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20th IFIP WG 5.1 international conference on Product Lifecycle Management, PLM 2023

ISBN: (纸本)9783031625770

The proceedings contain 61 papers. The special focus in this conference is on Product Lifecycle Management. The topics include: A data Structure for Developing data-Driven Digital Twins;a Quality-Oriented Decision Support Framework: Cyber-Physical systems and Model-Based Design to Develop Design for Additive manufacturing Features;examining the Influence of Business Models on Technical Implementation of Smart Services;navigating the Digital Twin: 3D Exploration for Asset Administration Shell Content;industrial Layout Mapping by Human-Centered Approach and Computer Vision;a data Management Approach for Modular Industrial Augmented Reality Applications;a Method to Interactive Simulations of Industrial Environments Based on Immersive Technologies;how to Support knowledge Exchange in a Multi-division manufacturing Firm Using a Prototype Platform?;Contextualization for Generating FAIR data: A Dynamic Model for Documenting Research Activities;gamification as a knowledge Management Tool;drivers of Change Impacting Outcome-Based Business Models in Industrial Production Equipment;exploration of Multi-layers Networks to Elicit and Capture Product Changes;a Model to Predict Span Time and Effort for Product Development Processes;a Multicriteria Framework Proposition for Project Management Approaches;innovative Development in a University Environment Based on the Triple Helix Concepts: A Systematic Literature Review;understanding Service Design in the Context of Servitization;SE Based Development Framework for Changeable Maritime systems;Interface Modeling for Complex systems Design: An MBSE and PLM System Integration Perspective;an Implementation of Integrated Approach in Product Life-Cycle Management Tool to Ensure Requirements-In-Loop During Complex Product Development: A Cubesat Case Study;holistic Perspective to the Drug-Device Combination Product Development Challenges;knowledge-Based engineering Design Supported by a Digital Twin Platform.

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Automatic generation of 2D surface topography specifications based on wed ontology language 5

Automatic generation of 2D surface topography specifications...

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5th international conference on Mechanical engineering, Intelligent manufacturing, and Mechatronics, MEIMM 2024

作者： Tang, Rongjiang You, Zeyu Wei, Yanhong Li, Jian Liang, Xiu School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin541004 China

ISBN: (数字)9781510686786

ISBN: (纸本)9781510686779

An ontology-based automatic two-dimensional surface topography specification (TSTS) generation method was proposed to solve the problem of different designers' different TSTS generation results and semantic interoperability during the process. According to the existing category model representing two-dimensional (2D) surface topography knowledge and the EXPRESS-language model representing Computer Aided Design (CAD) system extraction information, a knowledge representation model is constructed consisting of part surface layer, extraction information layer, TSTS information layer, and part information mapping layer. The part surface layer, extraction information layer, and 2D surface morphology feature information layer are constructed as meta-ontology using Web Ontology Language to reuse or inherit 2D surface morphology knowledge in specific fields. The part information mapping layer is represented by Semantic Web Rule Language (SWRL). Then, the meta-ontology and knowledge based on the SWRL representation are mapped to Java expert systems shell (JESS) facts and JESS rules, and the JESS inference machine is used to generate the TSTS. © 2024 SPIE.

关键词： knowledge representation

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A knowledge Push Method to Support the Debugging Process Design of Complex Electronic Products 24

A Knowledge Push Method to Support the Debugging Process Des...

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8th international conference on Electronic Information Technology and Computer engineering, EITCE 2024

作者： Xue, Hao Chen, Xingyu Luo, Tiannan Li, Yang Liu, Yuhan Zhang, Kai School of Mechanical Engineering Sichuan University Sichuan Chengdu China Southwest China Research Institute of Electronic Equipment Sichuan Chengdu China

ISBN: (纸本)9798400710094

The core of design lies in the acquisition and application of knowledge. knowledge push technology can effectively improve the utilization efficiency of knowledge by designers, thus enabling more efficient task completion. To address the current challenges in debugging process design, including insufficient knowledge management and the dynamic demand for knowledge, this paper proposes a knowledge push method tailored for the debugging process design of complex electronic products. By constructing a knowledge graph for debugging processes and utilizing a semantic similarity-based push algorithm, the method intelligently identifies knowledge needs during the design process, achieving precise knowledge delivery. On this basis, a prototype system supporting knowledge push for complex electronic product debugging design was developed. Taking the debugging process design of an elevator drive system as an example, the method has been proven to effectively improve design efficiency. © 2024 Copyright held by the owner/author(s).

