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Статті в журналах з теми "Knowledge based smart manufacturing system"
Vogel-Heuser, Birgit, Feng Ju, Cesare Fantuzzi, Yan Lu, and Dieter Hess. "Knowledge-Based Automation for Smart Manufacturing Systems." IEEE Transactions on Automation Science and Engineering 18, no. 1 (January 2021): 2–4. http://dx.doi.org/10.1109/tase.2020.3044620.
Повний текст джерелаAdamczyk, Bruno Sérgio, Anderson Luis Szejka, and Osiris Canciglieri. "Knowledge-based expert system to support the semantic interoperability in smart manufacturing." Computers in Industry 115 (February 2020): 103161. http://dx.doi.org/10.1016/j.compind.2019.103161.
Повний текст джерелаQu, Yuanju, Xinguo Ming, Yanrong Ni, Xiuzhen Li, Zhiwen Liu, Xianyu Zhang, and Liuyue Xie. "An integrated framework of enterprise information systems in smart manufacturing system via business process reengineering." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 233, no. 11 (December 20, 2018): 2210–24. http://dx.doi.org/10.1177/0954405418816846.
Повний текст джерелаStojadinovic, Slavenko M., Vidosav D. Majstorovic, Adam Gąska, Jerzy Sładek, and Numan M. Durakbasa. "Development of a Coordinate Measuring Machine—Based Inspection Planning System for Industry 4.0." Applied Sciences 11, no. 18 (September 10, 2021): 8411. http://dx.doi.org/10.3390/app11188411.
Повний текст джерелаChumnumporn, Kwanchanok, Chawalit Jeenanunta, Somrote Komolavanij, Natthawadee Saenluang, Kamonda Onsri, Koraphat Fairat, and Kanchanok Itthidechakhachon. "The Impact of IT Knowledge Capability and Big Data and Analytics on Firm’s Industry 4.0 Capability." Proceedings 39, no. 1 (January 9, 2020): 22. http://dx.doi.org/10.3390/proceedings2019039022.
Повний текст джерелаSujatha, M., N. Priya, A. Beno, T. Blesslin Sheeba, M. Manikandan, I. Monica Tresa, P. Subha Hency Jose, Vijayakumar Peroumal, and Sojan Palukaran Thimothy. "IoT and Machine Learning-Based Smart Automation System for Industry 4.0 Using Robotics and Sensors." Journal of Nanomaterials 2022 (September 15, 2022): 1–6. http://dx.doi.org/10.1155/2022/6807585.
Повний текст джерелаHorváth, Imre. "Connectors of smart design and smart systems." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 35, no. 2 (April 19, 2021): 132–50. http://dx.doi.org/10.1017/s0890060421000068.
Повний текст джерелаKoay, Fong Thai, Choo Jun Tan (Corresponding Author), Sin Yin Teh, Ping Chow Teoh, and Heng Chin Low. "SUPPORTING DECISION MAKING WITH AN ARIZ-BASED MODEL FOR SMART MANUFACTURING." Malaysian Journal of Computer Science 36, no. 1 (January 31, 2023): 53–78. http://dx.doi.org/10.22452/mjcs.vol36no1.4.
Повний текст джерелаChen, Shuxian, Zongqiang Ren, Xikai Yu, and Ao Huang. "A Dynamic Model of Evolutionary Knowledge and Capabilities Based on Human-Machine Interaction in Smart Manufactures." Computational Intelligence and Neuroscience 2022 (April 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/8584888.
Повний текст джерелаGaretti, Marco, Luca Fumagalli, and Elisa Negri. "Role of Ontologies for CPS Implementation in Manufacturing." Management and Production Engineering Review 6, no. 4 (December 1, 2015): 26–32. http://dx.doi.org/10.1515/mper-2015-0033.
Повний текст джерелаДисертації з теми "Knowledge based smart manufacturing system"
Cao, Qiushi. "Semantic technologies for the modeling of predictive maintenance for a SME network in the framework of industry 4.0 Smart condition monitoring for industry 4.0 manufacturing processes: an ontology-based approach Using rule quality measures for rule base refinement in knowledge-based predictive maintenance systems Combining chronicle mining and semantics for predictive maintenance in manufacturing processes." Thesis, Normandie, 2020. http://www.theses.fr/2020NORMIR04.
