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IEEE 20th international conference on Automation Science and engineering (CASE)

作者： Fang, Zonghang Yu, Chunlong Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China

ISBN: (纸本)9798350358513;9798350358520

In the era of Industry 4.0, with the availability of production data, there is an increasing demand to detect the throughput bottleneck in manufacturing systems using a datadriven approach. Traditional data-driven approaches identify the bottleneck indirectly by analyzing machine metrics collected from the shop floor, such as machine starvation and blockage. While efficient in most cases, these approaches often fail to identify the bottleneck according to its sensitivity-based definition, potentially resulting in incorrect results. To address this gap, we propose a novel data-driven approach named "Bottleneck Mining". This approach utilizes event logs as inputs to derive executable simulation models through process mining. Subsequently, it performs sensitivity analysis on the system throughput to accurately locate the bottleneck. The usefulness of the proposed approach has been demonstrated on several production lines.

关键词： data driven process mining manufacturing systems bottleneck identification

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Model-driven engineering of A knowledge-base for Assembly systems: An Architectural View 13

Model-driven Engineering of A Knowledge-base for Assembly Sy...

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13th Mediterranean conference on Embedded Computing (MECO)

作者： Khalilipour, Alireza Benito, Andres Decraemer, Bieke De Cock, Alexander Tezel, Baris Tekin Bozyigit, Fatma Challenger, Moharram Univ Antwerp Antwerp Belgium Flanders Make Lommel Belgium Daikin Europe Antwerp Belgium Flanders Make CodesignS Leuven Belgium Dokuz Eylul Univ Izmir Turkiye

ISBN: (纸本)9798350387568;9798350387575

The assembly process, a pivotal phase in manufacturing, involves the integration of individual components into a finished product. The limited flexibility of the product line often leads to repeated design production, a practice that imposes substantial costs on companies. Concurrently, a diverse array of data and knowledge must be conveyed within this environment to enable various tools to collaborate towards achieving the desired result. For instance, design data is serialized in a step file, while analysis tools present their findings or extract knowledge in a different format (e.g., graph). To curtail product line expenses and establish a user-friendly interface with all aspects of an assembly system, this paper introduces a knowledge base-a repository for storing raw data and extracted knowledge-to bridge design and analysis tools. The proposed knowledge base, developed using model-driven engineering techniques, tackles the primary challenge of mapping and transferring data among disparate sources. The effectiveness of the knowledge base is assessed using case studies from industrial partners in the AssistedDfA project.

关键词： Model-driven engineering knowledge-Base Assembly systems data Modeling CAD Tools data Exchange

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A forecast model for workers' performance in mass customised production

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international Journal of Industrial and systems engineering 2024年第1期48卷 125-152页

作者： Le, Song Thanh Quynh Ho, June Trinh, Thi Kim Hue VNU-HCM Ho Chi Minh City Viet Nam School of Engineering Macquarie University Sydney Australia

There has been a shift to mass customisation production for most of the manufacturing systems in recent decades. This leads to the need of predicting workers' performance for task assignments for all the main steps in manufacturing. However, the literature review shows that while most scholars pay more attention to the investigation of factors impacting worker performance;how these factors can be used to support decision making in task allocation is still in its infancy. This paper will propose a new method for determining the workers' performance by devising a rule-based system for the assembly manufacturing line. The application of previous experience, scientific knowledge, and historical data will increase the accuracy of the productivity and quality predictions. © 2024 Inderscience Enterprises Ltd.

关键词： Prediction models

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Design and Implementation of a Web-based Collaborative manufacturing System Based on knowledge Graph 4

Design and Implementation of a Web-based Collaborative Manuf...

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4th IEEE international conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Yuan, Meng Wu, Huaiyu Shen, Zhen Han, Yunjun Zhou, Jiehan Yue, Zhijian Xiong, Gang Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan523808 China Qingdao Academy of Intelligent Industries Intelligent Manufacturing Center Qingdao266109 China University of Oulu Faculty of Information Technology and Electrical Engineering OuluTS352 Finland

ISBN: (纸本)9798350349252

Collaborative manufacturing is central to agile, intelligent, and cloud-based manufacturing paradigms. This paper presents the design and implementation of a web-based collaborative manufacturing system based on a knowledge graph (KG). The system achieves the full spectrum of knowledge extraction, fusion, and reasoning across the industrial chain, thereby enhancing efficiency and reducing operational costs. The proposed system constructs an industrial chain KG and multi-agent systems, offering comprehensive digital services across the manufacturing value chain. It supports the integration of business processes, data, and standard information for enterprise groups, presenting collaborative manufacturing data of the industrial chain in real-time visualization. The paper further discusses a method for real-time perception of anomalies in the collaborative manufacturing process, utilizing expert knowledge and data-driven insights to construct an operational risk KG. The system's architecture, workflow, and functional design are detailed, along with application cases in visual dynamic management. The conclusion underscores the system's potential to improve supply-demand chain management and operational efficiency in collaborative manufacturing. © 2024 IEEE.

