Burggräf, Peter
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Publication Access status: Open Access , A Conceptual Process Architecture for the Digital Integration of Tube Fabrication in Mechanical and Plant EngineeringIn mechanical and plant engineering practice, tube fabrication is still frequently characterized by assembly-driven and experience-based procedures. Geometric specifications are often finalized during assembly based on measurements taken on the physical product, resulting in iterative adjustments, limited process standardization, and a strong dependence on tacit expert knowledge. Although advanced computer-aided design (CAD) and computer-aided manufacturing (CAM) technologies and automated tube bending systems are available, a structured integration of tube fabrication into a consistent digital process architecture is often lacking. This paper develops a conceptual process architecture that structures the digital integration of tube fabrication in project-driven mechanical and plant engineering environments. Based on an analysis of typical industrial process structures, the proposed architecture relocates geometric specification, manufacturability validation, and product–process coordination to early digital stages. Defined transition mechanisms between engineering, manufacturing, and assembly domains are introduced to prevent installation from functioning as a geometry-defining validation environment. The scientific contribution lies in the domain-specific operationalization of integration principles for tube fabrication through a governed process architecture that relocates geometric finalization to engineering, formalizes transitions between engineering and manufacturing, and institutionalizes manufacturing knowledge within digital design processes. The proposed process architecture comprises model-based tube system specification, manufacturing-oriented design validation, deterministic derivation and controlled release of fabrication data, digitally driven prefabrication, and structured geometric verification prior to assembly. In addition, technical and organizational boundary conditions, including data continuity, interface governance, and knowledge integration, are systematically addressed. The resulting framework provides a conceptual basis for reducing assembly-driven iteration, increasing reproducibility, and supporting the formalization of manufacturing knowledge in mechanical and plant engineering.Source Type:InProceedings2 10 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , Bibliometric study on the use of machine learning as resolution technique for Facility Layout ProblemsFacility Layout Problems (FLP) are concerned with finding efficient factory layouts. Numerous resolution approaches are known in literature for layout optimization. Among those, intelligent approaches are less researched than solutions from exact or approximating approaches. The recent surge of research interest in Artificial Intelligence, and specifically Machine Learning (ML) techniques, presages an increase of such techniques' usage in FLP. However, previous reviews on FLP research induce that, to date, this trend has not yet emerged. Utilizing a systematic literature review coupled with a k-Means based clustering algorithm, we analyzed 25 relevant publication full-texts from an original sample of 1,425 papers. Our findings corroborate the statement that ML techniques have attracted substantially less research interest than most other resolution approaches. While a few papers used Unsupervised Learning algorithms directly as a solution to the FLP, Supervised and Reinforcement Learning were found to be practically irrelevant. ML usage was significantly higher in FLP-adjacent planning tasks such as group technology. Drawing from experiences with other NP-hard combinatorial optimization problems in manufacturing research, we conclude that Reinforcement Learning is most promising to bridge the evident gap between FLP and ML research. Our study further contributes to FLP research by extending established classification frameworks.Source Type:Article407 290 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , Datengestützte Umformtechnik: Künstliche Intelligenz zwischen Prozesswissen, Regelung und ProduktionsrealitätWith the 7th Biegeforum, the Chair of Forming Technology at the University of Siegen once again offers researchers the opportunity to present their topics and findings to a specialist audience from industry and academia. This conference volume brings together current research findings and innovative developments in forming technology, highlighting the broad scope of the research field. The Biegeforum thus underscores its role as a key platform for exchange between research and industry — a role it has fulfilled since its first edition in 2011.Source Type:Conference ProceedingsSeries:Forschungsberichte des Lehrstuhls für Umformtechnik3 10 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , Machine learning-based prediction of missing components for assembly - a case study at an engineer-to-order manufacturerFor manufacturing companies, especially for machine and plant manufacturers, the assembly of products in time has an essential impact on meeting delivery dates. Often missing individual components lead to a delayed assembly start, hereinafter referred to as assembly start delayers. Identifying the assembly start delayers early in the production process can help to set countermeasures to meet the required delivery dates. In order to achieve this, we set up 24 prediction models on four different levels of detail utilizing different machine learning-algorithms - six prediction models on every level of detail - and applying a case-based research approach in order to identify the model with the highest model quality. The modeling approach on the four levels of detail is different. The models on the coarsest level of detail predict assembly start delayers utilizing a classification approach. The models on the three finer levels of detail predict assembly start delayers via a regression of different lead times and subsequent postprocessing operations to identify the assembly start delayers. After training of the 24 prediction models based on a real data set of a machine and plant manufacturer and evaluating their model quality, the classification model utilizing a Gradient Boosting classifier showed best results. Thus, performing a binary classification to identify assembly start delayers was the best modelling approach. With the achieved results, our study is a first approach to predict assembly start delayers and gives insights in the performance of different modeling approaches in the area of a production planning and control.Source Type:Article2 9