Повний текст джерелаIn the manufacturing domain, the detection of anomalies such as mechanical faults and failures enables the launching of predictive maintenance tasks, which aim to predict future faults, errors, and failures and also enable maintenance actions. With the trend of Industry 4.0, predictive maintenance tasks are benefiting from advanced technologies such as Cyber-Physical Systems (CPS), the Internet of Things (IoT), and Cloud Computing. These advanced technologies enable the collection and processing of sensor data that contain measurements of physical signals of machinery, such as temperature, voltage, and vibration. However, due to the heterogeneous nature of industrial data, sometimes the knowledge extracted from industrial data is presented in a complex structure. Therefore formal knowledge representation methods are required to facilitate the understanding and exploitation of the knowledge. Furthermore, as the CPSs are becoming more and more knowledge-intensive, uniform knowledge representation of physical resources and reasoning capabilities for analytic tasks are needed to automate the decision-making processes in CPSs. These issues bring obstacles to machine operators to perform appropriate maintenance actions. To address the aforementioned challenges, in this thesis, we propose a novel semantic approach to facilitate predictive maintenance tasks in manufacturing processes. In particular, we propose four main contributions: i) a three-layered ontological framework that is the core component of a knowledge-based predictive maintenance system; ii) a novel hybrid semantic approach to automate machinery failure prediction tasks, which is based on the combined use of chronicles (a more descriptive type of sequential patterns) and semantic technologies; iii) a new approach that uses clustering methods with Semantic Web Rule Language (SWRL) rules to assess failures according to their criticality levels; iv) a novel rule base refinement approach that uses rule quality measures as references to refine a rule base within a knowledge-based predictive maintenance system. These approaches have been validated on both real-world and synthetic data sets
蕭世良 and Sai-leung Siu. "A knowledge based process planning system for prismatic parts." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1991. http://hub.hku.hk/bib/B31232784.
Повний текст джерелаGovindan, Saravana. "A task based manufacturing knowledge maintenance method." Thesis, Loughborough University, 2013. https://dspace.lboro.ac.uk/2134/12414.
Повний текст джерелаRoy, Asok K. "Development of a knowledge based vision system for automated inspection." Thesis, Glasgow Caledonian University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363131.
Повний текст джерелаWondoloski, Karen M. "A knowledge-based cell controller and its integration in a manufacturing system." Thesis, Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/23341.
Повний текст джерелаSun, Qi-zhi. "Knowledge-based interactive real-time control system in product-focused manufacturing environment." Thesis, University of Portsmouth, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292501.
Повний текст джерелаAl-Awadhi, Waleed. "Integrating machine grouping and layout by using knowledge based system approach." Thesis, Brunel University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242982.
Повний текст джерелаAl-Khawaldeh, Mustafa Awwad Salem. "Ubiquitous robotics system for knowledge-based auto-configuration system for service delivery within smart home environments." Thesis, De Montfort University, 2014. http://hdl.handle.net/2086/10202.
Повний текст джерелаMahmood, Tariq. "Knowledge-based process planning and design system for the cold forging of steel." Thesis, Imperial College London, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264352.
Повний текст джерелаChun-Kit, Kwong. "A computer-aided concurrent design system." Thesis, University of Warwick, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263309.
Повний текст джерелаКниги з теми "Knowledge based smart manufacturing system"
Sun, Qi-zhi. Knowledge-based interactive real-time control system in product-focused manufacturing environment. Portsmouth: Portsmouth Polytechnic, Business School, 1991.
Знайти повний текст джерелаZhang, Jie. A prototype of tape tuning system with knowledge-based techniques. 1990.
Знайти повний текст джерелаWei, Wang. A knowledge based modelling system for the design and evaluation of flexible manufacturing facilities. 1989.
Знайти повний текст джерелаNowak, Dariusz, ed. Production–operation management. The chosen aspects. Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu, 2021. http://dx.doi.org/10.18559/978-83-8211-059-3.
Повний текст джерелаShengelia, Revaz. Modern Economics. Universal, Georgia, 2021. http://dx.doi.org/10.36962/rsme012021.
Повний текст джерелаUfimtseva, Nataliya V., Iosif A. Sternin, and Elena Yu Myagkova. Russian psycholinguistics: results and prospects (1966–2021): a research monograph. Institute of Linguistics, Russian Academy of Sciences, 2021. http://dx.doi.org/10.30982/978-5-6045633-7-3.
Повний текст джерелаЧастини книг з теми "Knowledge based smart manufacturing system"
Gembarski, Paul Christoph. "Design Catalogues as Knowledge-Base for CAD-Based Design Automation." In Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems, 645–52. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90700-6_73.
Повний текст джерелаSzejka, Anderson Luis, Fernando Mas, and Osiris Canciglieri Junior. "Towards Knowledge-Based System to Support Smart Manufacturing Processes in Aerospace Industry Based on Models for Manufacturing (MfM)." In Product Lifecycle Management. Green and Blue Technologies to Support Smart and Sustainable Organizations, 425–37. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94399-8_31.
Повний текст джерелаPark, Byungjun, and Jongpil Jeong. "Knowledge-Based Multi-agent System for Smart Factory of Small-Sized Manufacturing Enterprises in Korea." In Computational Science and Its Applications – ICCSA 2019, 81–93. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24311-1_6.
Повний текст джерелаGembarski, Paul Christoph, Dörthe Behrens, Jan Feldkamp, Lorenz Kies, and Lukas Hoppe. "Design Automation of a Motor Hoisting Crane – Results of Student Project on Knowledge-Based CAD." In Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems, 927–34. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90700-6_106.
Повний текст джерелаTorim, Ants, Innar Liiv, Chahinez Ounoughi, and Sadok Ben Yahia. "Pattern Based Software Architecture for Predictive Maintenance." In Communications in Computer and Information Science, 26–38. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17030-0_3.
