study, the method of systematic literature review was applied, and 128 relevant literatures in the field of machine vision application in manufacturing were retrieved and screened from 2011 to 2022. Statistical analysis was carried out on the extracted application directions and related technologies. The current status of machine vision technology's implementation in furniture manufacturing was summarized. In view of the new demand of the rapid development of intelligent manufacturing, the challenges, faced by machine vision, were also summarized. To build a more intelligent, comprehensive, and effective manufacturing workshop for wooden products, cutting-edge technologies, such as deep learning and 3D point cloud, must be further integrated into machine vision. This study can efficiently assist the pertinent practitioners in furniture manufacturing in quickly grasping the pertinent technical principles and future development directions of machine vision, which would be benefit for accomplishing intelligent manufacturing. [ABSTRACT FROM AUTHOR]

In this article, an approach to drill wear evaluation is presented. Tool condition monitoring is an important problem in furniture manufacturing and similar industries. At the same time, approaches that rely on sets of sensors, often tend to be to robust or complex for the production environment. Instead of signals acquired from dedicated sensors, presented approach uses images of drilled holes as input data. Initial pictures are processed and enhanced in order to highlight the crucial properties. A set of selected features is then calculated on the resulting images, and later used during the training of 5 state-of-the-art classifiers. Presented research also evaluates number of images for consecutive drillings that needs to be taken into account in order to produce accurate results. From the selected set, the best performing classifier was Random Forest and it achieved close to 100% accuracy. [ABSTRACT FROM AUTHOR]

Drilling into melamine-faced-wood-based panels is one of the most common processes in modern furniture manufacturing. Delamination is usually the main and the most troublesome quality defect in this case. A lot of scientific studies draw the conclusion that the progress of tool wearing during the cutting of wood-based materials is the key problem. Therefore, tool condition monitoring and the replacement of worn tools at the right time is the most useful and common (in the industrial practice) way to reduce delamination. However, the automation of this process is still a problem due to various issues. There is yet no commercial (even prototypical) offer for the furniture industry in this regard. For this reason, it is considered advisable to try to use the multilayer perceptron (MLP) algorithm to automatically identify a drill's condition during drilling in a laminated chipboard. It has been established that, for practical purposes, it is important to distinguish between the three different classes of tool conditions, which can be conventionally described as "Green" (keep working), "Red" (implicitly stop and replace) and "Yellow" (warning signal—stop and replace if you want to avoid deterioration in cutting quality). To register the signals generated in the cutting zone and those constituting the basis for the identification of the tool condition in the "on-line" mode, the following elements were used: contact sensor of acoustic emission, accelerometer for vibration, two-component force gauge and a microphone. The classification effects (with an overall accuracy above 70%) were ultimately fairly decent but slightly worse than those of the classification algorithms tested earlier (i.e., "nearest neighbors" or "support vector machine" algorithms). The most troublesome, however, is the fact that serious errors (mistakes between "Green" and "Red" classes) were occasionally noted (for about 1% of the analyzed cases). [ABSTRACT FROM AUTHOR]

Particleboards are used in the manufacturing of furniture and are often part of the interior of buildings. In the event of a fire, particleboards are a substantial part of the fuel in many building fires. The aim of the article is to monitor the effect of radiant heat on the surface of particle board according to the modified procedure ISO 5657: 1997. The significance of the influence of heat flux density and particle board properties on its thermal resistance (time to ignition) was monitored. Experimental samples were used particle board without surface treatment, with thicknesses of 12, 15, and 18 mm. The samples were exposed to a heat flux from 40 to 50 kW·m−2. The experimental results are the initiation characteristics such as of the ignition temperature and the weight loss. The determined factors influencing the time to ignition and weight loss were the thickness and density of the plate material, the density of the radiant heat flux and the distance of the particle board from the radiant source (20, 40, and 60 mm). The obtained results show a significant dependence of the time to ignition on the thickness of the sample and on the heat flux density. The weight loss is significantly dependent on the thickness of the particle board. Monitoring the influence of time to ignition from sample distance confirmed a statistically significant dependence. As the distance of the sample from the source increased, the time to ignition decreased linearly. As the distance of the sample from the source increased, the time to ignition increased. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Monitoring consumer buying behaviors in terms of their preferences and attitudes has been known as an important driver for the success and development of various industries, including a wood furniture manufacturing. The aim of this study was to identify and compare the purchasing behaviors of furniture consumers in the period before and during the COVID-19 pandemic. Two surveys were conducted in Croatia, the first in April and May 2020 with the assumption that consumer purchasing behaviors will change during the COVID-19 pandemic in the following year and the second one during March 2021. Differences regarding consumer purchasing behaviors and preferences for wood furniture between respondents regarding demographic and economic factors before and during the pandemic were found. The coronavirus pandemic is teaching us all hard lessons about resilience and adaptability, and new opportunities and solutions are essential. Nowadays, it is becoming increasingly important to produce information. Research offers insights into future design and building communication to better meet the information needs of different types of consumers and to more broadly increase the acceptance and appeal of wood furniture in society in the spirit of sustainable and bio-based circular economy. [ABSTRACT FROM AUTHOR]

