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17 results for “Industry 4.0”
Dataset of Survey Results on the Integration of Industry 4.0 in University Education (Baja California, 2024)
<p>This dataset contains the results of a survey conducted in 2024 on the integration of Industry 4.0 concepts and technologies in university education in Baja California. The survey was designed to assess the current state of adoption, challenges, and opportunities related to Industry 4.0 within academic institutions. The data includes responses from engineering students at the Autonomous University of Baja California (UABC) and the Polytechnic University of Baja California (UPBC). The insights gathered aim to inform future strategies for enhancing the implementation of Industry 4.0 in higher education curricula.</p>
Datenaufbereitung zum State-of-the-Art und Fortschritt europäischer Gaia-X sowie Datenraum Initiativen mit einem Schwerpunkt auf Industrie 4.0 Anwendungsfälle
<p><span>In seiner Gesamtheit umfasst der Datensatz eine Sammlung von Initiativen und Projekten, die mit </span><span>Gaia-X sowie Data Spaces im Allgemeinen in Verbindung stehen</span><span>.</span> Ziel der Datensammlung war es, eine detaillierte Übersicht über bestehende Projekte zu erhalten und diese systematisch zu kategorisieren, um anschließend spezifische industrielle Anwendungsfälle herauszuarbeiten und diese zu analysieren. Der Zeitraum dieser Sammlung erstreckte sich von April 2023 bis März 2024. Anzumerken ist, dass sämtliche zur Verfügung stehenden Informationen innerhalb dieses Zeitraums in den Datensatz aufgenommen wurden. Ab März 2024 wurden keine weiteren Daten erfasst, wodurch der Datensatz den aktuellen Stand bis zu diesem Zeitpunkt widerspiegelt.</p> <p>Insgesamt wurden 211 Initiativen aus 281 Quellen zusammengetragen und ausgewertet. Diese unterteilen sich (über alle Domänen hinweg) in 102 Data Spaces und 93 Anwendungsfälle. Die 93 Anwendungsfälle beinhalten 47 Industrie 4.0 relevante Anwendungsfälle.</p>
A Benchmark Dataset with Knowledge Graph Generation for Industry 4.0 Production Lines
<p>A benchmark dataset for knowledge graph generation in Industry 4.0 production lines and to show the benefits of using ontologies and semantic annotations of data to showcase how I4.0 industry can benefit from KGs and semantic datasets. This work is a result of collaborations with the production line managers, supervisors, and engineers of a football industry to acquire realistic production line data. Knowledge Graphs (KGs) or a Knowledge Graph (KG) emerged as a significant technology to store the semantics of the domain entities. The data is mapped and populated with RGOM classes and relations using an automated solution based on JenaAPI, producing an I4.0 KG. <br> <br> Usage:<br> <br> Recently, we use this dataset to analyze the performance of the five state-of-the-art KG embedding models, namely ComplEx, DistMult,TransE, ConvKB, and ConvE. We evaluated the models using two key metrics: Mean Reciprocal Rank (MRR), and Hits@N (Hits@10, Hits@3, and Hits@1). We observed that the TransE model outperforms other models, followed by ComplEx and DistMult, with ConvE demonstrating the lowest performance. Similarly, the dataset can be used alternatively in other potential scenarios.</p>
Designing industry 4.0 implementation from the initial background and context of companies