Повний текст джерелаReisinger, Gerhard, Philipp Hold, and Wilfried Sihn. "Automated Information Supply of Worker Guidance Systems in Smart Assembly Environment." In IFIP Advances in Information and Communication Technology, 235–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72632-4_17.
Повний текст джерелаBehrendt, Wernher, and Felix Strohmeier. "Orchestration and Situation Awareness in an Assistance System for Assembly Tasks." In IFIP Advances in Information and Communication Technology, 332–43. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72632-4_25.
Повний текст джерелаLiu, Hongxia, Qingcheng Hu, Yong Zhang, Chunxiao Xing, and Ming Sheng. "A Knowledge-Based Health Question Answering System." In Smart Health, 286–91. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67964-8_29.
Повний текст джерелаZhu, Kunpeng. "Machine Vision Based Smart Machining System Monitoring." In Springer Series in Advanced Manufacturing, 267–95. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87878-8_8.
Повний текст джерелаLiu, Penghe, Xiaoqing Wang, Xiaoping Sun, Xi Shen, Xu Chen, Yuzhong Sun, and Yanjun Pan. "HKDP: A Hybrid Knowledge Graph Based Pediatric Disease Prediction System." In Smart Health, 78–90. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59858-1_8.
Повний текст джерелаТези доповідей конференцій з теми "Knowledge based smart manufacturing system"
Brundage, Michael P., Boonserm Kulvatunyou, Toyosi Ademujimi, and Badarinath Rakshith. "Smart Manufacturing Through a Framework for a Knowledge-Based Diagnosis System." In ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/msec2017-2937.
Повний текст джерелаKuvychko, Igor. "Knowledge and vision engines: a new generation of image understanding systems combining computational intelligence methods and model-based knowledge representation and reasoning." In Intelligent Systems and Smart Manufacturing, edited by David P. Casasent. SPIE, 2000. http://dx.doi.org/10.1117/12.403787.
Повний текст джерелаZuoxu, Wang, Li Xinyu, Chen Chun-Hsien, and Zheng Pai. "A Hypergraph-Based Knowledge Representation Model for Smart Product-Service System Development." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-66732.
Повний текст джерелаWang, Xi Vincent, Brenda N. Lopez N., Lihui Wang, Jinhui Li, and Winifred Ijomah. "A Smart Cloud-Based System for the WEEE Recovery/Recycling." In ASME 2014 International Manufacturing Science and Engineering Conference collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/msec2014-4109.
Повний текст джерелаKangru, Tavo, Kashif Mahmood, Tauno Otto, Madis Moor, and Jüri Riives. "Knowledge-Driven Based Performance Analysis of Robotic Manufacturing Cell for Design Improvement." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23541.
Повний текст джерелаWei, Qiongqiong, and Yanshuo Zhu. "On the Structure Model of Knowledge Manufacturing and Service System of Smart Library Based on Super-Network." In 2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2022. http://dx.doi.org/10.1109/ddcls55054.2022.9858543.
Повний текст джерелаHeilala, Janne, and Pawel Krolas. "Locating A Smart Manufacturing based on Supply Chain Segregation." In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003899.
Повний текст джерелаScholz, S., A. Elkaseer, T. Muller, U. Gengenbach, and V. Hagenmeyer. "Smart modular reconfigurable fully-digital manufacturing system with a knowledge-based framework: example of a fabrication of microfluidic chips." In 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE). IEEE, 2018. http://dx.doi.org/10.1109/coase.2018.8560405.
Повний текст джерелаDiaz-Elsayed, Nancy, Luis Hernandez, Ravi Rajamani, and Brian A. Weiss. "Asset Condition Management: A Framework for Smart, Health-Ready Manufacturing Systems." In ASME 2020 15th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/msec2020-8326.
Повний текст джерелаLi, Yunpeng, and Utpal Roy. "Challenges in Developing a Computational Platform to Integrate Data Analytics With Simulation-Based Optimization." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46410.
Повний текст джерелаЗвіти організацій з теми "Knowledge based smart manufacturing system"
Canto, Patricia, ed. The role of vocational training knowledge intensive business services. (Main conclusions). Universidad de Deusto, 2020. http://dx.doi.org/10.18543/vyqr9353.
Повний текст джерелаUnknown, Author. WINMOP-R03 Performance of Offshore Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), June 2003. http://dx.doi.org/10.55274/r0011744.
Повний текст джерелаTorrijos, Ivan Dario Pinerez, Tina Puntervold, Skule Strand, Panagiotis Aslanidis, Ingebret Fjelde, and Aleksandr Mamonov. Core restoration: A guide for improved wettability assessments. University of Stavanger, November 2021. http://dx.doi.org/10.31265/usps.198.
Повний текст джерелаUche, Chidi, Zita Ekeocha, Stephen Robert Byrn, and Kari L. Clase. Retrospective Study of Inspectors Competency in the Act of Writing GMP Inspection Report. Purdue University, December 2021. http://dx.doi.org/10.5703/1288284317445.
Повний текст джерела