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ScienceDirect
Procedia Manufacturing 00 (2017) 000–000
www.elsevier.com/locate/procedia
* Paulo Afonso. Tel.: +351 253 510 761; fax: +351 253 604 741
E-mail address: psafonso@dps.uminho.pt
2351-9789 © 2017 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017.
Manufacturing Engineering Society International Conference 2017, MESIC 2017, 28-30 June
2017, Vigo (Pontevedra), Spain
Costing models for capacity optimization in Industry 4.0: Trade-off
between used capacity and operational efficiency
A. Santanaa, P. Afonsoa,*, A. Zaninb, R. Wernkeb
a University of Minho, 4800-058 Guimarães, Portugal
bUnochapecó, 89809-000 Chapecó, SC, Brazil
Abstract
Under the concept of "Industry 4.0", production processes will be pushed to be increasingly interconnected,
information based on a real time basis and, necessarily, much more efficient. In this context, capacity optimization
goes beyond the traditional aim of capacity maximization, contributing also for organization’s profitability and value.
Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of
maximization. The study of capacity optimization and costing models is an important research topic that deserves
contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical
model for capacity management based on different costing models (ABC and TDABC). A generic model has been
developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization’s
value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity
optimization might hide operational inefficiency.
© 2017 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference
2017.
Keywords: Cost Models; ABC; TDABC; Capacity Management; Idle Capacity; Operational Efficiency
1. Introduction
The cost of idle capacity is a fundamental information for companies and their management of extreme importance
in modern production systems. In general, it is defined as unused capacity or production potential and can be measured
in several ways: tons of production, available hours of manufacturing, etc. The management of the idle capacity
Procedia Manufacturing 28 (2019) 174–176
2351-9789 © 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the International Conference on Changeable, Agile, Reconfigurable
and Virtual Production.
10.1016/j.promfg.2018.12.028
10.1016/j.promfg.2018.12.028 2351-9789
© 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientic committee of the International Conference on Changeable, Agile, Recongurable
and Virtual Production.
Available online at www.sciencedirect.com
ScienceDirect
Procedia Manufacturing 00 (2019) 000000
www.elsevier.com/locate/procedia
2351-9789 © 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the International Conference on Changeable, Agile, Reconfigurable and Virtual
Production.
International Conference on Changeable, Agile, Reconfigurable and Virtual Production
Editorial: Formal Ontologies meet Industry
Emilio Sanfilippoa, Walter Terkajb
*
aLS2N, 1 rue de la Noë, Nantes
bIstituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato (STIIMA),Consiglio Nazionale delle Ricerche (CNR),
via A.Corti, 12 - 20133 Milano, Italy
The Formal Ontologies meet Industry (FOMI) workshop series is a scientific initiative supported by the
International Association for Ontology and its Applications (IAOA)
aimed at bringing together academics and
practitioners interested in ontologies for industry. FOMI addresses research and application topics concerni ng, e.g.,
the design of domain-specific ontologies, the development of ontology-based information systems, or the
investigation of the theoretical underpinnings of formal ontology when tuned to engineering applications.
As documented in the literature (see, e.g., [5][7]), some of the key motivations for using ontology in engineering
are:
To enable knowledge sharing across multiple human or software agents.
To tackle semantic interoperability among computer-based systems and data sources.
To capitalize experts’ knowledge using formal axioms that are accessible to machines.
To support knowledge and data visualization.
To maintain a cut-off distinction between (reusable) domain knowledge and application-dependent,
sometimes even proprietary, knowledge.
In short, from an engineering perspective, ontologies are interesting tools because they represent reference
knowledge models that can be shared by various systems and communities to guarantee a smooth interaction in
tasks related to data sharing or knowledge management.