关键词： Smart manufacturing

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A Framework for Ontology-Based engineering systems: Advances and Open Questions About knowledge Capture and Use in Aerospace manufacturing 5th

A Framework for Ontology-Based Engineering Systems: Advances...

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5th international conference on Innovative Intelligent Industrial Production and Logistics

作者： Mas, Fernando Arista, Rebeca Skrzek, Murillo Oliva, Manuel Morales-Palma, Domingo Szejka, Anderson Luis Univ Seville Seville Spain Airbus SAS Toulouse France Pontif Catholic Univ Parana PUCPR Curitiba Parana Brazil Airbus Def & Space Seville Spain CT Engn Grp Madrid Spain

ISBN: (纸本)9783031807749;9783031807756

Modern Ontology-Based engineering (OBE) systems, built over the foundations of knowledge-Based engineering (KBE), leverage ontology-based technologies and the semantic web to capture and formalise knowledge holistically. This comprehensive knowledge repository can subsequently feed design and decision support tools with robust generative features. However, the integration and reuse of knowledge poses challenges for which it is necessary to develop appropriate methods and tools capable of handling diverse types of knowledge from both human experience and databases. The objective of this paper is to discuss the current limitations of OBE systems to address knowledge integration and reuse, based on the research and experiences of the authors in the aerospace manufacturing industry. This aims to provide a set of Research Questions (RQ) and the author's position to them, to serve as future research work and topics of discussion for the scientific community.

关键词： Ontology-Based engineering (OBE) knowledge-Based engineering (KBE) Models for manufacturing (MfM) Ontologies

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knowledge transfer. From Electronic Design Automation to Advanced Process manufacturing 67

Knowledge transfer. From Electronic Design Automation to Adv...

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67th IEEE international Midwest Symposium on Circuits and systems (MWSCAS)

作者： Torres, J. Andres Tao, Melody Doinychko, Anastasiia Govindaraj, Mohan Jayaram, Srividya Greeneltch, Nathan Siemens Digital Ind Wilsonville OR 97070 USA Siemens Digital Ind Fremont CA USA Siemens Digital Ind Grenoble France

ISBN: (纸本)9798350387186;9798350387179

On the path towards more complex systems that deliver more value at lower cost in semiconductor products, the industry had to develop frameworks to support the rapid design of very complex systems giving rise to widespread adoption of VLSI. A very similar situation exists for the advanced manufacturing process supporting the semiconductor industry. In order to keep the pace of innovation in manufacturing technology, the concept of a Digital Twin has been the main target of multiple studies and proposals. In this work we demonstrate that by learning from the lessons of VLSI, we aim to construct a digital twin of the manufacturing process that permits the monitoring and optimization of the most important aspects of semiconductor manufacturing, while at the same time demonstrating that such agnostic approach can also be applied to other manufacturing operations. A key component to the digital twin is the creation and deployment of reliable, experimentally verified machine learning models. In this work we will present necessary model characteristics to meet such requirements, under data conditions that are atypical for traditional machine learning training methods.

关键词： VLSI SOC Semiconductor manufacturing Machine Learning Digital Twin knowledge Transfer Feature engineering

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Investigation on domain adaptation of additive manufacturing monitoring systems to enhance digital twin reusability 20

Investigation on domain adaptation of additive manufacturing...

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IEEE 20th international conference on Automation Science and engineering (CASE)

作者： Xie, Jiarui Yang, Zhuo Hu, Chun-Chun Yang, Haw-Ching Lu, Yan Zhao, Yaoyao Fiona McGill Univ Montreal PQ Canada Georgetown Univ Washington DC USA Natl Cheng Kung Univ Tainan Taiwan Natl Kaohsiung Univ Sci & Technol Kaohsiung Taiwan NIST Gaithersburg MD 20899 USA

ISBN: (纸本)9798350358513;9798350358520

Powder bed fusion (PBF) is an emerging metal additive manufacturing (AM) technology that enables rapid fabrication of complex geometries. However, defects such as pores and balling may occur and lead to structural unconformities, thus compromising the mechanical performance of the part. This has become a critical challenge for quality assurance as the nature of some defects is stochastic during the process and invisible from the exterior. To address this issue, digital twin (DT) using machine learning (ML)-based modeling can be deployed for AM process monitoring and control. Melt pool is one of the most commonly observed physical phenomena for process monitoring, usually by high-speed cameras. Once labeled and preprocessed, the melt pool images are used to train ML-based models for DT applications such as process anomaly detection and print quality evaluation. Nonetheless, the reusability of DTs is restricted due to the wide variability of AM settings, including AM machines and monitoring instruments. The performance of the ML models trained using the dataset collected from one setting is usually compromised when applied to other settings. This paper proposes a knowledge transfer pipeline between different AM settings to enhance the reusability of AM DTs. The source and target datasets are collected from the National Institute of Standards and Technology and National Cheng Kung University with different cameras, materials, AM machines, and process parameters. The proposed pipeline consists of four steps: data preprocessing, data augmentation, domain alignment, and decision alignment. Compared with the model trained only using the source dataset, this pipeline increased the melt pool anomaly detection accuracy by 31% without any labeled training data from the target dataset.