Drilling operations are an essential part of furniture from MDF laminated boards required for product assembly. Faults in the process might introduce adverse effects to the furniture. Inspection of the drilling quality can be challenging due to a big variety of board surface textures, dust, or woodchips in the manufacturing process, milling cutouts, and other kinds of defects. Intelligent computer vision methods can be engaged for global contextual analysis with local information attention for automated object detection and segmentation. In this paper, we propose blind and through drilled holes segmentation on textured wooden furniture panel images using the UNet encoder-decoder modifications enhanced with residual connections, atrous spatial pyramid pooling, squeeze and excitation module, and CoordConv layers for better segmentation performance. We show that even a lightweight architecture is capable to perform on a range of complex textures and is able to distinguish the holes drilling operations' semantical information from the rest of the furniture board and conveyor context. The proposed model configurations yield better results in more complex cases with a not significant or small bump in processing time. Experimental results demonstrate that our best-proposed solution achieves a Dice score of up to 97.89% compared to the baseline U-Net model's Dice score of 94.50%. Statistical, visual, and computational properties of each convolutional neural network architecture are addressed. [ABSTRACT FROM AUTHOR]

The study of industrial volatile organic compound (VOC) emission inventories is essential for identifying VOC emission levels and distribution. This paper established an industrial VOC emission inventory in 2015 for Hebei Province and completed an emission projection for the period 2020–2030. The results indicated that the total emissions of industrial VOCs in 2015 were 1017.79 kt. The use of VOC products accounted for more than half of the total. In addition, the spatial distribution characteristics of the industrial VOC emissions were determined using a geographic information statistics system (GIS), which showed that the VOCs were mainly distributed the central and southern regions of Hebei. Considering the future economic development trends, population changes, related environmental laws and regulations, and pollution control technology, three scenarios were defined for forecasting the industrial VOC emissions in future years. This demonstrated that industrial VOC emissions in Hebei would amount to 1448.94 kt and 2203.66 kt in 2020 and 2030, with growth rates of 42.36% and 116.51% compared with 2015, respectively. If all industrial enterprises took the control measures, the VOC emissions could be reduced by 69% in 2030. The analysis of the scenarios found that the most effective action plan was to take the best available control technologies and clean production in key industries, including the chemical medicine, coke production, mechanical equipment manufacturing, organic chemical, packaging and printing, wood adhesive, industrial and construction dye, furniture manufacturing, transportation equipment manufacturing, and crude oil processing industries. [ABSTRACT FROM AUTHOR]

This work is devoted to the study of the effect of ultraviolet rays for the surface activation of pine wood thermally modified at temperatures of 180−240 °C in order to increase the surface roughness, enhance the wettability of thermal wood and the adhesive strength of the glue in the production of wood block furniture panels. Studies were carried out to measure the contact angle of wettability of thermally modified wood samples of pine, as a result of which it was determined that the ultraviolet treatment process contributes to an increase in the adhesion properties of the surface layer of thermally modified wood by more than 13% due to the reactivity of ultraviolet rays to oxidize and degrade ligno-containing wood products. At the same time, the most active process of surface activation takes place during 60 min of ultraviolet irradiation of wood with a total irradiation of at least 125 W/cm2. It was revealed that the combined effect of two-stage wood processing, including preliminary volumetric thermal modification followed by surface ultraviolet treatment, causes an increase in the moisture resistance of glued wood products by 24%. So, if the strength of the glue seam when gluing natural wood samples after boiling decreased by 46%, then the samples that underwent two-stage processing showed a decrease only by 22%. In connection with the results obtained, an improved technology for the production of furniture boards for the manufacture of moisture-resistant wood products is proposed. [ABSTRACT FROM AUTHOR]

Seeking a more flexible and efficient production, additive manufacturing (AM) has emerged as a major player in the industrial field, streamlining the fabrication of custom tangible assets by directly 3D printing them. However, production still takes too long due to printing, but also due to the product design stage, in which the customer works together with an expert to create a 3D model of the targeted product by means of computer-aided design (CAD) software. Skipping intermediate agents and making customers responsible for the design process will reduce waiting times and speed up the manufacturing process. This work is conceived as a first step towards that optimized AM model, being aimed at bringing CAD tools closer to clients through an enhanced user experience, and consequently at simplifying pre-manufacturing design tasks. Specifically, as an alternative to the traditional user interface operated with the keyboard and mouse duo, standard in CAD and AM, the paper presents a comprehensive multi-touch interaction system conceived as a customer-centric human-machine interface. To depict the proposed solutions, we adopt furniture manufacturing as a case study and, supported by a CAD-like software prototype for 3D modeling of custom cabinets introduced in a previous work of the authors, we assess our approach's validity in terms of usability by conducting in-lab and remote user studies. The comparison between the designed multi-touch interaction and its desktop alternative yields promising results, showing improved performance and higher satisfaction of the end-user for the touch-based approach, that lay the groundwork for a smarter factory vision based on remotely-operated AM. [ABSTRACT FROM AUTHOR]