The importance of ontologies in industry is demonstrated by European and national research programmes but
also by past and ongoing international initiatives such as the Ontology Based Engineering (OBE) Group [1], the
Industrial Ontologies Foundry (IOF)
, and the W3C Linked Building Data Community Group
§
. However, several
research priorities related to technological and methodological gaps between academic studies and industrial
practices remain to be addressed to fully exploit the potential of ontology engineering approaches and technologies.
* Corresponding author.
E-mail address: walter.terkaj@stiima.cnr.it
https://iaoa.org/
https://sites.google.com/view/industrialontologies
§ https://www.w3.org/community/lbd/
Available online at www.sciencedirect.com
ScienceDirect
Procedia Manufacturing 00 (2019) 000000
www.elsevier.com/locate/procedia
2351-9789 © 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the International Conference on Changeable, Agile, Reconfigurable and Virtual
Production.
International Conference on Changeable, Agile, Reconfigurable and Virtual Production
Editorial: Formal Ontologies meet Industry
Emilio Sanfilippoa, Walter Terkajb*
aLS2N, 1 rue de la Noë, Nantes
bIstituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato (STIIMA),Consiglio Nazionale delle Ricerche (CNR),
via A.Corti, 12 - 20133 Milano, Italy
The Formal Ontologies meet Industry (FOMI) workshop series is a scientific initiative supported by the
International Association for Ontology and its Applications (IAOA) aimed at bringing together academics and
practitioners interested in ontologies for industry. FOMI addresses research and application topics concerni ng, e.g.,
the design of domain-specific ontologies, the development of ontology-based information systems, or the
investigation of the theoretical underpinnings of formal ontology when tuned to engineering applications.
As documented in the literature (see, e.g., [5][7]), some of the key motivations for using ontology in engineering
are:
To enable knowledge sharing across multiple human or software agents.
To tackle semantic interoperability among computer-based systems and data sources.
To capitalize experts’ knowledge using formal axioms that are accessible to machines.
To support knowledge and data visualization.
To maintain a cut-off distinction between (reusable) domain knowledge and application-dependent,
sometimes even proprietary, knowledge.
In short, from an engineering perspective, ontologies are interesting tools because they represent reference
knowledge models that can be shared by various systems and communities to guarantee a smooth interaction in
tasks related to data sharing or knowledge management.
The importance of ontologies in industry is demonstrated by European and national research programmes but
also by past and ongoing international initiatives such as the Ontology Based Engineering (OBE) Group [1], the
Industrial Ontologies Foundry (IOF), and the W3C Linked Building Data Community Group§. However, several
research priorities related to technological and methodological gaps between academic studies and industrial
practices remain to be addressed to fully exploit the potential of ontology engineering approaches and technologies.
* Corresponding author.
E-mail address: walter.terkaj@stiima.cnr.it
https://iaoa.org/
https://sites.google.com/view/industrialontologies
§
https://www.w3.org/community/lbd/
2 Author name / Procedia Manufacturing 00 (2019) 000000
Examples of such priorities concern:
The development and (re-)use of standard and inter-connected ontologies, both upper- and domain-level
ontologies.
A library of modelling alternatives, e.g., in the form of ontology design patterns, tuned to engineering
representational needs. The purpose is to avoid the proliferation of modelling mistakes and, therefore, to
foster the development of well-defined ontologies.
The need of more efficient and effective software environments to support the various phases in the
lifecycle of an ontology-based software tool.
The development of high performing reasoners, as well as mature case studies concerning the use of
reasoning inferencing mechanisms to tackle real-world problems.
In order to foster the adoption of ontology-based technologies in industrial application contexts, it is also needed
the willingness of stakeholders to collaborate with universities and research centres with an open-minded aptitude.
Ontology development is challenging because it requires a collaborative interaction between ontology engineers,
domain experts, software engineers, database managers, and data analysts. If industrial practitioners are not willing
to share their knowledge, ideas, and problems, then researchers can hardly support any advancement in the
technologies and methodologies they adopt.
