Modeling Manufacturing Resources: An Ontological Approach

Sanfilippo, Emilio M.; Benavent, Sergio; Borgo, Stefano; Guarino, Nicola; Troquard, Nicolas; Romero, Fernando; Rosado, P.; Solano, Lorenzo; Belkadi, Farouk; Bernard, Alain

doi:10.1007/978-3-030-01614-2_28

Cited by 12 publications

(4 citation statements)

References 18 publications

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“…Thus, AI technologies have seldom been adopted in these studies. At present, ontology (as shown in Section IV) offers insights into dynamic reconfiguration of manufacturing resources [102], [126].…”

Section: B Dynamic Reconfiguration Of Manufacturing Systemsmentioning

confidence: 99%

Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges

et al. 2021

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The traditional production paradigm of large batch production does not offer flexibility towards satisfying the requirements of individual customers. A new generation of smart factories is expected to support new multi-variety and small-batch customized production modes. For that, Artificial Intelligence (AI) is enabling higher value-added manufacturing by accelerating the integration of manufacturing and information communication technologies, including computing, communication, and control. The characteristics of a customized smart factory are to include self-perception, operations optimization, dynamic reconfiguration, and intelligent decision-making. The AI technologies will allow manufacturing systems to perceive the environment, adapt to the external needs, and extract the process knowledge, including business models, such as intelligent production, networked collaboration, and extended service models.This paper focuses on the implementation of AI in customized manufacturing (CM). The architecture of an AI-driven customized smart factory is presented. Details of intelligent manufacturing devices, intelligent information interaction, and construction of a flexible manufacturing line are showcased. The state-of-the-art AI technologies of potential use in CM, i.e., machine learning, multi-agent systems, Internet of Things, big data, and cloud-edge computing are surveyed. The AI-enabled technologies in a customized smart factory are validated with a case study of customized packaging. The experimental results have demonstrated that the AI-assisted CM offers the possibility of higher production flexibility and efficiency. Challenges and solutions related to AI in CM are also discussed.

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Section: B Dynamic Reconfiguration Of Manufacturing Systemsmentioning

confidence: 99%

Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges

et al. 2021

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“…EdsUnit (eco-design software unit) particularizes the class for the definite modeling of eco-design software units, see (Eds_1) and (Eds_2). The relation ModeOf applied in (Eds_1) – (Eds_2) is quoted on the basis of DOLCE and IDEF (Sanfilippo et al , 2018). It particularizes the relationships (as shown in Table 1) and seizes the connection between development processes and their actors.…”

Section: The Presented Ontology For Product Life Cycle With Eco-desig...mentioning

confidence: 99%

A knowledge with ontology representation for product life cycle to support eco-design activities

Liang

2021

JEDT

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Purpose This study aims to develop a synthetic knowledge repository consisted of interrelated Web Ontology Language. Design/methodology/approach The ontology composes the main framework to categorize data of product life cycle with eco-design mode (PLC-EDM) and automatically infer specialists’ knowledge for data confirmation, eventually assisting the utilizations and generation of strategies toward decision-making Findings (i) utilization of a novel model with ontology mode for information reuse cross the different eco-design applications; (ii) generation of a sound platform toward life cycle evaluation; and (iii) implementation of the PLC-EDM model along the product generation process. Research limitations/implications It cannot substitute an evaluation tool of life cycle. Certainly, this model does not predict the “target and range” and/or the depiction of the “utility module” that are basic activities in life cycle assessments as characterized through the international organization for standardization regulations. Practical implications As portion of this framework, a prototype Web application is presented which is applied to produce, reuse and verify knowledge of product life cycle. Social implications By counting upon the ontology, the information conducted by the utilization is certainly semantically represented to promote the data sharing among various participants and tools. Besides, the data can be verified against possible faults by inferring over the ontology. Hence, a feasible way to a popular topic in the domain of eco-design applications extension in the industry. Originality/value The goals are: to lean on rigid modeling principles; and to promote the interoperability and diffusion of the ontology toward particular utilization demands.

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“…Sanfilippo et al [20], described a Manufacturing Resource as a physical object or amount of mater, which can be available/dedicated, agentive/non-agentive, or an input/output/mechanism. Järvenpää [1] defines a manufacturing resource as a device or factory unit as part of MaRCO ontology, with the goal of describing the capabilities and combined capabilities of manufacturing resources.…”

Section: 2mentioning

confidence: 99%

Initial Approach to an Industrial Resources Ontology in Aerospace Assembly Lines

Arista¹,

Mas²,

Vallellano³

2020

Product Lifecycle Management Enabling Smart X

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Industrial ontologies can support the Product Development Process (PDP) and the product and industrial system lifecycle, to have a seamless collaboration between actors. In this sense, an industrial resource ontology that supports an aerospace assembly line design process is key within the conceptual phase of the PDP. Industrial resources can have a different classification to support the design goal of the assembly line, in terms of process optimization, layout and space optimization, production time, costs, or even the assembly line capabilities definition. This work describes an initial approach to an industrial resources ontology, considering the notions that will describe these resources inside an assembly line design perimeter.

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Modeling Manufacturing Resources: An Ontological Approach

Cited by 12 publications

References 18 publications

Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges

Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges

A knowledge with ontology representation for product life cycle to support eco-design activities

Initial Approach to an Industrial Resources Ontology in Aerospace Assembly Lines

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