Search Results (7)

Wood-based panels (WBPs) have versatile structural applications and are a suitable alternative to plastic panels and metallic materials. They have appropriate strength parameters that provide the required stiffness and strength for furniture products and construction applications. WBPs are usually processed by cutting, milling and drilling. Especially in the furniture industry, the accuracy of processing is crucial for aesthetic reasons. Ensuring the WBP surface’s high quality in the production cycle is associated with the appropriate selection of processing parameters and tools adapted to the specificity of the processed material (properties of wood, glue, type of resin and possible contamination). Therefore, expert assessment of the durability of WBPs is difficult. The interest in the automatic monitoring of cutting tools in sustainable production, according to the concept of Industry 4.0, is constantly growing. The use of flexible automation in the machining of WBPs is related to the provision of tools monitoring the state of tool wear and surface quality. Drilling is the most common machining process that prepares panels for assembly operations and directly affects the surface quality of holes and the aesthetic appearance of products. This paper aimed to synthesize research findings across Medium-Density Fiberboards (MDFs), particleboards and oriented strand boards (OSBs), highlighting the impact of processing parameters and identifying areas for future investigation. This article presents the research trend in the adoption of the new general methodological assumptions that allow one to define both the drill condition and delamination monitoring in the drilling of the most commonly used wood-based boards, i.e., particleboards, MDFs and OSBs. Full article

Many factors (material properties, drill bit type and size, drill bit wear, drilling parameters used, and machine-tool characteristics) affect the efficiency of the drilling process, which could be quantified through the delamination factor, thrust force, and drilling torque. To find the optimal combination among the factors that affect the desired responses during drilling of wood-based composites, various modelling techniques could be applied. In this work, an artificial neural network (ANN) and response surface methodology (RSM) were applied to predict and optimize the delamination factor at the inlet and outlet, thrust force, and drilling torque during drilling of prelaminated particleboards, medium- density fiberboard (MDF), and plywood. The artificial neural networks were used to design four models—one for each analyzed response. The coefficient of determination (R²) during the validation phase of designed ANN models was among 0.39 and 0.96. The response surface methodology was involved to reveal the individual influence of analyzed factors on the drilling process and also to figure out the optimum combination of factors. The regression equations obtained an R² among 0.88 and 0.99. The material type affects mostly the delamination factor. The thrust force is mostly influenced by the drill type. The chipload has a significant effect on the drilling torque. A twist drill with a tip angle equal to 30° and a chipload of 0.1 mm/rev. could be used to efficiently drill the analyzed wood-based composites. Full article

Most of the parts of furniture made of medium density fiberboards (MDF) require at least one hole to be assembled. The drilling technological parameters influence the quality of holes. Factors such as tip angle of the drill bit, feed rate, type and diameter of the drill bit, and spindle rotational speed could affect the drilling process. Therefore, the right choosing of drilling parameters is a mandatory condition to improve the drilling efficiency that is expressed through tool durability, cost, and quality of the drilling. Thus, in this work, we are proposed an approach that consists in combining two modelling techniques, which were successfully applied in various fields, namely artificial neural network (ANN) and response surface methodology (RSM), to analyze and optimize the drilling process of MDF boards. Four artificial neural network models with a reasonable accuracy were developed to predict the analyzed responses, namely delamination factor at inlet, delamination factor at outlet, thrust force, and drilling torque. These models were used to complete the experimental design that was requested by the RSM. The optimum values of the selected factors and their influence on the drilling process of the MDF boards were revealed. A part of optimum combinations among analyzed factors could be used both during the drilling of the MDF boards and prelaminated wood particleboards. Full article

This work presents a cyber-physical drilling machine that incorporates technologies discovered in the fourth industrial revolution. The machine is designed to realize its state by detecting whether it hits or breaks through the workpiece, without the need for additional sensors apart from the position sensor. Such self-recognition enables the machine to adapt and shift the controllers that handle position, velocity, and force, based on the workpiece and the drilling environment. In the experiment, the machine can detect and switch controls that follow the drilling events (HIT and BREAKHTROUGH) within 0.1 and 0.5 s, respectively. The machine’s high visibility design is beneficial for classification of the workpiece material. By using a support-vector-machine (SVM) on thrust force and feed rate, the authors are seen to achieve 92.86% accuracy for classification of material, such as medium-density fiberboard (MDF), acrylic, and glass. Full article

Machinability testing of ordinary wood-based panels can be useful, but testing prototypical (not produced industrially) panels is even more useful. So, the innovative (made only on a laboratory scale) flat-pressed WPCs were the subject of this study. The study consisted of experimental machinability testing of samples of fourteen different types of particleboards. Nine of them were innovative (non-commercial by design) particleboards, which differed from each other in terms of the type of plastic that was used and its percentage. The wood particles were bonded with either polyethylene (PE), polystyrene (PS) or polypropylene (PP). The percentages of plastic were either 30%, 50% or 70%. The research stand used for testing the machinability while drilling was based on a standard CNC (computerized numerical control) machining center. The experimental procedure involved the use of a specialized, accurate system for measuring cutting forces. Moreover, the maximum widths of the damage zones visible around the hole, on the drill entry side and the drill exit side were monitored using a digital camera and graphical software. Two key relative machinability indices were determined (quality problem index and cutting force problem index). Generally, the machinability of wood–polypropylene (W-PP) and wood–polystyrene (W-PS) composites was relatively good and generally similar both to each other and to the machinability of raw, standard particleboard P4. However, wood–polyethylene (W-PE) composite turned out to be the best wood-based board that was tested (even better than standard MDF) from the point of view of the cutting force criterion. On the other hand, the general quality of the holes made in W-PE composite was very poor (not much better than for raw, standard particleboard P5, but clearly better than for standard OSB). Full article

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. Full article

There is increasing use of wood-based composites in industry not only because of the shortage of solid wood, but above all for their better properties such as: strength, aesthetic appearance, etc., compared to wood. Medium density fiberboard (MDF) is a wood-based composite that is widely used in the furniture industry. The goal of the research conducted was to determine the effect of the type of coating on the drill cutting blades on the value of thrust force (F_t), cutting torque (M_c), cutting tool temperature (T) and surface roughness of the hole in drilling MDF panels. In the tests, three types of carbide drills (HW) were used: not coated, TiAlN coated and ZrN coated. The measurement of both the thrust force and the cutting torque was carried out using an industrial piezoelectric sensor. The temperature of the cutting tool in the drilling process was measured using an industrial temperature measurement system using a K-type thermocouple. It was found that the value of the maximum temperature of the tool in the drilling process depends not only on the cutting speed and feed rate, but also on the type of coating of the cutting tool. The value of both the cutting torque and the thrust force is significantly influenced by the value of the feed rate and the type of drill coating. The effect of varying plate density on the surface roughness of the hole and the variation of the value of the thrust force is also discussed. The results of the investigations were statistically analyzed using a multi-factorial analysis of variance (ANOVA). Full article