For the 10th edition, FOMI was hosted by the Changeable, Agile, Reconfigurable and Virtual Production
(CARV) conference. For this reason, the workshop was explicitly focused on ontologies for manufacturing,
including product design, process planning, manufacturing system design, layout planning, production planning, and
maintenance. The call for paper stressed the need of ontology-based applications for factories of the future [12]
adopting Industry 4.0 enabling technologies like digital factory, cloud computing, Internet of Things, Cyber-
Physical Systems, robotics, virtual and augmented reality, among others. Also, call explicitly asked companies
working with ontology-based systems to share their experiences in developing or using ontologies.
A total of six papers have been presented at FOMI 2018. In both the academic and industrial community, we
note an interest for foundational ontologies to clarify the semantic of the represented notions, and develop domain or
application ontologies on the basis of rigorous modelling principles. Three different foundational ontologies are
used in three papers, i.e., the Descriptive Ontology for Cognitive and Linguistic Engineering (DOLCE) [9] (see [2]),
the Unified Foundational Ontology (UFO) [6] (see [3]), and the Basic Formal Ontology (BFO) [1] (see [4]).
Benavent et al. [2] present a preliminary restructuring of the ontologies Product and Processes Development
Resource Capability (PPDRC) and Manufacturing and Inspection Resource Capability (MIRC) on the basis of
DOLCE. According to the authors, the restructuring is due to the need of spelling out clearly the basic modelling
assumptions behind both PPDRC and MIRC. An example is the distinction between roles and their bearers, which is
useful in manufacturing to model physical objects like drillers and the resource role they possibly play.
Cao et al. [3] present a core ontology, called CM-core, for condition monitoring. This work is contextualized
within the Industry 4.0 vision with the idea of using the ontology to facilitate the exchange of condition monitoring
data. Differently from existing works, CM-core is developed independently from specific application settings in
such a way to facilitate its reuse, hence to foster the interoperability of condition monitoring data and applications.
The ontology has been designed by relying on industrial standards and previously developed ontologies. Also, from
an upper-level perspective, it relies on UFO.
Mas et al. [8] discuss a preliminary ontology developed at Airbus for applications in the aerospace domain. The
presented work is framed within a model-based approach in design and manufacturing aimed at enabling the
homogeneous handling of data across multiple applications. The ontology is meant to be applied in a Product
Lifecycle Management (PLM) system to handle aerospace data through the entire product lifecycle.
Negri et al. [10] present an ontology-based approach for supporting the generation and integration of simulation
tools aimed at evaluating the performance of manufacturing systems via Discrete Event Simulation (DES). A
semantic data model is used to develop a digital twin of evolving manufacturing systems, thus enhancing the
exploitation of other digital technologies such as cloud computing and Cyber Physical Systems (CPS). The approach
is tested on lab-scale pilot plant dedicated to the assembly of mobile phones.
Schneider et al. [11] address how a domain ontology for Finite State Machine (FSM) can be exploited to
formalize knowledge about automation and control systems. Such formalization can help to support interoperability
for industrial applications, in particular in the scope of Industry 4.0. The paper shows how a domain ontology
enables a smooth conversion among different FSM formalisms while taking as a reference an industrial use case.
Emilio Sanlippo et al. / Procedia Manufacturing 28 (2019) 174176 175
www.elsevier.com/locate/procedia
2351-9789 © 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the International Conference on Changeable, Agile, Reconfigurable and Virtual
Production.
International Conference on Changeable, Agile, Reconfigurable and Virtual Production
Editorial: Formal Ontologies meet Industry
Emilio Sanfilippoa, Walter Terkajb*
aLS2N, 1 rue de la Noë, Nantes
bIstituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato (STIIMA),Consiglio Nazionale delle Ricerche (CNR),
via A.Corti, 12 - 20133 Milano, Italy
The Formal Ontologies meet Industry (FOMI) workshop series is a scientific initiative supported by the
International Association for Ontology and its Applications (IAOA) aimed at bringing together academics and
practitioners interested in ontologies for industry. FOMI addresses research and application topics concerni ng, e.g.,
the design of domain-specific ontologies, the development of ontology-based information systems, or the
investigation of the theoretical underpinnings of formal ontology when tuned to engineering applications.
