scholarly journals An AutomationML Based Ontology for Sensor Fusion in Industrial Plants

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1311 ◽  
Author(s):  
Eder Gonçalves ◽  
Alvaro Freitas ◽  
Silvia Botelho
Keyword(s):  
Sensor Fusion ◽  
Production Systems ◽  
Data Format ◽  
Industrial Plants ◽  
Knowledge Based ◽  
Complete Knowledge ◽  
Engineering Data ◽  
Basic Functions ◽  
Sensors Fusion

AutomationML (AML) can be seen as a partial knowledge-based solution for manufacturing and automation domains since it permits integrating different engineering data format, and also contains information about physical and logical structures of production systems, using basic concepts as resources, process, and products, in semantic structures. However, it is not a complete knowledge-based solution because it does not have mechanisms for querying and reasoning procedures, which are basic functions for semantic inferences. Additionally, AutomationML does not deal with aspects of sensor fusion naturally. In this sense, we propose an ontology to describe those sensors’ fusion elements, including procedures for runtime processing, and also elements that can turn AutomationML into a complete knowledge-based solution. The approach was applied in a case study with two different industrial processes with some sensors under fusion. The results obtained demonstrate that the ontology allows describing sensors that are under fusion and deal with the occurrence of data divergence. In a broader view, the results show how to apply AutomationML description for runtime processing of data generated from different sensors of a manufacturing system using an ontology to complement the AML description, where AutomationML concentrates knowledge about a specific production system and the ontology describes a general and reusable knowledge about sensor fusion.

Cyberphysical Design Automation Framework for Knowledge-based Engineering

2013 ◽  
Vol 1 (1) ◽  
pp. 158-178
Author(s):  
Urcun John Tanik
Keyword(s):  
System Design ◽  
Design Process ◽  
Design Automation ◽  
Design Theory ◽  
Knowledge Bases ◽  
Knowledge Based ◽  
Knowledge Based Engineering ◽  
Cyberphysical System ◽  
Automation Framework

Cyberphysical system design automation utilizing knowledge based engineering techniques with globally networked knowledge bases can tremendously improve the design process for emerging systems. Our goal is to develop a comprehensive architectural framework to improve the design process for cyberphysical systems (CPS) and implement a case study with Axiomatic Design Solutions Inc. to develop next generation toolsets utilizing knowledge-based engineering (KBE) systems adapted to multiple domains in the field of CPS design automation. The Cyberphysical System Design Automation Framework (CPSDAF) will be based on advances in CPS design theory based on current research and knowledge collected from global sources automatically via Semantic Web Services. A case study utilizing STEM students is discussed.

SEISMIC RISK AND RESILIENCE ASSESSMENT OF INDUSTRIAL FACILITIES: CASE STUDY ON A BLACK CARBON PLANT

2020 ◽  
Author(s):  
George Karagiannakis
Keyword(s):  
Seismic Risk ◽  
Numerical Models ◽  
Fragility Curves ◽  
Human Life ◽  
Mitigation Strategies ◽  
Economic Losses ◽  
Risk And Resilience ◽  
Industrial Plants ◽  
Plant Equipment

