Procedia CIRP最新文献

{"title":"Investigating the reparability of fusion bonded metal-plastic composites for improved circularity","authors":"Christian Gundlach ,&nbsp;Klaus Dilger ,&nbsp;Sven Hartwig","doi":"10.1016/j.procir.2024.09.018","DOIUrl":"10.1016/j.procir.2024.09.018","url":null,"abstract":"<div><div>Multi-material structures have long been established for various components in the automotive industry in the context of lightweight construction, with metal and fiber-reinforced plastic in particular being advantageously joined together in the form of so-called hybrid designs. The joining technology plays a decisive role here with regard to the hybrid component's performance and aging resistance. However, the complexity of component manufacture and the feasibility of subsequent repair, reprocessing and recycling concepts are largely determined by the choice of joining technology. The aim of the present work is to investigate the reparability of joints produced fusion bonding on a fundamental level. Lap shear specimens consisting of a laser-structured, metallic joining partner and a fiber-reinforced thermoplastic (FRTP) sheet are used as a basis for evaluation. The test sequence comprises an initial joining and three consecutive repair processes of the joint by means of fusion bonding, with a destructive lap shear test taking place after each joining process. Two repair approaches (with and without an additional polyamide-6 film) are investigated. Over three repair processes, a slightly decreasing trend in lap shear strength is found if the repair is carried out without additional polyamide-6 film. Using a polyamide-6 film during the repair leads to higher strength in every case tested. Thermal analysis and microindentation on the FRTP substrate takes place in order to quantify the influence of the heating required for repair. A negative effect on the degree of crystallinity and the strength of the polyamide-6 matrix close to the steel-FRTP interface is found. Nevertheless, based on the criteria derived from DIN EN 45554, the conclusion is drawn that the reparability of metal-FRTP joints produced by fusion bonding is possible on a sample scale. For future work, a more in-depth study of the joining surfaces, especially the metal part, as well as the inclusion of an aging step before each lap shear test should be considered.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"131 ","pages":"Pages 113-118"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A data augmentation algorithm for surface inspection in point cloud data","authors":"Daan Büchner ,&nbsp;Ole Schmedemann ,&nbsp;Thorsten Schüppstuhl","doi":"10.1016/j.procir.2025.02.142","DOIUrl":"10.1016/j.procir.2025.02.142","url":null,"abstract":"<div><div>Due to the high standards in aircraft maintenance, high resolution sensors, such as white light interferometers, are needed. Those sensors scan surfaces in nanometer scale and generate point clouds. This data can be used to detect surface defects. Such anomalies should be identified during the inspection process to assess the current condition of the workpiece. Deep learning algorithms can be used to evaluate the data. However, in the domain of 3D data, the challenge of obtaining training data is amplified due to the time-consuming labeling process. Therefore, this work introduces an algorithm that combines surface features, like cracks, into surface data to generate new labeled training data. The resulting dataset is then used to train a deep learning algorithm to segment the cracks in the point cloud data. The results indicate that the augmented data enhances the training process.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"134 ","pages":"Pages 437-442"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Assembly Instruction Generation for Cognitive Assistance Systems with Large Language Models","authors":"Benedikt Kelm ,&nbsp;Paul Hubert Haas ,&nbsp;Simon Jochum ,&nbsp;Lennard Margies ,&nbsp;Rainer Müller","doi":"10.1016/j.procir.2025.03.010","DOIUrl":"10.1016/j.procir.2025.03.010","url":null,"abstract":"<div><div>Cognitive Assistance Systems enhance manual assembly by shortening learning cycles and allowing workers to handle a wider range of products. However, generating assembly instructions remains time-consuming, particularly in environments with high product variability. This paper presents a novel approach to automate and streamline this process using MTM-based standardized instruction texts and Large Language Models via the OpenAI API. By deriving instructions from MTM analyses, a unified syntax and structure can be realized, improving consistency and efficiency. The integration of GTP-4o further enables the automatic generation of context-specific warnings and error notifications. Model fine-tuning and prompt engineering play a pivotal role in this approach, allowing the generation of precise instructions. The evaluation, based on the BLEU and METEOR scores, focuses on the assessment of the technical functionality and the quality of the generated outputs and shows promising results that highlight the potential of this approach for improving the automated generation of standardized assembly instructions for Cognitive Assistance Systems.