As documented in the literature (see, e.g., [5][7]), some of the key motivations for using ontology in engineering
are:
To enable knowledge sharing across multiple human or software agents.
To tackle semantic interoperability among computer-based systems and data sources.
To capitalize experts’ knowledge using formal axioms that are accessible to machines.
To support knowledge and data visualization.
To maintain a cut-off distinction between (reusable) domain knowledge and application-dependent,
sometimes even proprietary, knowledge.
In short, from an engineering perspective, ontologies are interesting tools because they represent reference
knowledge models that can be shared by various systems and communities to guarantee a smooth interaction in
tasks related to data sharing or knowledge management.
The importance of ontologies in industry is demonstrated by European and national research programmes but
also by past and ongoing international initiatives such as the Ontology Based Engineering (OBE) Group [1], the
Industrial Ontologies Foundry (IOF), and the W3C Linked Building Data Community Group§. However, several
research priorities related to technological and methodological gaps between academic studies and industrial
practices remain to be addressed to fully exploit the potential of ontology engineering approaches and technologies.
* Corresponding author.
E-mail address: walter.terkaj@stiima.cnr.it
https://iaoa.org/
https://sites.google.com/view/industrialontologies
§ https://www.w3.org/community/lbd/
www.elsevier.com/locate/procedia
2351-9789 © 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the International Conference on Changeable, Agile, Reconfigurable and Virtual
Production.
International Conference on Changeable, Agile, Reconfigurable and Virtual Production
Editorial: Formal Ontologies meet Industry
Emilio Sanfilippoa, Walter Terkajb*
aLS2N, 1 rue de la Noë, Nantes
bIstituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato (STIIMA),Consiglio Nazionale delle Ricerche (CNR),
via A.Corti, 12 - 20133 Milano, Italy
The Formal Ontologies meet Industry (FOMI) workshop series is a scientific initiative supported by the
International Association for Ontology and its Applications (IAOA) aimed at bringing together academics and
practitioners interested in ontologies for industry. FOMI addresses research and application topics concerni ng, e.g.,
the design of domain-specific ontologies, the development of ontology-based information systems, or the
investigation of the theoretical underpinnings of formal ontology when tuned to engineering applications.
As documented in the literature (see, e.g., [5][7]), some of the key motivations for using ontology in engineering
are:
To enable knowledge sharing across multiple human or software agents.
To tackle semantic interoperability among computer-based systems and data sources.
To capitalize experts’ knowledge using formal axioms that are accessible to machines.
To support knowledge and data visualization.
To maintain a cut-off distinction between (reusable) domain knowledge and application-dependent,
sometimes even proprietary, knowledge.
In short, from an engineering perspective, ontologies are interesting tools because they represent reference
knowledge models that can be shared by various systems and communities to guarantee a smooth interaction in
tasks related to data sharing or knowledge management.
The importance of ontologies in industry is demonstrated by European and national research programmes but
also by past and ongoing international initiatives such as the Ontology Based Engineering (OBE) Group [1], the
Industrial Ontologies Foundry (IOF), and the W3C Linked Building Data Community Group§. However, several
research priorities related to technological and methodological gaps between academic studies and industrial
practices remain to be addressed to fully exploit the potential of ontology engineering approaches and technologies.
* Corresponding author.
E-mail address: walter.terkaj@stiima.cnr.it
https://iaoa.org/
https://sites.google.com/view/industrialontologies
§ https://www.w3.org/community/lbd/
2 Author name / Procedia Manufacturing 00 (2019) 000000
Examples of such priorities concern:
The development and (re-)use of standard and inter-connected ontologies, both upper- and domain-level
ontologies.
