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Bio-based products show a great potential to reduce the dependence on fossil resources. Germany currently has a forerunner role in the EU, using biomass as the same resource base for material and energetic utilisation.
Beech wood is hyped in both ways: In material usage directly e.g., in the building sector to balance the possible scarcity of soft wood in the future, as well as indirectly as a feedstock for biorefineries, representing a long-term alternative to the fossil-based industry, and secondly, as energetic use due to its excellent burning quality.
The BIOECONOMY CLUSTER sees itself as an actor in a difficult tension field within implementation of the wood-based Bioeconomy. With a total stocking volume of about 635 million m3, beech is the most abundant hardwood tree species in Germany. Nevertheless, beech wood-based biorefineries compete in a difficile tension field.
One crucial factor is the reliable supply of raw materials - a consistent biomass quality, season-independent availability and efficient logistics of the raw material.
However, the production processes are technically feasible; thus, finally, there is no guarantee for selling the bio-based products to the obtained price.
How to overcome the strong tension field in the beech-based Bioeconomy? Different selected examples show possible approaches. To manage the wood-based Bioeconomy and the beech wood supply chain effectively, the influencing of factors and the managing of the same is crucial.
This paper presents a method which allows to assess the wettability of thermomechanical pulp (TMP) with thermoplastic. The method is based on the usage of laboratory sheets made of TMP and plastic granules which form drops under heat. The contact angle of the drops after re-solidification on the surface of the laboratory sheets serves as the wetting characteristic. This method enables a direct and evaluable testing of wood fiber thermoplastic combinations. The exemplary results indicate that wood species, pulping temperature and mercerization influence the wettability of TMP with thermoplastic.
Investigation of tool wear of the trimming unit and resulting quality in the edgebanding process
(2023)
The edgebanding process, i.e. the covering of the raw edge of the laminated particleboard by means of decorative plastic or real wood veneer bands, is an important process step in the manufacture of furniture components. The quality of the final product is significantly influenced by this production step. The manufacturing process step within the edgebanding machine begins with the milling of the raw edge. The quality of the edgebanding process is determined by a large number of process input variables, such as the tool used and the cutting geometry, the type of laminate used for the wood-based material, adhesives, etc. The quality of the edgebanding process is also determined by the type of material processed. Particularly with regard to the materials, the increased use of recycled wood in the particleboard sector and different types of adhesives (with reduced formaldehyde emissions) results in increased demands on the milling process. Due to this developments, most of the earlier investigations (in the years 1990 - 2000) are not comparable with today’s general conditions. The mechanical and optical properties of the edge applied to the end product are decisively influenced by the quality of this joint. The milling technology used, as well as the tool used for this purpose and its condition, is a decisive influencing factor in the formation of quality. Tests on the indication of various influencing variables in the edgebanding process at the Rosenheim Technical University of Applied Sciences showed that in the case of chipboard milling, a decrease in the electrical power required for the cutting process can take place with increasing tool wear. It was also demonstrated that previous process steps in furniture production, such as panel dividing by means of sawing or milling/nesting, generally have a significantly different influence on the subsequent joining process during edge banding process. Furthermore, a direct correlation was determined between the mechanical properties of the end product and the condition of the tool used, which can be used as a measurable indicator. In addition to the effect of the tool condition on the mechanical properties of the edge banding, this also has an impact on the optical quality characteristics. Accordingly, it could be demonstrated here that an increase in the size and number of chippings takes place in the laminate of the board material to be processed.
Due to the increasing customer demands, companies face the challenge of adapting their manufacturing facilities to produce batch size one parts. In this context, each component comes with specific customer requirements, significantly increasing the complexity of its manufacturing process. Traditional process planning done by employees is no longer feasible in the face of the growing complexity and diversity of batch size one parts. Therefore, there is a need for novel computer-aided process planning methods capable of autonomously learning and identifying the necessary machining features of these parts. We propose a configurable automated feature recognition framework tailored to batch size one parts to address this need. This framework comprises two main components. First, a customizable data generator is developed to create a dataset of computer-aided design models incorporating user-specific machining features. Second, a graph neural network is employed to learn these user-specific machining features by leveraging the graph structure represented by the vertices and edges in each computer-aided design model. We evaluate the effectiveness of our framework using a real-world example from the furniture industry. Specifically, we construct a dataset comprising over 7000 computer-aided design models of wooden boards using our framework's data generator. Each wooden board contains up to 30 randomly placed machining features for the application of connectors and fittings, randomly selected from 14 standard machining features commonly used in the furniture industry. An optimization algorithm adapts a graph neural network for classifying these specific machining features. The results demonstrate that our framework addresses the automated feature recognition problem without requiring data conversions, such as pixels or voxels, as other approaches often do. By introducing this configurable automated feature recognition framework, we solve the challenges posed by batch size one manufacturing. The framework's effectiveness and accuracy are demonstrated through extensive testing on the example of the furniture industry, showcasing its potential for enhancing manufacturing processes in various domains