Industrial ontologies for interoperability in agile and resilient manufacturing

Ameri, Farhad; Šormaz, Dušan; Psarommatis, Foivos; Kiritsis, Dimitris

doi:10.1080/00207543.2021.1987553

Cited by 54 publications

(15 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To the best of authors' knowledge, AMODO is the only methodology for ontology modelling specifically referring to the maintenance field. Furthermore, it is consistent with advanced general methodologies, namely NeOn [37] and DOGMA [38], and it integrates most of IOF (Industrial Ontologies Foundry) guidelines [39]; finally, AMODO provides a compendium of ontologies in the field of maintenance and asset management, which is useful not to start from scratch. The methodology entails four main phases, which are hereinafter summarised (please refer to the original work for more information):…”

Section: Ontology Development Methodology and Design Choicesmentioning

confidence: 80%

An ontological modelling of multi-attribute criticality analysis to guide Prognostics and Health Management program development

Polenghi

Roda

Macchi

et al. 2022

Auton. Intell. Syst.

View full text Add to dashboard Cite

Digital technologies are becoming more pervasive and industrial companies are exploiting them to enhance the potentialities related to Prognostics and Health Management (PHM). Indeed, PHM allows to evaluate the health state of the physical assets as well as to predict their future behaviour. To be effective in developing PHM programs, the most critical assets should be identified so to direct modelling efforts. Several techniques could be adopted to evaluate asset criticality; in industrial practice, criticality analysis is amongst the most utilised. Despite the advancement of artificial intelligence for data analysis and predictions, the criticality analysis, which is built upon both quantitative and qualitative data, has not been improved accordingly. It is the goal of this work to propose an ontological formalisation of a multi-attribute criticality analysis in order to i) fix the semantics behind the terms involved in the analysis, ii) standardize and uniform the way criticality analysis is performed, and iii) take advantage of the reasoning capabilities to automatically evaluate asset criticality and associate a suitable maintenance strategy. The developed ontology, called MOCA, is tested in a food company featuring a global footprint. The application shows that MOCA can accomplish the prefixed goals; specifically, high priority assets towards which direct PHM programs are identified. In the long run, ontologies could serve as a unique knowledge base that integrate multiple data and information across facilities in a consistent way. As such, they will enable advanced analytics to take place, allowing to move towards cognitive Cyber Physical Systems that enhance business performance for companies spread worldwide.

show abstract

Section: Ontology Development Methodology and Design Choicesmentioning

confidence: 80%

An ontological modelling of multi-attribute criticality analysis to guide Prognostics and Health Management program development

Polenghi

Roda

Macchi

et al. 2022

Auton. Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…Psarommatis (2021) proposed a methodology that uses a digital twin to properly design a manufacturing system for using the four ZDM strategies to achieve ZDM. Semantic modelling and data and information models play key roles in determining the efficiency and capabilities of data driven technologies for ZDM (Ameri et al (2022); Grevenitis et al (2019)). This paper describes a fabric for building such information models.…”

Section: Figurementioning

confidence: 99%

A semantic systems engineering framework for zero-defect engineering and operations in the continuous process industries

Cameron

Waaler

Fjøsna³

et al. 2022

Front. Manuf. Technol.