A library of modelling alternatives, e.g., in the form of ontology design patterns, tuned to engineering
representational needs. The purpose is to avoid the proliferation of modelling mistakes and, therefore, to
foster the development of well-defined ontologies.
The need of more efficient and effective software environments to support the various phases in the
lifecycle of an ontology-based software tool.
The development of high performing reasoners, as well as mature case studies concerning the use of
reasoning inferencing mechanisms to tackle real-world problems.
In order to foster the adoption of ontology-based technologies in industrial application contexts, it is also needed
the willingness of stakeholders to collaborate with universities and research centres with an open-minded aptitude.
Ontology development is challenging because it requires a collaborative interaction between ontology engineers,
domain experts, software engineers, database managers, and data analysts. If industrial practitioners are not willing
to share their knowledge, ideas, and problems, then researchers can hardly support any advancement in the
technologies and methodologies they adopt.
For the 10th edition, FOMI was hosted by the Changeable, Agile, Reconfigurable and Virtual Production
(CARV) conference. For this reason, the workshop was explicitly focused on ontologies for manufacturing,
including product design, process planning, manufacturing system design, layout planning, production planning, and
maintenance. The call for paper stressed the need of ontology-based applications for factories of the future [12]
adopting Industry 4.0 enabling technologies like digital factory, cloud computing, Internet of Things, Cyber-
Physical Systems, robotics, virtual and augmented reality, among others. Also, call explicitly asked companies
working with ontology-based systems to share their experiences in developing or using ontologies.
A total of six papers have been presented at FOMI 2018. In both the academic and industrial community, we
note an interest for foundational ontologies to clarify the semantic of the represented notions, and develop domain or
application ontologies on the basis of rigorous modelling principles. Three different foundational ontologies are
used in three papers, i.e., the Descriptive Ontology for Cognitive and Linguistic Engineering (DOLCE) [9] (see [2]),
the Unified Foundational Ontology (UFO) [6] (see [3]), and the Basic Formal Ontology (BFO) [1] (see [4]).
Benavent et al. [2] present a preliminary restructuring of the ontologies Product and Processes Development
Resource Capability (PPDRC) and Manufacturing and Inspection Resource Capability (MIRC) on the basis of
DOLCE. According to the authors, the restructuring is due to the need of spelling out clearly the basic modelling
assumptions behind both PPDRC and MIRC. An example is the distinction between roles and their bearers, which is
useful in manufacturing to model physical objects like drillers and the resource role they possibly play.
Cao et al. [3] present a core ontology, called CM-core, for condition monitoring. This work is contextualized
within the Industry 4.0 vision with the idea of using the ontology to facilitate the exchange of condition monitoring
data. Differently from existing works, CM-core is developed independently from specific application settings in
such a way to facilitate its reuse, hence to foster the interoperability of condition monitoring data and applications.
The ontology has been designed by relying on industrial standards and previously developed ontologies. Also, from
an upper-level perspective, it relies on UFO.
Mas et al. [8] discuss a preliminary ontology developed at Airbus for applications in the aerospace domain. The
presented work is framed within a model-based approach in design and manufacturing aimed at enabling the
homogeneous handling of data across multiple applications. The ontology is meant to be applied in a Product
Lifecycle Management (PLM) system to handle aerospace data through the entire product lifecycle.
Negri et al. [10] present an ontology-based approach for supporting the generation and integration of simulation
tools aimed at evaluating the performance of manufacturing systems via Discrete Event Simulation (DES). A
semantic data model is used to develop a digital twin of evolving manufacturing systems, thus enhancing the
exploitation of other digital technologies such as cloud computing and Cyber Physical Systems (CPS). The approach
is tested on lab-scale pilot plant dedicated to the assembly of mobile phones.
Schneider et al. [11] address how a domain ontology for Finite State Machine (FSM) can be exploited to
formalize knowledge about automation and control systems. Such formalization can help to support interoperability
for industrial applications, in particular in the scope of Industry 4.0. The paper shows how a domain ontology
enables a smooth conversion among different FSM formalisms while taking as a reference an industrial use case.
