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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
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
Modern production facilities are becoming increasingly complex, as companies battle to meet the increasingly customer-specific demands with shorter lead times and higher efficiency. For the employees in the factory, who often bear the brunt of making short-term process decisions, this complexity is becoming unmanageable. The PhD project presented in the following aims to decrease the complexity of modern manufactory facilities by means of artificial intelligence. The focus is on product data analysis and manufactory process planning. Different approaches of artificial intelligence for use in a Self-Adapting Smart System (SASS) are to be investigated.