<p>Industry 4.0 is a promising concept that allows industries to meet customers' demands with flexible and resilient processes, and highly personalised products. This concept is made up of different dimensions. For a long time, innovative digital technology has been thought of as the only dimension to succeed in digital transformation projects. Next, other dimensions have been identified such as organisation, strategy, and human resources as being key while rolling out digital technology in factories. From these findings, researchers have designed industry 4.0 theoretical models and then, built readiness models that allow for analysing the gap between the company initial situation and the theoretical model. Nevertheless, this purely deductive approach does not take into consideration a company's background and context, and eventually favours one single digital transformation model. This article aims at analysing four actual digital transformation projects and demonstrating that the digital transformation's success or failure depends on the combination of two variables related to a company's background and context. This research is based on a double approach: deductive and inductive. First, a literature review has been carried out to define industry 4.0 concept and its main dimensions and digital transformation success factors, as well as barriers, have been investigated. Second, a qualitative survey has been designed to study in-depth four actual industry digital transformation projects, their genesis as well as their execution, to analyse the key variables in succeeding or failing. 46 semi-structured interviews were carried out with projects' members. The interviews have been analysed with thematic content analysis. Then, each digital transformation project has been modelled regarding the key variables and analysed with regards to succeeding or failing. Investigated projects have consolidated the models of digital transformation. Finally, nine digital transformation models have been identified. Industry practitioners could design their digital transformation project organisation and strategy according to the right model.</p>
Industrie 4.0 - Forschungskooperationen mit der CDHAW und der Tongji Universität Shanghai
<p><b>Abstract</b></p><p class="dhik-abstract-content">Viele mittelständische Unternehmen scheuen den Wechsel zu Industrie 4.0. Ängste vor zu hohen Kosten, Ängste vor Veränderungen stehen hier im Mittelpunkt. Beide sind grundlos, wie das Beispiel eines Projekts an der CDHAW Shanghai vor Augen führt. Prozessaufnahmen sowie Übertragungen in die digitale Welt lassen sich auch mit einfachen Mittel, z.b. Praktikanten der CDHAW umsetzen. </p><p></p><p><b>Weitere Beiträge aus dem DHIK-Forum 2022 auf Zenodo:</b></p><p class="dhik-session-list"></p><ul><li>Session #1: Viktor Sigrist: Internationalisierung - Partnerschaften für den Ausbau von Forschung und Entwicklung (DOI:<a href="https://zenodo.org/record/7123701">10.5281/zenodo.7123701</a>)</li><li>Session #2: Dieter Leonhard: DHIK- Strategien der internationalen Zusammenarbeit in Forschung und Lehre (DOI:<a href="https://zenodo.org/record/7123456">10.5281/zenodo.7123456</a>)</li><li>Session #3: Stephen Wittkopf: Wissens- und Innovationstransfer - Interdisziplinäre Zusammenarbeit mit Unternehmen und Institutionen (DOI:<a href="https://zenodo.org/record/7025707">10.5281/zenodo.7025707</a>)</li><li>Session #4: Xiao Feng: CDHAW - Chinesisch-Deutsche Hochschule für Angewandte Wissenschaften (DOI:<a href="https://zenodo.org/record/7123458">10.5281/zenodo.7123458</a>)</li><li>Session #5: Antonio Pita und Isabel Kreiner: Academy-Industry-Collaboration - Outreach Strategy (DOI:<a href="https://zenodo.org/record/7123460">10.5281/zenodo.7123460</a>)</li><li>Session #6: Martin Sternberg: Promotionsrecht – aktueller Stand an deutschen Hochschulen für angewandte Wissenschaften (DOI:<a href="https://zenodo.org/record/7123757">10.5281/zenodo.7123757</a>)</li><li>Session #7: Adrian Derungs: Duo mit Innovationskraft - Zusammenspiel von Forschung und Wirtschaft in der Zentralschweiz (DOI:<a href="https://zenodo.org/record/7123767">10.5281/zenodo.7123767</a>)</li><li>Session #8: Theres Paulsen: Transdisziplinäre Forschung - komplexe gesellschaftliche Herausforderungen erfordern diverse Ansätze (DOI:<a