176 Emilio Sanlippo et al. / Procedia Manufacturing 28 (2019) 174176
Author name / Procedia Manufacturing 00 (2019) 000000 3
Cheong [4] proposes a method to validate JSON by exploiting state-of-the-art ontology-based tools. The need of
such validation stems from the ever growing use of JSON format to exchange among software applications in
Industry 4.0 scenarios, particularly in the case of cloud platforms. However, this requires a challenging conversion
from JSON schemas and data to the Web Ontology Language (OWL).
Finally, Table 1 presents the FOMI 2018 papers by mapping their contents against motivations to use formal
ontologies, addressed ontology research priorities, and specific FOMI 2018 topics.
Table 1: Mapping of the FOMI 2018 papers
Paper
Motivations for ontology use
Ontology research priority
FOMI @CARV Focus
Benavent et al. [2]
Formal representation of engineering
knowledge; alignment with foundational
ontology
Development and use of domain
ontologies
Product design, process
planning, production planning
Cao et al. [3]
Semantic interoperability; formal
representation of engineering
knowledge; alignment with foundational
ontology; Industry 4.0
Development and use of domain
ontologies
Maintenance of manufacturing
systems
Cheong [4]
Semantic interoperability; ontology-
based software development; alignment
with foundational ontology
Management of large amount of
data
Ontology integration with
Industry 4.0 technologies
Mas et al. [8]
Semantic interoperability; formal
representation of engineering
knowledge; ontology-based software
development
Development and use of domain
ontologies
Manufacturing system design;
manufacturing use case
Negri et al. [10]
Semantic interoperability; Industry 4.0
Development and use of ontology-
based applications
Ontology integration with
Industry 4.0 technologies;
manufacturing use case
Schneider et al. [11]
Semantic interoperability; separation of
domain knowledge from proprietary
systems
Use of domain ontologies
Ontology integration with
Industry 4.0 technologies;
manufacturing use case
References
[1] Arp R, Smith B, Spear AD (2015) Building ontologies with Basic Formal Ontology. MIT Press.
[2] Benavent S, Rosado P, Solano L, Guarino N, Sanfilippo EM (2019) How to Restructure PPDRC and MIRC According to DOLCE.
Proceedings of International Conference on Changeable, Agile, Reconfigurable and Virtual Production. Procedia Manufacturing
[3] Cao Q, Zanni-Merk C, Reich C (2019) Towards a Core Ontology for Condition Monitoring. Proceedings of International Conference on
Changeable, Agile, Reconfigurable and Virtual Production. Procedia Manufacturing
[4] Cheong H (2019) Translating JSON Schema logics into OWL axioms for unified data validation on a digital manufacturing platform.
Proceedings of International Conference on Changeable, Agile, Reconfigurable and Virtual Production. Procedia Manufacturing
[5] El Kadiri S, Terkaj W, Urwin EN, Palmer C, Kiritsis D, Young R (2015) Ontology in engineering applications. FOMI 2015 7th
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Springer Verlag.
[6] Guizzardi G, Wagner G, Almeida JPA, Guizzardi RS (2015). Towards ontological foundations for conceptual modeling: the unified
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[7] Gruninger M (2009) The ontological stance for a manufacturing scenario. Journal of Cases on Information Technology (JCIT), 11(4), 1-25.
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Production. Procedia Manufacturing
[9] Masolo C, Borgo S, Gangemi A, Guarino N, Oltramari A (2003) Ontology Library. WonderWeb Deliverable D18, ISTC-CNR technical
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[10] Negri E, Fumagalli L, Cimino C, Macchi M (2019) FMU-supported simulation for CPS Digital Twin. Proceedings of International
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[12] Tolio T, Copani G, Terkaj W (2019) Factories of the Future - The Italian Flagship Initiative. Springer International Publishing
© 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the International Conference on Changeable, Agile,
Reconfigurable and Virtual Production.
