Requirements and languages for the semantic representation of manufacturing systems

Negri, Elisa; Fumagalli, Luca; Garetti, Marco; Tanca, Letizia

doi:10.1016/j.compind.2015.10.009

Cited by 85 publications

(43 citation statements)

References 54 publications

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“…This way, physical and virtual dimensions coexist and are synchronized in real-time [19]. However, synchronization requires full data models of alternative scenarios to be simulated [32][33][34]. This issue led to the concept of Digital Twin (DT), or a virtual representation of physical objects coping their behaviour through a real-time data acquisition from the field [16,35].…”

Section: Simulationmentioning

confidence: 99%

Integrating Virtual Reality and Digital Twin in Circular Economy Practices: A Laboratory Application Case

et al. 2020

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The increasing awareness of customers toward climate change effects, the high demand instability affecting several industrial sectors, and the fast automation and digitalization of production systems are forcing companies to re-think their business strategies and models in view of both the Circular Economy (CE) and Industry 4.0 (I4.0) paradigms. Some studies have already assessed the relations between CE and I4.0, their benefits, and barriers. However, a practical demonstration of their potential impact in real contexts is still lacking. The aim of this paper is to present a laboratory application case showing how I4.0-based technologies can support CE practices by virtually testing a waste from electrical and electronic equipment (WEEE) disassembly plant configuration through a set of dedicated simulation tools. Our results highlight that service-oriented, event-driven processing and information models can support the integration of smart and digital solutions in current CE practices at the factory level.

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Section: Simulationmentioning

confidence: 99%

Integrating Virtual Reality and Digital Twin in Circular Economy Practices: A Laboratory Application Case

et al. 2020

Self Cite

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“…As stated and thoroughly discussed by Ramos [16] and Sanfilippo et al [36], to date, generally accepted manufacturing ontologies are lacking; there is no standard ontology for materials, products, resources, processes, or manufacturing systems. Moreover, ontology languages have different levels of expressivity, making careful process of evaluation for each use case necessary [37].…”

Section: Semantic Web Technologies For Mps According Tomentioning

confidence: 99%

“…e hierarchal classification of process selection knowledge alone is not sufficient to capture causal relationships. Semantic web rule language (SWRL) is an effective method to represent causal relations and has been widely applied in knowledge systems [37]. SWRL rules need to be considered to enable deductive reasoning and knowledge retrieval.…”

Section: Rules and Inferencementioning

confidence: 99%

An Ontology-Enabled Case-Based Reasoning Decision Support System for Manufacturing Process Selection

Mabkhot

Al-Samhan

Hidri

2019

Advances in Materials Science and Engineering

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In nowadays industry 4.0 and changeable manufacturing context, designers and manufacturing engineers struggle to determine appropriate quick, accurate (with flawless quality), and cost-effective processes to design highly customized products to meet customer requirements. To determine manufacturing processes, the matching between product features, material characteristics, and process capabilities needs to be optimized. Finding such an optimized matching is usually referred to as manufacturing process selection (MPS), which is not an easy task because of the infinite combinations of product features, numerous material characteristics, and various manufacturing processes. Although problems associated with MPS have received considerable attention, semantic web technologies are still underexplored and their potential is still uncovered. Almost no previous study has considered combining case-based reasoning (CBR) with ontologies, a famous and powerful semantic web enabler, to achieve MPS. In this study, we developed a decision support system (DSS) for MPS based on ontology-enabled CBR. By applying automatic reasoning and similarity retrieving on an industrial case study, we show that ontologies enable process selection by determining competitive matching between product features, material characteristics, and process capabilities and by endorsing effective case retrieval.

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“…Thus, it comes as no surprise that the application of OWL ontologies in the representation of product information (including tolerance information) is gaining importance and popularity within the academia [81][82][83][84][85]. However, the OWL ontology-based model is probably not an efficient representation model for tolerance information as a whole, because OWL ontology has drawbacks in the aspects of degree of automation, time complexity, additional work, and negation in the model [80].…”

Section: Ontology-based Modelmentioning

confidence: 99%

A review of representation models of tolerance information

Qin

et al. 2017

Int J Adv Manuf Technol

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A good representation model of tolerance information is indispensable for achieving computer-aided tolerancing. Currently, the representation model used in industry is the EXPRESS model, while at the same time, a number of other kinds of representation models have been presented within the academia. The coexistence of different kinds of representation models generates a series of questions and discussions: can the EXPRESS model completely implement the representation of tolerance information semantics in an explicit, computer-readable, and computer-interpretable way? What challenges have been addressed to date by the presented representation models? What are the advantages and disadvantages of each representation model? What capabilities should an ideal representation model have? What are the potential research directions in tolerance information representation in the future? To approach these questions, a review of representation models of tolerance information is presented in this paper. An in-depth analysis of existing representation models is firstly provided. Then, the review makes a detailed comparison among them based on this analysis. Finally, some future research directions in tolerance information representation are suggested.

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Requirements and languages for the semantic representation of manufacturing systems

Cited by 85 publications

References 54 publications

Integrating Virtual Reality and Digital Twin in Circular Economy Practices: A Laboratory Application Case

Integrating Virtual Reality and Digital Twin in Circular Economy Practices: A Laboratory Application Case

An Ontology-Enabled Case-Based Reasoning Decision Support System for Manufacturing Process Selection

A review of representation models of tolerance information

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