关键词： knowledge graph

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AI in Smart manufacturing: An Ethical and Legal Perspective 4

AI in Smart Manufacturing: An Ethical and Legal Perspective

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4th international Multidisciplinary Information Technology and engineering conference, IMITEC 2024

作者： Onu, Peter Pradhan, Anup Madonsela, Nelson Sizwe Bowen University Mechatronics Engineering Programme Iwo Nigeria University of Johannesburg Quality and Operaions Managment Johannesburg South Africa

ISBN: (纸本)9798350387988

The rapid advancement and deployment of Artificial Intelligence (AI) technologies in the manufacturing sector and the integration of the Internet of Things (IoT) and big data analytics are transforming traditional manufacturing processes. This is significant for Smart manufacturing and poses immense potential for creating highly efficient, flexible, and interconnected production systems. However, these technological advancements come with substantial ethical and legal considerations that necessitate careful examination and proactive management. Using a qualitative research approach, this paper analyzes the ethical implications of AI implementation in manufacturing, exploring key challenges and opportunities for promoting responsible AI development. The paper presents a comprehensive review of emerging engineering solutions to address ethical concerns and protect intellectual property in the context of AI-driven Smart manufacturing. It discusses the potential of blockchain technology in ensuring data integrity and transparency, federated learning approaches for collaborative model training while preserving data privacy, and AI transparency mechanisms to enhance trust and accountability in AI systems. Based on previous cases, industry methods, and scholarly discussions, this study provides practical suggestions for dealing with the ethical and intellectual property challenges of integrating AI into manufacturing processes. © 2024 IEEE.

关键词： Smart manufacturing

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data-Driven Discovery of manufacturing Processes and Performance from Worker Localisation 32nd

Data-Driven Discovery of Manufacturing Processes and Perform...

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32nd international conference on Flexible Automation and Intelligent manufacturing

作者： Aslan, Ayse El-Raoui, Hanane Hanson, Jack Vasantha, Gokula Quigley, John Corney, Jonathan Edinburgh Napier Univ Edinburgh EH10 5DT Midlothian Scotland Univ Strathclyde Glasgow G1 1XQ Lanark Scotland Univ Edinburgh Edinburgh EH8 9YL Midlothian Scotland

ISBN: (纸本)9783031382406;9783031382413

In complex manufacturing industries that are not fully automated and involve human workers it is important to identify deviations from the planned production schedule and locate bottlenecks for improved efficiency. This is not an easy task as it requires data on how workers are actually performing the manufacturing activities. Ultra-wideband (UWB) tags, which are sensors that track movement, can be used to collect this data. Previous research has mostly focused on using these sensors to detect faults and anomalies and to ensure worker safety. However, this paper presents a method for using UWB data to discover process models of manufacturing activities using process mining techniques. We applied our method to a real assembly line with UWB data and found deviations from the prescribed process steps and bottlenecks in the assembly line, which indicated that the first assembly step can take twice as much time compared to other steps.

关键词： manufacturing process optimisation industrial productivity process mining indoor positioning systems

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Exploring Historical data Utilization in Skill-Based Production systems 6

Exploring Historical Data Utilization in Skill-Based Product...

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6th international conference on Industry 4.0 and Smart manufacturing, ISM 2024

作者： Kremser, Patrick Legler, Tatjana Ruskowski, Martin Kaiserslautern67663 Germany Kaiserslautern67663 Germany

This paper investigates the improvement of skill-based production systems through the use of historical performance data and emerging data-driven techniques. It provides an in-depth examination of case-based reasoning, knowledge graphs and simulation environments, evaluating their effectiveness in analyzing past activities and providing insights for future production tasks. The study explains how case-based reasoning draws on past examples for decision-making, knowledge graphs reveal data relationships, and simulation environments enable risk-free testing of different scenarios. Through a comparative analysis, the paper explores how these methods can be integrated into skills-based production to improve system efficiency and initiate steps towards greater autonomy. It outlines the strengths, limitations and future research opportunities, highlighting the importance of adopting these technologies to improve operational efficiency and adaptability in manufacturing. The aim of this paper is to demonstrate how historical data can enhance skill development and decision making in production systems. © 2024 The Authors. Published by Elsevier B.V.

关键词： knowledge graph

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Health status assessment of warship power equipment 5

Health status assessment of warship power equipment

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5th international conference on Mechanical engineering, Intelligent manufacturing, and Mechatronics, MEIMM 2024

作者： Chao, Liu Li, Zhang Dongliang, Yin Graduate School Naval University of Engineering Wuhan430033 China No. 266071 Unit of PLA Qingdao266000 China Department of Operations Research and Planning Naval University of Engineering Wuhan430033 China

ISBN: (数字)9781510686786

ISBN: (纸本)9781510686779

Aiming at the problems of complex data and difficult health status assessment of ship power plant, this paper summarizes the research status at home and abroad, and analyzes the current equipment health status assessment methods, including physical model-based methods, knowledge-based methods and data-driven methods. Through the research, it is found that in the process of equipment health status assessment, three steps can be followed. First, the classification of dynamic system conditions based on unsupervised learning algorithm;The second is the regression analysis of the operating state based on the operating data of the dynamic system. Third, the research of power system health assessment based on differential analysis modeling can provide theoretical support for equipment health state assessment. © 2024 SPIE.