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This book presents results relevant in the manufacturing research field, that are mainly aimed at closing the gap between the academic investigation and the industrial application, in collaboration with manufacturing companies. Several hardware and software prototypes represent the key outcome of the scientific contributions that can be grouped into five main areas, representing different perspectives of the factory domain:1) Evolutionary and reconfigurable factories to cope with dynamic production contexts characterized by evolving demand and technologies, products and processes. 2) Factories for sustainable production, asking for energy efficiency, low environmental impact products and processes, new de-production logics, sustainable logistics. 3) Factories for the People who need new kinds of interactions between production processes, machines, and human beings to offer a more comfortable and stimulating working environment. 4) Factories for customized products that will be more and more tailored to the final user’s needs and sold at cost-effective prices. 5) High performance factories to yield the due production while minimizing the inefficiencies caused by failures, management problems, maintenance. This books is primarily targeted to academic researchers and industrial practitioners in the manufacturing domain.
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JSON (JavaScript Object Notation) is a prevalent data format used in cloud-based platforms that support composable digital manufacturing workflows. The current work presents a method to translate the logics found in JSON Schema into OWL axioms, in order to facilitate ontology-based unified data validation with JSON data. The specific contributions of this paper include the demonstration of using a formal ontology for the logic translation and data validation, a technique for disambiguating implicit relations found in JSON Schema as explicit OWL properties, and mapping JSON Schema validation keywords to equivalent OWL expressions.
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Manufacturing companies are experiencing the fourth industrial revolution characterised by the introduction of new technologies into production equipment, such as the Cyber Physical Systems and the Digital Twin simulations. Companies are then challenged by the digitization of products and production systems information, which leads to new potentials for digital continuity – i.e. information available and continuously updated for the decision makers – along the lifecycles. A semantic data model, that structures and stores physical and operational data from the field, can support the digital continuity to be used in production system simulations in a Digital Twin paradigm. This work proposes to model specific aspects and behaviours of the production system separately from the core simulation, in order to flexibly decide whether to activate the replica of the specific behaviours only when needed. The modules interact with the main simulation run through standard interfaces, allowing an easy reusability of the single modules also in different simulation environments.
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More and more information and communication technologies originating from the web are introduced in industrial automation systems. The vision for future automation systems includes intelligent self-descriptive components, which exchange information and potentially reason by themselves through knowledge-assisted methods. Formal domain descriptions are required to enable this vision, including knowledge related to mechanical, electrical and control domains. This work focuses on formalizing knowledge of the automation and control domain and investigates how knowledge-based methods can support the automated conversion between different formalisms for modelling discrete behaviour in manufacturing automation: finite state machines. We detail our approach by deploying the presented method in a use case related to the automation of a pick and place unit available from the literature.
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Condition monitoring is performed to identify the functioning state of a machine or a mechanical system. It is an important task by which the machine or mechanical system deterioration tendency and the location of a failure can be detected. In recent years, ontologies have shown promising results to enhance knowledge sharing in condition monitoring tasks, while offering a logically defined and controlled vocabulary of domain entities. Motivated by the growing demand for unification and formal representation of useful concepts in condition monitoring, in this paper we present CM-core, an ontology of core condition monitoring entities. It incorporates several ISO standards as sources and also extracts general concepts from a series of domain ontologies. The ontology contains taxonomies of core condition monitoring concepts such as system, function, behavior, structure, state, failure and fault, with their interrelationships. The CM-core ontology has a broader domain coverage than the existing ontologies, and its generality ensures further specification into more specific domain ontologies.