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"134 ","pages":"Pages 7-12"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144290741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Quantum Computing Applications in the Study and Operation of Manufacturing Systems","authors":"Oliver Schiffmann ,&nbsp;James Gopsill ,&nbsp;Max Ridley ,&nbsp;Maria Valero","doi":"10.1016/j.procir.2025.02.161","DOIUrl":"10.1016/j.procir.2025.02.161","url":null,"abstract":"<div><div>This paper explores the potential application areas for Quantum Computing in solving numerical problems found in the design and operation of Manufacturing Systems. The paper identifes manufacturing system problems requiring substantial classical computational resource and ofers a quantum alternative. The paper then discusses the quantum hardware that the alternatives could be implemented on commenting on the suitability of today’s Noisy Intermediate Scale Quantum hardware options as well as developments likely to occur in the near-term. The most promising area identified was job scheduling where quantum alternatives exist that could be implemented on today’s Quantum Computing hardware. This is followed by facility layout and vehicle scheduling although further hardware/algorithm development is required. A quantum alternative for fault detection was also discussed however it is too early to be sure of the potential advantages.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"134 ","pages":"Pages 573-578"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144290742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-driven multi-objective alloy selection framework for mechanical property criteria","authors":"Erkan Tur ,&nbsp;Joseph Betts ,&nbsp;Laurent Perge ,&nbsp;Alborz Shokrani","doi":"10.1016/j.procir.2025.03.056","DOIUrl":"10.1016/j.procir.2025.03.056","url":null,"abstract":"<div><div>This study presents a new machine learning framework for multi-objective alloy selection, focusing on key mechanical properties such as tensile strength, elongation, hardness, and Charpy energy. Traditional tools are often limited in their ability to manage large datasets and the complex, non-linear relationships between alloy composition, process parameters, and mechanical properties. In contrast, machine learning models such as XGBoost, Fine-Tuned Stacking, and Ensemble methods provide a scalable solution, allowing for the simultaneous consideration of multiple mechanical property objectives. The models were trained on a comprehensive dataset of stainless steel alloys, filtering materials that meet predefined performance criteria. Among the models, the Ensemble approach achieved the best results, with a precision of 0.98 and recall of 0.93. The findings show that integrating machine learning into the alloy selection process has the potential to improve decision-making accuracy for practical engineering applications.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"134 ","pages":"Pages 61-66"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144290787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Human-Robot Collaboration in Industrial Assembly: Augmented Reality-Supported Dynamic Task Allocation with Intuitive Process Planning","authors":"Christoph Petzoldt ,&nbsp;Dario Niermann ,&nbsp;Dennis Keiser ,&nbsp;Michael Freitag","doi":"10.1016/j.procir.2025.02.173","DOIUrl":"10.1016/j.procir.2025.02.173","url":null,"abstract":"<div><div>As industrial assembly faces increasing demands for cost-efficiency and flexibility, human-robot collaboration (HRC) emerges as a promising solution for small to medium production volumes. To address practical challenges in HRC implementation – such as effective task distribution, trust, and workers’ information needs – this paper introduces a system that uses augmented reality (AR) to enhance adaptive HRC. The system intuitively provides essential information to workers and dynamically adjusts task allocation based on individual performance. It consists of five core components: an AR-integrated situation recognition system, situation-aware robot path planning, dynamic task allocation, AR visualization of work information, and a no-code software interface with a digital twin for process creation and monitoring. The paper offers a comprehensive overview of the system architecture, the user interfaces for process creation and AR visualization, and details a novel approach for optimization-based dynamic task allocation. Improvements in efficiency and collaboration are demonstrated through a practical assembly scenario at a laboratory workstation for collaborative assembly.