关键词： Warships

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Human in the data-Driven Zero Defect manufacturing Loop: Case Examples from manufacturing Companies 32nd

Human in the Data-Driven Zero Defect Manufacturing Loop: Cas...

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32nd international conference on Flexible Automation and Intelligent manufacturing

作者： Arica, Emrah Oliveira, Manuel Pedersen, Torbjorn Mannhardt, Felix Myklebust, Odd SINTEF Mfg Trondheim Norway KIT AR London England IDLETECHS Trondheim Norway

ISBN: (纸本)9783031381645;9783031381652

data-driven Zero Defect manufacturing (ZDM) system gathers and organizes data from different sources, integrating and analyzing the data using different tools, with the purpose to react on potential quality issues before they happen with adequate levels of data accuracy and precision. This paper discusses the role of humans in the data driven ZDM loop, considering the context of four manufacturing companies, from the EU H2020 project DAT4Zero which has also funded this study. These companies represent distinct manufacturing environments, each with specific industrial challenges and requirements, which were studied to map, analyze, and exemplify the potential role of humans in the data driven ZDM framework in real manufacturing environments.

关键词： Zero defect manufacturing Human in the loop Quality control

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Optimal scheduling of glass substrate evaporation based on improved genetic algorithm 3

Optimal scheduling of glass substrate evaporation based on i...

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3rd international conference on Advanced manufacturing Technology and manufacturing systems, ICAMTMS 2024

作者： Ren, Zihao Zhang, Feng School of Mechanical and Electrical Engineering Wuhan University of Technology Wuhan China

ISBN: (纸本)9781510681804

Aiming at the problems of long production cycle and low equipment utilization rate in glass substrate evaporation process, the batch scheduling model of glass substrate evaporation process was established by analyzing the process characteristics such as manufacturing resource constraints and batch rules, with the maximum completion time as the optimization objective, and an improved genetic algorithm was designed to solve it. In the initialization phase of the algorithm, a heuristic strategy based on grouping is designed to improve the quality of the initial population;In the process of population iteration, adaptive crossover and mutation operations are used to obtain more excellent individuals. Finally, the effectiveness of the proposed model and the superiority of the algorithm are verified by a case study on the actual production data of a screen manufacturing enterprise. © 2024 SPIE.

关键词： Glass substrates

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Bibliometric and Content Analysis of Large Language Models Research in Software engineering: The Potential and Limitation in Software engineering

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international Journal of Advanced Computer Science and Applications 2025年第4期16卷 344-356页

作者： Damayanti, Annisa Dwi Gani, Hamdan Zhipeng, Feng Gani, Helmy Zuhriyah, Sitti Nurani Djabir, St. Nurhayati Wardani, Nur Ilmiyanti Department of Environmental Engineering Faculty of Engineering Hasanuddin University Makassar Indonesia Department of Machinery Automation System ATI Makassar Polytechnic Makassar Indonesia School of Culture Creativity and Media Hangzhou Normal University Zhejiang Hangzhou China Department of Industrial Hygiene Faculty of Public Health Occupational Health and Safety Makassar College of Health Sciences Indonesia Department of Computer System Universitas Handayani Makassar Makassar Indonesia Department of Information Systems and Technology Institut Teknologi dan Bisnis Nobel Indonesia Jl. Sultan Alauddin No.212 Makassar90221 Indonesia Department of Informatics Engineering Universitas Handayani Makassar Makassar Indonesia

Large Language Models (LLM) is a type of artificial neural network that excels at language-related tasks. The advantages and disadvantages of using LLM in software engineering are still being debated, but it is a tool that can be utilized in software engineering. This study aimed to analyze LLM studies in software engineering using bibliometric and content analysis. The study data were retrieved from Web of Science and Scopus. The data were analyzed using two popular bibliometric approaches: bibliometric and content analysis. VOS Viewer and Bibliometrix software were used to conduct the bibliometric analysis. The bibliometric analysis was performed using science mapping and performance analysis approaches. Various bibliometric data, including the most frequently referenced publications, journals, and nations, were evaluated and presented. Then, the synthetic knowledge method was utilized for content analysis. This study examined 235 papers, with 836 authors contributing. The publications were published in 123 different journals. The average number of citations per publication is 1.44. Most publications were published in Proceedings international conference on Software engineering and ACM international conference Proceeding Series, with China and the United States emerging as the leading countries. It was discovered that international collaboration on the issue was inadequate. The most often used keywords in the publications were "software design," "code (symbols)," and "code generation." Following the content analysis, three themes emerged: 1) Integration of LLM into software engineering education, 2) application of LLM in software engineering, and 3) potential and limitation of LLM in software engineering. The results of this study are expected to provide researchers and academics with insights into the current state of LLM in software engineering research, allowing them to develop future conclusions. © (2025), (Science and Information Organization). All Rights Res

关键词： Software design

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