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This paper describes a long-term research program on developing ontological foundations for conceptual modeling. This program, organized around the theoretical background of the foundational ontology UFO (Unified Foundational Ontology), aims at developing theories, methodologies and engineering tools with the goal of advancing conceptual modeling as a theoretically sound discipline but also one that has concrete and measurable practical implications. The paper describes the historical context in which UFO was conceived, briefly discusses its stratified organization, and reports on a number of applications of this foundational ontology over more than a decade. In particular, it discusses the most successful application of UFO, namely, the development of the conceptual modeling language OntoUML. The paper also discusses a number of methodological and computational tools, which have been developed over the years to support the OntoUML community. Examples of these methodological tools include ontological patterns and anti-patterns; examples of these computational tools include automated support for pattern-based model construction, formal model verification, formal model validation via visual simulation, model verbalization, code generation and anti-pattern detection and rectification. In addition, the paper reports on a variety of applications in which the language as well as its associated tools have been employed to engineer models in several institutional contexts and domains. Finally, it reflects on some of these lessons learned by observing how OntoUML has been actually used in practice by its community and on how these have influenced both the evolution of the language as well as the advancement of some of the core ontological notions in UFO.
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The goal of this position paper is to introduce the research topics in engineering and the motivations for the application of semantic technologies. A group called Ontology Based Engineering – OBE has been created by the engineering community to share experiences in this field and the various challenges faced in using ontologies and related tools. The OBE groups aims at creating a dialog with the ontology specialists by sharing the research challenges and problems in ontology application.
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During the conceptual design phase of an aerospace assembly line, the Design Solutions that will meet the functional and industrial requirements are defined. As a result it is possible to define an early conceptual design of the assembly line and its basic parameters. Assembly Line Balancing (ALB) comprises ordering of tasks among stations to satisfy precedence constraints. Due to the specific features of an aircraft, such approach is not fully suitable for the case of an aerospace assembly line where the number of stations relates to technological criteria rather than to a calculation aiming to minimize a specific parameter. This paper presents a preliminary ontology definition for aerospace assembly lines in the Airbus context using Model for Manufacturing (MfM) methodology. The authors implement a new approach to characterize mono configured aerospace products. This new approach lets maximum flexibility in the shop floor implementing the adherence characteristic and is developed using a novel methodology, MfM. The new approach for modelling manufacturing systems has been coined as an extension of the previous authors’ research to introduce MBSE (Model Based Systems Engineering) concepts in manufacturing. A preliminary ontology based on MfM is developed and proposed to represent an aerospace assembly line using the innovative concept of adherence to assembly stations. The model scope is the configuration of the design solutions against manufacturing solutions, and the arrangement of them in a network of networks. The ontology definition is based on the DA08 artifact, used by authors in several researches, supporting the models currently in development and the next steps.
Conference Paper
Condition monitoring is performed to identify the functioning state of a machine or a mechanical system. It is an important task by which the machine or mechanical system deterioration tendency and the location of a failure can be detected. In recent years, ontologies have shown promising results to enhance knowledge sharing in condition monitoring tasks, while offering a logically defined and controlled vocabulary of domain entities. Motivated by the growing demand for unification and formal representation of useful concepts in condition monitoring, in this paper we present CM-core, an ontology of core condition monitoring entities. It incorporates several ISO standards as sources and also extracts general concepts from a series of domain ontologies. The ontology contains taxonomies of core condition monitoring concepts such as system, function, behavior, structure, state, failure and fault, with their interrelationships. The CM-core ontology has a broader domain coverage than the existing ontologies, and its generality ensures further specification into more specific domain ontologies.
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The semantic integration of software systems can be supported through a shared understanding of the terminology in their respective ontologies. In practice, however, the author is faced with the additional challenge that few applications have an explicitly axiomatized ontology. To address this challenge, we adopt the Ontological Stance, in which we can model a software application as if it were an inference system with an axiomatized ontology, and use this ontology to predict the set of sentences that the inference system determines to be entailed or satisfiable. This chapter gives an overview of a deployment of the Process Specification Language (PSL) Ontology as the interchange ontology for the semantic integration of three manufacturing software applications currently being used in industry-a process modeller, a process planner, and a scheduler.