Self Cite

View full text Add to dashboard Cite

The on-going twin transition demands that the continuous process industry builds and operates their facilities in a more sustainable way. This change affects the entire supply-chain. The market demands new ways of engineering, procuring and constructing plants that assure quality at each step of the process. Petroleum and petrochemical producers must reduce their waste and environmental footprint and find ways of migrating to sustainable production. There is zero tolerance for waste, emissions or process malfunctions. Engineering contractors need to transfer their skills to new processes and produce series, non-custom facilities for new applications like offshore wind energy, modular production and industrial symbiosis. This is leading to a convergence in methods with discrete manufacturing, especially the automotive industries. In this climate, this sector can benefit from applying Zero-defect Manufacturing (ZDM) to both engineering design and operations. This work defines a framework for implementing ZDM in the process industry supply chain. The framework brings together modelling techniques and models from the following disciplines: system engineering, computer-aided process engineering, automation (especially Industry 4.0) and semantic technologies. These contributions are synthesised into an information fabric that allows engineering firms to work in new ways. Operators and contractors can use the fabric to move from document-driven engineering to data-based processes. The fabric captures requirements and intent in design so that facilities can be delivered and started-up and operated with zero defects in the design and construction. The information is also a vital support for safe and efficient operations and maintenance. We call this zero-defect O&M. The framework combines a systems engineering break-down of facilities, based on ISO/IEC81346, with implementation in SysML, with semantic interoperability frameworks from the process industries (ISO15926). We build upon and synthesise the results of recent standardization initiatives from the industry, notably CFIHOS, DEXPI and READI. We draw on results from process systems engineering, the OntoCAPE ontology and the CAPE-OPEN standards. The framework is illustrated by application to a non-proprietary process system, namely the Tennessee-Eastman process. This example is used to show the modelling approach and indicate how the fabric supports zero-defect practices.

show abstract

“…Therefore, demand is driven by quality, quantity and variety of products and services. Furthermore, as it became obvious in the last couple of years, modern manufacturing, and production systems, although they are capable of coping with the volatile customer demand, they suffer from resiliency (Ameri et al, 2022). In other words, taking into consideration of the analogue of the spring/damper system, manufacturing systems can be realized as the spring/damper couple, and the external excitations are the system disturbances (Makkonen, 1999).…”

Section: Introductionmentioning

confidence: 99%

Advances in Adaptive Scheduling in Industry 4.0

Mourtzis

2022

Front. Manuf. Technol.

View full text Add to dashboard Cite

The shift of traditional mass-producing industries towards mass customisation practices is nowadays evident. However, if not implemented properly, mass customisation can lead to disturbances in material flow and severe reduction in productivity. Moreover, manufacturing enterprises often face the challenge of manufacturing highly customized products in small lot sizes. One solution to adapt to the ever-changing demands, which increases resource flexibility, lies in the digitization of the manufacturing systems. Furthermore, the distributed manufacturing environment and the ever-increasing product variety and complexity result in reduced time-to market, ubiquitous data access and sharing and adaptability and responsiveness to changes. These requirements can be achieved through smart manufacturing tools and especially Wireless Sensor Networks (WSN). Thus, the aim of this position paper is to summarize the design and development of solutions based on cutting-edge technologies such as Cloud Computing, Artificial Intelligence (AI), Internet of Things (IoT), Simulation, 5G, and so on. Concretely, the first part discusses the development of a Cloud-based production planning and control system for discrete manufacturing environments. The proposed approach takes into consideration capacity constraints, lot sizing and priority control in a “bucket-less” manufacturing environment. Then, an open and interoperable Internet of Things platform is discussed, which is enhanced by innovative tools and methods that transform them into Cyber-Physical Systems (CPS), supporting smart customized shopping, through gathering customers’ requirements, adaptive production, and logistics of vending machines replenishment and Internet of Things and Wireless Sensor Networks for Smart Manufacturing. To that end, all the proposed methodologies are validated using data derived from Computer Numerical Control (CNC) machine building industry, from European Metal-cutting and mold-making SMEs, from white goods industry and SMEs that produces solar panels.

show abstract

Industrial ontologies for interoperability in agile and resilient manufacturing

Cited by 54 publications

References 50 publications

An ontological modelling of multi-attribute criticality analysis to guide Prognostics and Health Management program development

An ontological modelling of multi-attribute criticality analysis to guide Prognostics and Health Management program development

A semantic systems engineering framework for zero-defect engineering and operations in the continuous process industries

Advances in Adaptive Scheduling in Industry 4.0

Contact Info

Product

Resources

About