href="https://zenodo.org/record/7123769">10.5281/zenodo.7123769</a>)</li><li>Session #9: Jörg Schneider: International research collaboration - New funding opportunities for universities of applied sciences (DOI:<a href="https://zenodo.org/record/7123771">10.5281/zenodo.7123771</a>)</li><li>Session #10: Cornelia Spycher und Matthew Whellens: Horizon Europe - overview of funding opportunities for your research and innovation (DOI:<a href="https://zenodo.org/record/7123773">10.5281/zenodo.7123773</a>)</li><li>Session #11: Janique Siffert: Eureka Eurostars - erfolgreiche Förderung für internationale Innovationsprojekte (DOI:<a href="https://zenodo.org/record/7123777">10.5281/zenodo.7123777</a>)</li><li>Session #12: Ludger Fischer: Energy Lab - ein Netzwerk für innovative Lösungen im Energiebereich (DOI:<a href="https://zenodo.org/record/7123779">10.5281/zenodo.7123779</a>)</li><li>Session #13: Jörg Worlitschek: Thermal energy storage - heating the north, cooling the south (DOI:<a href="https://zenodo.org/record/7123781">10.5281/zenodo.7123781</a>)</li><li>Session #14: Jonas Mühlethaler: Neues DC Microgrid-Konzept – netzunabhängige Elektrifizierung in Entwicklungsländern (DOI:<a href="https://zenodo.org/record/7123783">10.5281/zenodo.7123783</a>)</li><li>Session #15: Tommy Claussen: Dekarbonisierung des Gebäudesektors - digitale Transformation in der Gebäudetechnik und im Gebäudemanagement (DOI:<a href="https://zenodo.org/record/7123785">10.5281/zenodo.7123785</a>)</li><li>Session #16: Christoph Imboden: Flexibility solutions - making the power grid fit for the future (DOI:<a href="https://zenodo.org/record/7123787">10.5281/zenodo.7123787</a>)</li><li>Session #17: Uwe Schulz: Spielerisches Sarnetz - Simulationen für die fossile Unabhängigkeit einer Ortschaft (DOI:<a href="https://zenodo.org/record/7123790">10.5281/zenodo.7123790</a>)</li><li>Session #18: Jana Koehler: Künstliche Intelligenz – Erfolg durch Erwünschtheit, Machbarkeit und Wirtschaftlichkeit (DOI:<a href="https://zenodo.org/record/7123792">10.5281/zenodo.7123792</a>)</li><li>Session #19: Rolf Kamps: KI in der Prävention - Befragungsmethoden und Schulungen trainieren, Krankheitserreger erkennen (DOI:<a href="https://zenodo.org/record/7123794">10.5281/zenodo.7123794</a>)</li><li>Session #20: Gwendolyne Pascua: Artificial Intelligence in Space - CIMON assisting astronauts on the International Space Station (DOI:<a href="https://zenodo.org/record/7123796">10.5281/zenodo.7123796</a>)</li><li>Session #21: Tobias Matter et.al.: Augmented Reality Soundscapes - mit maschinellem Lernen Klangkulissen von zukünftigen Bauvorhaben generieren (DOI:<a href="https://zenodo.org/record/7123798">10.5281/zenodo.7123798</a>)</li><li>Session #22: Angela Nicoara: Internet of Things - transforming businesses, people's lives and driving growth in the coming years (DOI:<a href="https://zenodo.org/record/7123800">10.5281/zenodo.7123800</a>)</li><li>Session #23: Adrian Koller: Feldrobotik - unermüdliche und zunehmend intelligentere Hilfe in der Landwirtschaft (DOI:<a href="https://zenodo.org/record/7123802">10.5281/zenodo.7123802</a>)</li><li>Session #24: Widar von Arx et.al.: Realisierung der Verkehrswende - Einfluss der Preispolitik in der Mobilität (DOI:<a href="https://zenodo.org/record/7124000">10.5281/zenodo.7124000</a>)</li><li>Session #25: Andreas Liebrich: Tourismusdateninfrastruktur - Was die Schweiz von Europa lernen kann (DOI:<a href="https://zenodo.org/record/7123806">10.5281/zenodo.7123806</a>)</li><li>Session #26: Frank Pöhlau und Stefan May: Find life on Mars - Schülerprojekte zur mobilien Robotik (DOI:<a href="https://zenodo.org/record/7123808">10.5281/zenodo.7123808</a>)</li><li>Session #27: Jiayun Shen: Open Innovation - Innovationsmanagement bei der Schweizerischen Post (DOI:<a href="https://zenodo.org/record/7123810">10.5281/zenodo.7123810</a>)</li><li>Session #28: Tobias Specker: Interkulturelles Management – innovative Konzepte zum Ausbau der China-Kompetenzen an Hochschulen (DOI:<a href="https://zenodo.org/record/7123812">10.5281/zenodo.7123812</a>)</li><li>Session #29: Elena Algorri: Swimming robots - exploring the unterwater from the surface (DOI:<a href="https://zenodo.org/record/7123814">10.5281/zenodo.7123814</a>)</li><li>Session #30: Sergio Camacho: Robotics and Digital Systems Engineering at the Tec de Monterrey (DOI:<a href="https://zenodo.org/record/7123816">10.5281/zenodo.7123816</a>)</li><li><b>Session #31: Thomas Dorn: Industrie 4.0 - Forschungskooperationen mit der CDHAW und der Tongji Universität Shanghai (<a href="#collapseTwo">Video</a>)</b></li><li>Session #32: Walter Reichert et.al.: Kollaboration und Unterstützung - Mobile Robotik und Exoskelette in der flexiblen Produktion (DOI:<a href="https://zenodo.org/record/7123820">10.5281/zenodo.7123820</a>)</li><li>Session #33: Louis Palmer: Solar Butterfly - climate pioneer world tour supported by HSLU (DOI:<a href="https://zenodo.org/record/7123822">10.5281/zenodo.7123822</a>)</li></ul><p></p>
International comparison of cross-disciplinary integration in industry 4.0: A co-authorship analysis using academic literature databases
<p>In innovation strategy, a type of Schumpeterian competitive strategy in business administration, "intra-individual diversity" has attracted attention as one factor for creating innovation. In this study, we redefine "framework for identifying researchers' areas of expertise" as "a framework for quantifying intra-individual diversity among researchers. Note that diversity here refers to authorship of articles in multiple research fields. The application of this framework then made it possible to visualize organizational diversity by accumulating the intra-individual diversity of researchers and to discuss the innovation strategy of the organization. The analysis in this study discusses how countries are promoting research on the topics of artificial intelligence (AI), big data, and Internet of Things (IoT) technologies, which are at the core of Industry 4.0, from an innovation perspective. Note that Industry 4.0 is a technological framework that aims to "improve the efficiency of all social systems," "create new industries," and "increase intellectual productivity." For the analysis, we used 19-year bibliographic data (2000–2018) from the top 20 countries in terms of the number of papers in AI, big data, and IoT technologies. As the results, this study classified the styles of cross-disciplinary fusion into four patterns in AI and three patterns in big data. This study did not consider the results in IoT because of only small differences between countries. Furthermore, regional differences in the style of cross-disciplinary fusion were also observed, and the global innovation patterns in Industry 4.0 were classified into seven categories. In Europe and North America, the cross-disciplinary integration style was similar to that between the United States, Germany, the Netherlands, Spain, England, Italy, Canada, and France. In Asia, the cross-disciplinary fusion style was similar between China, Japan, and South Korea.</p>
Survey on Industry 4.0 implementation within 5 companies in Slovakia
<p>Dataset containing results of a survey related to Industry 4.0 implementation and usage distributed within employees of 5 selected companies in Slovakia</p>
Designing industry 4.0 implementation from the initial background and context of companies
Open the record for dataset details and reuse information.
International comparison of cross-disciplinary integration in industry 4.0: A co-authorship analysis using academic literature databases
Open the record for dataset details and reuse information.
Industrial Electronics 4.0: Is It Going to Be Free and Open?