This paper deals with state of the art risk and resilience calculations for industrial plants. Resilience is a top priority issue on the agenda of societies due to climate change and the all-time demand for human life safety and financial robustness. Industrial plants are highly complex systems containing a considerable number of equipment such as steel storage tanks, pipe rack-piping systems, and other installations. Loss Of Containment (LOC) scenarios triggered by past earthquakes due to failure on critical components were followed by severe repercussions on the community, long recovery times and great economic losses. Hence, facility planners and emergency managers should be aware of possible seismic damages and should have already established recovery plans to maximize the resilience and minimize the losses. Seismic risk assessment is the first step of resilience calculations, as it establishes possible damage scenarios. In order to have an accurate risk analysis, the plant equipment vulnerability must be assessed; this is made feasible either from fragility databases in the literature that refer to customized equipment or through numerical calculations. Two different approaches to fragility assessment will be discussed in this paper: (i) code-based Fragility Curves (FCs); and (ii) fragility curves based on numerical models. A carbon black process plant is used as a case study in order to display the influence of various fragility curve realizations taking their effects on risk and resilience calculations into account. Additionally, a new way of representing the total resilience of industrial installations is proposed. More precisely, all possible scenarios will be endowed with their weighted recovery curves (according to their probability of occurrence) and summed together. The result is a concise graph that can help stakeholders to identify critical plant equipment and make decisions on seismic mitigation strategies for plant safety and efficiency. Finally, possible mitigation strategies, like structural health monitoring and metamaterial-based seismic shields are addressed, in order to show how future developments may enhance plant resilience. The work presented hereafter represents a highly condensed application of the research done during the XP-RESILIENCE project, while more detailed information is available on the project website https://r.unitn.it/en/dicam/xp-resilience.

scholarly journals Projections of Local Knowledge-Based Adaptation Strategies of Mexican Coffee Farmers

Climate ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 60
Author(s):  
Patricia Ruiz-García ◽  
Cecilia Conde-Álvarez ◽  
Jesús David Gómez-Díaz ◽  
Alejandro Ismael Monterroso-Rivas
Keyword(s):  
Climate Change ◽  
Extreme Events ◽  
Local Knowledge ◽  
Production Systems ◽  
Agroforestry Systems ◽  
Southern Mexico ◽  
Knowledge Based ◽  
Coffee Farmers ◽  
Extreme Hydrological Events ◽  
Adaptation Policies

Local knowledge can be a strategy for coping with extreme events and adapting to climate change. In Mexico, extreme events and climate change projections suggest the urgency of promoting local adaptation policies and strategies. This paper provides an assessment of adaptation actions based on the local knowledge of coffee farmers in southern Mexico. The strategies include collective and individual adaptation actions that farmers have established. To determine their viability and impacts, carbon stocks and fluxes in the system’s aboveground biomass were projected, along with water balance variables. Stored carbon contents are projected to increase by more than 90%, while maintaining agroforestry systems will also help serve to protect against extreme hydrological events. Finally, the integration of local knowledge into national climate change adaptation plans is discussed and suggested with a local focus. We conclude that local knowledge can be successful in conserving agroecological coffee production systems.

scholarly journals MODEL-BASED DESIGN OF AM COMPONENTS TO ENABLE DECENTRALIZED DIGITAL MANUFACTURING SYSTEMS

2021 ◽  
Vol 1 ◽  
pp. 2127-2136
Author(s):  
Olivia Borgue ◽  
John Stavridis ◽  
Tomas Vannucci ◽  
Panagiotis Stavropoulos ◽  
Harry Bikas ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Data Generation ◽  
Production Networks ◽  
Model Based ◽  
Manufacturing Technologies ◽  
Am Processes ◽  
Final Consumer

AbstractAdditive manufacturing (AM) is a versatile technology that could add flexibility in manufacturing processes, whether implemented alone or along other technologies. This technology enables on-demand production and decentralized production networks, as production facilities can be located around the world to manufacture products closer to the final consumer (decentralized manufacturing). However, the wide adoption of additive manufacturing technologies is hindered by the lack of experience on its implementation, the lack of repeatability among different manufacturers and a lack of integrated production systems. The later, hinders the traceability and quality assurance of printed components and limits the understanding and data generation of the AM processes and parameters. In this article, a design strategy is proposed to integrate the different phases of the development process into a model-based design platform for decentralized manufacturing. This platform is aimed at facilitating data traceability and product repeatability among different AM machines. The strategy is illustrated with a case study where a car steering knuckle is manufactured in three different facilities in Sweden and Italy.