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"134 ","pages":"Pages 585-590"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144290807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards Pattern Modeling for Asynchronicity in Production Data","authors":"Arno Schmetz ,&nbsp;Achim Kampker","doi":"10.1016/j.procir.2025.03.023","DOIUrl":"10.1016/j.procir.2025.03.023","url":null,"abstract":"<div><div>Modern Production and Next Generation Manufacturing Systems rely heavily on data from production and production environments. This leads to critical dependency on the quality of said data, where lacks in quality result in limited performance, reduced resilience, and applicability of data-driven models. In complex production setups, multiple sources of data must be aggregated and synchronized accurately to enable correct assignment of sensors to the same location and time during production. The Time Synchronization Problem in manufacturing describes the problem of asynchronous data streams based on the technical limitations of technical clocks. In this paper, we present modeling approaches to the asynchronicity of production data streams in short- and long-term data acquisition. Based on experiments with production machines, we propose a set of typical asynchronicity patterns, which can be used to model the asynchronicity in offline synchronization methods to improve quality of the production data quality for manufacturing systems and models.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"134 ","pages":"Pages 37-42"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144290882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating the environmental footprint methodology for aluminium products with post-consumer scrap","authors":"Chibuikem Nwagwu ,&nbsp;Jon Halfdanarson ,&nbsp;Christina Meskers","doi":"10.1016/j.procir.2025.02.168","DOIUrl":"10.1016/j.procir.2025.02.168","url":null,"abstract":"<div><div>The Norwegian aluminium value chain aims to increase the use of post-consumer scrap (PCS) in product manufacturing to lower the product environmental footprints. Discussions with product manufacturers, shredding &amp; sorting companies, and remelters were held to investigate the challenges and current implementation of the environmental footprint methods as these are an integral part of companies’ disclosure efforts on PCS use. The company’s position in the value chain impacts their interpretations of aluminium flow nomenclature and the associated environmental credits, and thus influences methodological choices. Furthermore, a method that better reflects the industrial realities and balances the different circularity goals is needed. The net-scrap approach (e.g. EN15804) was identified as it incentivises both recycled material usage and PCS recycling. Also, the end-of-waste concept, quality aspects, and temporal dimensions of using PCS were identified as challenges. This paper dives deeper into these topics and provides a direction for the aluminium manufacturing industry.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"134 ","pages":"Pages 479-484"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144290899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Co-innovation tools, mechanisms and methods – status quo and potentials","authors":"Arian Lamann ,&nbsp;Herwig Winkler ,&nbsp;Hans-Rüdiger Lange ,&nbsp;Nizar Abdelkafi","doi":"10.1016/j.procir.2025.02.165","DOIUrl":"10.1016/j.procir.2025.02.165","url":null,"abstract":"<div><div>Co-innovation and open innovation are approaches to innovation management that are designed to stimulate innovation in organizations. This study analyzes technical offline and online support tools that can aid co-innovation projects by identifying appropriate functionalities. A systematic literature review and analysis of co-innovation tools reveals a lack of support, particularly in the areas of (i) intellectual property management, (ii) compliance and legal management, (iii) planning and analysis management, and (iv) performance and monitoring management. Physical toolkits show advanced development in meeting the criteria, while software solutions lag behind. The findings highlight the need for comprehensive innovation support tools.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"134 ","pages":"Pages 514-519"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144290905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Objective validation for short-term material flow simulation","authors":"Heiner Reinhardt ,&nbsp;Boran Bogdanow ,&nbsp;Roman Arnold ,&nbsp;Bastian Prell ,&nbsp;Philipp Klimant ,&nbsp;Frank Seifert ,&nbsp;Mahtab Mahdaviasl ,&nbsp;Steffen Ihlenfeldt","doi":"10.1016/j.procir.2025.02.170","DOIUrl":"10.1016/j.procir.2025.02.170","url":null,"abstract":"<div><div>Material flow simulation, by means of discrete event simulation (DES), is utilized in various industries to support production planning and control. This is due to the fact that changes to manufacturing systems and production strategies can be virtually examined before actual implementation. Since the manual development and repeated adjustment of simulation models is time-consuming and error-prone, numerous studies suggest computerized model generation and synchronization. However, validation of such generated or synchronized models is rarely considered. Here, we present an extendible objective validation technique for the repeated short-term simulation of a generated model. To illustrate this, the technique is applied to a high-volume automotive production line and a series of simulation cycles.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"134 ","pages":"Pages 526-531"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144290907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}