<p>The presentation addresses phenomena of free software and open hardware and it is based on twelve years of the author's experience in using free software, ten years of teaching a course that covers free software tools used in electrical engineering, about five years of experience with open hardware, and four years of teaching a reformed course in electrical measurements, which involves extensive use of free software. Teaching experiences are presented in the paper, plans for further improvements in existing courses, as well as some ideas about new courses that would cover free software and open hardware topics not covered already. Actual software tools are discussed. Also, the use of free software, open hardware, open data, and open culture in general, in research practice is presented, where open access articles and open public repositories are new means for disseminating scientific information supporting reproducibility and verifiability of the results, extending the information exchange beyond the paper form. In embedded systems, the method of building upon finds free software a useful tool, besides security and privacy advantages. Promising ongoing projects, like open instruction set architectures, are also discussed. In the time when Industry 4.0 and the fourth industrial revolution are discussed, it seems that open models of software, scientific, and intellectual production are unexpected winners, regardless indirect supporting business models. According to author's experience, free software is mature enough and offers numerous advantages to be a basic tool in education, and open culture practices require modification of curricula to address the situation already present.</p>
Tracking sustainable Industry 4.0 for listed companies - DataSet
<p>The DataSet contains a set of data that informs the analyses conducted for the Tracking sustainable Industry 4.0 for listed companies study. <span>This study aims to develop a replicable methodology for diagnosing sustainable Industry 4.0 status of publicly listed companies. Employing quantitative content analysis through MAXQDA software, the authors utilized a predefined keyword set and categories with lemmatization on both annual reports and Internet news. The analysis involved companies listed on the Warsaw Stock Exchange, representing about two-thirds of the total market capitalization across various sectors. Inconsistencies in communication and Internet news were observed, influenced by company characteristics. The methodology effectively addressed research questions on the incorporation of Industry 4.0 and sustainability in company reports and online communications. However, a limitation is the absence of a qualitative perspective to elucidate underlying phenomena for potential correlations. Despite this, the methodology provides valuable signals for policymakers, aiding in positioning economies globally on Industry 4.0 and sustainability. The research demonstrates high potential for replicability and traceability, applicable to other indices for sample selection, sector analyses, market exploration, and facilitating year-to-year comparisons.</span></p>
Types of competencies and skills for Industry 4.0
<p><strong>Types of competencies and skills for Industry 4.0</strong></p>
Dataset: Architecture-based Attack Propagation and Variation Analysis for Identifying Confidentiality Issues in Industry 4.0
<p>Dataset for the publication <em>Architecture-based Attack Propagation and Variation Analysis for Identifying Confidentiality Issues in Industry 4.0</em></p> <p>More Information can be found in the zipped Readme</p>
Architectural Uncertainty Analysis for Access Control Scenarios in Industry 4.0 - Data Set
<p>This data set contains additional information to the master's thesis of Nicolas Boltz. Included are the implementation, tests, and model instances of sample scenarios.</p>
Dataset used in Paper 1046 of the IFAC World Congress 2020 "Data Quality Assessment for System Identification in the Age of Big Data and Industry 4.0"
<p><br> The data used was obtained from a section of the lead zinc concentrator at the Mount Isa Mines in Queensland, Australia.The data collected for this investigation consists of two months of plant operation, collected at a a frequency of one minute. The historian’s interpolation routine is used to ensure the data is aligned. No special care was used to ensure that the data had any particular characteristics, other than that the plant was running. It was reported that during this period some step tests had been conducted. Forty-three variables were collected: for each of the PID controllers, setpoint, process value and output (SV/PV/MV) were recorded. The three analysers provide measure of iron, lead and zinc percentages.</p>
Raw OPC-UA Dataset of a Working Industry 4.0 Smart Factory (H-DA AutFab)
<p>This OPC-UA dataset was recorded in the AutFab, the fully automated Industry 4.0 learning factory of the University of Applied Sciences Darmstadt (Description of the AutFab, refer to: 10.1016/j.promfg.2017.04.023). Recording Date was the 2019-03-13. Five successful assemblies would be done during recording. The Dataset consists of 17.464 columns and 11.455 rows. Each column correspond to an OPC-UA node. The pre-transformation was limited to transpose the logformat: time, opcua-nodeid, value to time, opcua-nodeid 1, ..., opcua-nodeid N and align all events by time. A short analysis was carried out, which shows that only 1022 columns change more than once. This dataset contains no anomalies or errors. Usage must be requested.</p>
Dataset and code for the publication "From Unstructured Product Descriptions to Structured Data for Industry 4.0 with ChatGPT"
<p>This is the code as well as the dataset for our publication "From Unstructured Product Descriptions to Structured Data for Industry 4.0 with ChatGPT".</p><p>Please see the README.md for more information.</p>
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