scholarly journals Semantic and knowledge based support to business model evaluation to stimulate green behaviour of electric vehicles’ drivers and energy prosumers

2021 ◽  
Author(s):  
Beniamino Di Martino ◽  
Dario Branco ◽  
Luigi Colucci Cante ◽  
Salvatore Venticinque ◽  
Reinhard Scholten ◽  
...  
Keyword(s):  
Business Model ◽  
Model Evaluation ◽  
Business Models ◽  
Real Case ◽  
Sustainable Mobility ◽  
Semantic Framework ◽  
Knowledge Based ◽  
Survey Results ◽  
Energy Prosumers

AbstractThis paper proposes a semantic framework for Business Model evaluation and its application to a real case study in the context of smart energy and sustainable mobility. It presents an ontology based representation of an original business model and examples of inferential rules for knowledge extraction and automatic population of the ontology. The real case study belongs to the GreenCharge European Project, that in these last years is proposing some original business models to promote sustainable e-mobility plans. An original OWL Ontology contains all relevant Business Model concepts referring to GreenCharge’s domain, including a semantic description of TestCards, survey results and inferential rules.

Knowledge transfer roles of subsidiaries in MNCs’ networks

2019 ◽  
Vol 29 (4) ◽  
pp. 329-346 ◽  
Author(s):  
Cigdem Baskici
Keyword(s):  
Knowledge Transfer ◽  
Single Case ◽  
Research Question ◽  
Hierarchical Structures ◽  
Knowledge Flows ◽  
Consumer Electronics ◽  
Knowledge Network ◽  
Content Type ◽  
Knowledge Based

Purpose Although there have been a considerable number of studies regarding subsidiary role typology in multinationals’ management literature, there appear to be few studies that consider knowledge-based role typology from the network-based perspective. The purpose of this study is to fill this gap and extend the study of Gupta and Govindarajan (1991). Thus, the study focuses on answering the following research question: Do subsidiaries have different roles in terms of knowledge flows within a multinational company (MNC)? Design/methodology/approach This empirical study has been carried out as an explorative single case study. An MNC with 15 foreign subsidiaries headquartered in Turkey, which operated in the manufacturing of household appliances and consumer electronics, has been selected as the case. Knowledge transfer is analyzed in this MNC from the network perspective. Findings Four role typologies are detected for subsidiaries of the MNC: collector transmitter, collector diffuser, converter transmitter and converter diffuser. Research limitations/implications Findings of this study are specific to this case. Testing the findings in a sample consisting of subsidiaries of MNCs producing transnational products may contribute to the generalizability of these roles. Practical implications This study offers potentially important findings for MNC managers to use. First, in this study, knowledge flows' route could be defined within MNCs’ dual network. Second, role typologies could inform MNC managers to design their MNCs’ knowledge network. Originality/value The suggested typologies are expected to more accurately define the roles of subsidiaries within contemporary MNCs which are accepted to be transformed from hierarchical structures to network-based organizations.

DEVELOPMENT OF A KNOWLEDGE-BASED INTELLIGENT CAD SYSTEM FOR AUTOMOTIVE PANEL DIE

2008 ◽  
Vol 07 (01) ◽  
pp. 51-54 ◽  
Author(s):  
HUI-XIA LIU ◽  
WEI WEI ◽  
XIAO WANG ◽  
LAN CAI
Keyword(s):  
Die Design ◽  
Design System ◽  
Working Process ◽  
Cad System ◽  
Knowledge Based ◽  
Working Principle ◽  
Panel Die ◽  
Automotive Panels ◽  
Automotive Panel

A knowledge-based intelligent die design system for automotive panels is developed by UG software platform. This system can accomplish design intelligently and automatically through engineering rules in the knowledge base. The framework and implementation of the system are discussed. Finally, a case study of the panel die design of car trunk in the system is implemented, which illustrates working process, working principle, implement method and practicability of the system, and validates the advanced design conception proposed in this paper.

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