As an important industry of the national economy, the development of furniture manufacturing industry is very rapid. In particular, with the development of panel furniture industry, wood-based panels have become a necessary choice for furniture material for modern families in recent years. As a new particleboard material, in order to be more widely used in the furniture industry, it is not enough to have the characteristics of environmental protection. The material should also have excellent appearance and dimensional stability, so as to change people's dependence on traditional wood-based panels. In this study, the rice straw particleboard (RSP) substrate was veneered by Betula sp. and Cyclobalanopsis glauca. In the process of veneering, different RSP specimens were treated by different sanding thicknesses and moisture contents of the RSP substrate, glue spread, species and thickness of veneer. The dimensional stability of different RSP specimens after veneering was analyzed. Based on the same variables, the change in the panel dimension and warp degree of the specimens of RSP which the sanding thickness was 0.2 mm were higher than the specimens with a sanding thickness of 0.6 mm. The dimensional stability of specimens of Cyclobalanopsis glauca veneer was better than that of Betula sp. veneer. A certain degree of change within the appropriate moisture content had a little effect on dimensional stability of veneered RSP. The greater of the amount of glue, the worse the dimensional stability of veneered RSP. The thinner the veneer, the worse was the dimensional stability of the veneered RSP. [ABSTRACT FROM AUTHOR]

This study analyzed the variability of truckloads of large-sized pine logs transported to a furniture manufacturing mill, depending on the origin of the timber, delivery period, length of the transported logs, volume of the load and its mass. It was assumed that the volume and mass of the transported timber load depends on the season of the year and the origin of the timber, which has a significant impact on the mass of one cubic meter of the load. The analysis of the wood origin parameters (location of its growth) took into account the type of forest habitat, soil type and stand abundance. The characteristics of the tree parameters from which the roundwood was obtained took into account age, diameter at breast height (DBH) and height of the stand. The origin of the timber was determined for 1063 timber loads from 40 forest districts transported over a 12 month period. We obtained an average timber load volume of 29.34 m3 which includes single shipments differing significantly in their volume of between 24.21 and 36.51 m3. The origin of the timber, habitat conditions and stand parameters of the harvesting site influence load mass and the mass of 1 m3 of load. The delivery date has an impact on the studied elements of the sawlogs load, with two similar periods—spring and summer. The volume of the transported timber is already at a stabilized level in accordance with legal regulations, which is no longer dependent on other factors and only on the accepted conversion factors for roundwood density and the mass of the empty vehicle set. This has not influenced a reduction in the mass of the transported timber. [ABSTRACT FROM AUTHOR]

The objective of this experimental study was to evaluate the surface roughness and hardness of laminated wood-based composite panels as a function of exposure to high relative humidity (RH). All samples were conditioned in a room having a temperature of 20 °C and a relative humidity of 65% before the tests were carried out. Surface roughness, Janka hardness and mass change rate of the raw and overlaid samples were determined before and after humidity exposure. The surface of the overlaid samples was also investigated by SEM. The stylus method was used to determine the fluctuations of the surface quality of the raw and overlaid composites. The surface quality and hardness of the samples were influenced by increases of the RH level and exposure time. The mass rate change was higher for the raw samples compared with the overlaid ones. The findings of this study can be applied to improve production techniques in furniture manufacturing and to enhance the use of overlaid composite panels. [ABSTRACT FROM AUTHOR]

At present, technical regulations are adopted for furniture products of various types, establishing the protection of human health from the influence of harmful substances in the parts and assemblies of wood products and wood materials. The technical regulation adopted the requirements for ensuring chemical safety in the design and manufacture of furniture. The technical regulation is based on the condition of furniture products, eliminating the risk of harm to human health due to exceeding the maximum permissible concentration of harmful chemicals in indoor air. Such substances include free formaldehyde, migrating from the volume of chipboard and glue interlayers when gluing and veneering furniture parts and assemblies. Harmful chemicals are vapor-gas mixtures of solvents, the migration of which is observed from solid lacquer membrane of protective and decorative coatings of parts and components of furniture into the indoor air environment. These chemicals are present in the construction and auxiliary materials in the development of furniture. It is desirable that manufacturers of furniture products determine in advance, at the stage of its design, the level of safety when using certain raw materials. For this purpose, a technique is proposed that allows us to determine the concentration of harmful substances in the air of the premises with the selected materials, design and manufacturing technology of furniture products. Preliminary calculations based on the proposed method, allows us to determine the life of the furniture, in which the harmful substances migrating into the air environment of the premises will be within the permissible concentrations. This allows certifying the manufactured furniture products for chemical safety with the assignment of certain quality parameters. The proposed technique may be useful for enterprises focused on the production of furniture products. [ABSTRACT FROM AUTHOR]