关键词： Additive manufacturing machine learning digital twin knowledge transfer domain adaptation

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Construction of a knowledge graph for the full lifecycle data of customized furniture panel production 4

Construction of a knowledge graph for the full lifecycle dat...

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4th international conference on Mechanical engineering, Intelligent manufacturing, and Automation Technology, MEMAT 2023

作者： Xie, Yanyan Zhang, Cunji Liu, Bingsheng Qu, Hanxin Chen, Chuangeng Xia, Zengguang Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology Guilin University of Electronic Technology Guangxi Guilin541004 China College of Mechanical and Electrical Engineering Guilin University of Electronic Science and Technology Guangxi Guilin541004 China

ISBN: (纸本)9781510674820

In order to enhance the quality of customized furniture panel processing, avoid information silos between workstations, and promote intelligent manufacturing in customized furniture enterprises, this study constructs a knowledge graph focused on the full lifecycle data of customized furniture panel production. Firstly, the data from the production department of the enterprise is organized and preprocessed, including tokenization, annotation, and other preprocessing techniques, to establish the data layer of the knowledge graph. Secondly, based on real enterprise data, an ontology model is constructed to complete the pattern layer construction of the knowledge graph. Finally, the GPLinker model is utilized for high-precision knowledge extraction of entity relationships. The research results demonstrate that by constructing a knowledge graph focused on the full lifecycle data of customized furniture panel production, interconnectivity and interoperability of data throughout the entire lifecycle of a customized furniture enterprise can be achieved, providing a scientific basis for decision-making in addressing processing anomalies. © 2024 SPIE.

关键词： Extraction

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RESILIENT OPERATOR-ROBOT COLLABORATION IN SMART FLEXIBLE manufacturing systems

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international JOURNAL OF INDUSTRIAL engineering-THEORY APPLICATIONS AND PRACTICE 2024年第4期31卷 907-922页

作者： Masmali, Majed Northern Border Univ Coll Engn Ind Engn Dept Ar Ar Saudi Arabia

manufacturing key metrics are a useful approach for evaluating shop floor operations. The collaboration between operators and robots is essential in maintaining a resilient performance within smart and flexible manufacturing systems. For effective collaboration, both operators and robots must possess varying degrees of resilience, including full resilience, partial resilience and the ability to handle total disruptions. In this paper, lead time is considered a significant key metric. When the system is fully resilient and dependable, it achieves the optimal lead time. Consequently, lead time serves as a benchmark for evaluating the system's performance. However, if the robot experiences significant performance issues, it can negatively impact the cycle time, resulting in longer lead times. The discrepancy between the optimal lead time and the lead time obtained during partial or complete disruption is subtracted from the optimal lead time. To ensure the validity of the findings, mathematical equations are utilized in combination with other relevant data. This approach contributes to the knowledge base in the field. Finally, the paper will provide suggestions for future research endeavors..

关键词： Flexible manufacturing System Lead Time manufacturing Key Metrics manufacturing Resilience Operator-Robot Collaboration Smart manufacturing

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Compatibility Assessment for Interfaces in Drivetrains of Robot-Like systems 5

Compatibility Assessment for Interfaces in Drivetrains of Ro...

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5th international conference on Industry 4.0 and Smart manufacturing (ISM)

作者： Volpert, Marcus Vogel-Heuser, Birgit Hujo, Dominik Stahl, Karsten Zimmermann, Markus Tech Univ Munich TUM Sch Engn & Design Inst Automat & Informat Syst Garching Germany Tech Univ Munich TUM Sch Engn & Design Inst Machine Elements Garching Germany Tech Univ Munich Lab Product Dev & Lightweight Design TUM Sch Engn & Design Garching Germany

Due to the complexity of robot-like systems (RLS), new developments are often avoided, and most variants of the systems are designed. The primary determinant of an RLS is its drivetrain, which comprises purchased components, namely the controller, motor driver, motor, and gearbox. Each of these purchased parts has different interfaces that are not standardized. If the interfaces are sufficiently complex, the compatibility of the parts can no longer be guaranteed manually. Therefore, this paper presents a draft ontology that verifies the compatibility of purchased parts in RLS drivetrains. Classes and properties are obtained from expert knowledge, norms, data standards, and data sheets to build an ontology. The ontology design is evaluated using an application example for a motor-gearbox interface.

关键词： robot-like systems interface modeling ontology model-based systems engineering drivetrains

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