TY - INPR A1 - Böhm, Stefan-Andreas A1 - Zagar, Bare Luka A1 - Riß, Fabian A1 - Kortüm, Christian A1 - Knoll, Alois Christian T1 - Automated Feature Recognition in Surface Cad Models Based on Graph Neural Networks N2 - Driven by increasing customer demands, manufacturing processes encompass increasingly intricate workflows. The industry relies on computer-aided process planning software to effectively manage the intricacies of these complex manufacturing processes. This software is crucial to analyze computer-aided design data for a product and determining the required machining steps. However, a notable challenge arises, particularly in the case of custom products, where the machining steps can significantly vary depending on the available machinery and the employees' preferences.This study introduces a configurable automated feature recognition framework based on expert knowledge. Experts can encode their insights and expertise within this framework using a configurable synthetic data generator. Modern graph neural networks learn from the data generated by this data generator, achieving an average recognition accuracy up to 80% (F1-score) with a runtime performance of 21.98 milliseconds per model. Importantly, it accomplishes this even when confronted with highly intersecting machining features without requiring data conversion into alternative formats, such as voxel or pixel representations. KW - 3D deep learning KW - CAD KW - Graph neural networks KW - Machining feature recognition KW - Snthetic data generation KW - Surface models Y1 - 2024 U6 - https://doi.org/10.2139/ssrn.4772779 ER - TY - CHAP A1 - Kortüm, Christian A1 - Böhm, Stefan-Andreas ED - Schajer, Gary T1 - Holistic approach for geometric and technological similarity search in the production for profiled elements in the wood working industry for a more efficient production T2 - Proceedings of the 25th International Wood Machining Seminar N2 - In the whole field of woodworking, from carpentry to industry, new products are created and designed every day and manufacturers wonder if similar workpieces have already been produced in the company and if corresponding drawings, information, tools, jigs, etc. can be reused. Under certain circumstances, considerable duplication of work can be avoided and a more efficient production is possible. In order to find similar workpieces, it must be clarified what exactly is meant by similarity. The different fields of knowledge do not provide a clear definition. However, similarities are always used to compare objects. In woodworking, objects can be a wide variety of components and products, such as carcase sides, furniture fronts, fittings, tools or profile strips. The search for similar profiled elements – related to the potential, data structure and algorithms – is the focus of this paper. There is a wide range of different profiled elements, which are used constructively as well as decoratively, e.g. for baseboards, door frames, window frames or decorative moulding profiles. These profiles are manufactured in a multi-stage production process in which the workpieces are first machined with profiled tools before surface finishing takes place. Along this production chain, tools, machine settings and, in some cases, fixtures are used that are adapted to the respective profile and can be reused for the same or similar jobs. Inheritance of information offers the greatest potential of similarity search. Inheritance means that existing knowledge in the form of drawings, CNC programs, process parameters and setup specifications, etc., is transferred from existing profiles to a new profile. By inheriting existing information, for example, the effort required for process setting can be reduced by adopting spindle positions, positions of guides and stops, or feed and cutting speeds. However, this can only be done if the process knowledge and other technological information concerning a profile are sufficiently documented. This is the only way how all information is made available for the similarity search and inheritance, and also for setting the processes when the profile is manufactured once again. The profile data must therefore be stored in a meaningful structure without loss of information. Therefore, a holistic product data model was developed that combines the geometrical with the technological profile data. The developed model allows a combined search for geometrically and e.g. materially similar products – also with a weighted evaluation of the individual criteria. The research has shown that data modelling is the essential foundation for the implementation of modern AI algorithms to enable similarity search. In conclusion, the use of a search system for geometrically and technologically similar profiles represents a real innovation for wood processing companies. This paper presents a holistic approach for geometrical and technological similarity search presenting different algorithms KW - similarity search KW - profiled elements KW - product data model KW - multi-stage production process Y1 - 2023 ER - TY - CHAP A1 - Kortüm, Christian A1 - Riegel, Adrian T1 - Geometric Dimensioning and Tolerancing of Furniture Components T2 - Proceedings of the 24th International Wood Machining Seminar N2 - The machining of furniture components requires a complete geometrical specification of dimensions with associated tolerances to define in the first step appropriate manufacturing processes and to carry out the design and setup. Furthermore, a verification of the defined specification after production is a next step to check the quality and related to the production equipment, to calculate process capability and effectiveness. In 2011, the system of Geometrical Product Specification (GPS) has been published completely. The system is built for a distinct range of geometrical quality characteristics and consists of chains of standards referring to each other. With three elements of these chains, the product can be specified. With three additional elements, the verification of the product and the measuring system is possible. It does not contain the verification of the production processes, the process qualification. The German standard DIN 68100 provides a frame for dimensioning and tolerancing lengths and also angles, parallelism and straightness for furniture components. It includes a method to consider swelling and shrinking of wood and wood-based materials. Apart from its benefit of specifying products in a supply chain, it is rarely used in the branch nowadays. DIN 68100 is currently far away from GPS. To gain all benefits arising from GPS the branch needs a consolidated action. New symbols for drafts and an updated method for tolerancing with moisture consideration have to be developed as well as measuring dimensions of flexible work pieces, like most parts in the furniture industry. In GPS, the link between product verification and process specification is not that consistent. In other branches SPC and methods of process qualifications are established. At the moment the kitchen industry is establishing SPC or similar procedures. An action towards GPS can also accompany these efforts. Due to a shift in the tolerance principles, the GPS system is hardly directly applicable to the furniture industry, e.g. DIN 68100. But there is a real threat that the system is called up accidentally and out of ignorance. This paper sketches a comprehensive system of product specification, verification for furniture components and process qualification. It will share experience gained in the kitchen furniture industry and will add a theoretical analysis of a possible application of GPS-chains for important examples of quality characteristics. The activities are part of a process (VDI 3415-2) carried out in the working group 102 of the Society of German Engineers (VDI). KW - Geometrical Product Specification (GPS) KW - Tolerances KW - Specification and Verification KW - Process Qualification, Furniture Components Y1 - 2019 ER - TY - CHAP A1 - Böhm, Stefan-Andreas A1 - Kortüm, Christian ED - Schajer, Gary T1 - Machining Feature Recognition in Furniture Parts based on a Graph Neural Network T2 - Proceedings of the 25th International Wood Machining Seminar N2 - 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 KW - Furniture Industry KW - Automatic Feature Recognition, KW - Machining Features KW - Machine Learning, Graph Neural Networks Y1 - 2023 ER - TY - CHAP A1 - Binninger, Karsten A1 - Kortüm, Christian A1 - Scholz, Frieder ED - Schajer, Gary T1 - Investigation of tool wear of the trimming unit and resulting quality in the edgebanding process T2 - Proceedings of the 25th International Wood Machining Seminar N2 - 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. KW - edgebanding process KW - tool wear KW - trimming KW - product quality Y1 - 2023 ER -