Dimitris Kiritsis

Dimitris Kiritsis

École Polytechnique Fédérale de Lausanne

H-index: 46

Europe-Switzerland

About Dimitris Kiritsis

Dimitris Kiritsis, With an exceptional h-index of 46 and a recent h-index of 32 (since 2020), a distinguished researcher at École Polytechnique Fédérale de Lausanne, specializes in the field of Circular Manufacturing, Closed Loop Lifecycle Management, Applied Semantics, Ontology Based Engineering, Industrial Ontologies.

His recent articles reflect a diverse array of research interests and contributions to the field:

Ecosystem integration: the use of ontologies in integrating knowledge across manufacturing value networks

A semantic model-based systems engineering approach for assessing the operational performance of metal forming process

A semantic-driven tradespace framework to accelerate aircraft manufacturing system design

Design ontology for cognitive thread supporting traceability management in model-based systems engineering

An aircraft assembly process formalism and verification method based on semantic modeling and MBSE

Exploring the effectiveness of using internal CNC system signals for chatter detection in milling process

Zero defect manufacturing in the era of industry 4.0 for achieving sustainable and resilient manufacturing

Horizons in manufacturing technology

Dimitris Kiritsis Information

University

École Polytechnique Fédérale de Lausanne

Position

___

Citations(all)

8176

Citations(since 2020)

4420

Cited By

4937

hIndex(all)

46

hIndex(since 2020)

32

i10Index(all)

138

i10Index(since 2020)

94

Email

University Profile Page

École Polytechnique Fédérale de Lausanne

Dimitris Kiritsis Skills & Research Interests

Circular Manufacturing

Closed Loop Lifecycle Management

Applied Semantics

Ontology Based Engineering

Industrial Ontologies

Top articles of Dimitris Kiritsis

Ecosystem integration: the use of ontologies in integrating knowledge across manufacturing value networks

Authors

Michela Magas,Dimitris Kiritsis,María Poveda-Villalón,Lan Yang,Sten-Erik Björling,Andreas Rudenå

Journal

Frontiers in Manufacturing Technology

Published Date

2024/3/21

One of the greatest challenges in creating effective decision-making systems for connected enterprises is the management of cross-domain information. In manufacturing value networks where supply chains are increasingly intertwined, and closed-loop lifecycle management requires traversing several domains, ontologies are proving to be a reliable reference for cross-domain semantic interoperability. However, ontology development, implementation, and management are fragmented and difficult for new users of ontologies to grasp. This is a significant challenge in environments where ontologies are vital for managing effective data exchanges in complex industrial processes. The OntoCommons project has evolved an ontology ecosystem that aims to lower the entry barrier to using ontologies. Building on this ambition, we present a holistic approach to the integration and management of ontologies horizontally across manufacturing ecosystems, including the creation of reference documentation for manufacturing value networks and related standards, available tools for working with ontologies, and examples of vertical integration of knowledge from application level with domain-level and top-level ontology reference documentation. As a novel research direction, we propose a meta-level approach to ontology-driven knowledge management in manufacturing ecosystems. Based on evidence from recent breakthroughs, we present future and emerging research directions.

A semantic model-based systems engineering approach for assessing the operational performance of metal forming process

Authors

Jinzhi Lu,George Tsinarakis,Nikolaos Sarantinoudis,George Arampatzis,Xiaochen Zheng,Dimitris Kiritsis

Journal

Computers & Industrial Engineering

Published Date

2024/3/4

Metal Forming is a basic and essential industrial process to provide materials for constructing complex products. To design an efficient metal forming process, the functional requirements and operational performance are two important aspects to be considered. In this paper, a semantic Model-based Systems Engineering (sMBSE) approach is proposed to support the design of the entire metal forming process. A multi-architecture modeling language KARMA is used to develop meta-models and architecture models of the metal forming process from the perspectives of mission, operation, function, logic flow and physical structure. Enabled by customized software, the KARMA models are transformed to ontology models, which are then converted to Petri Net (PN) models. Simulations are conducted based on the PN models to evaluate the operational performance of the created processes. A case study based on a real …

A semantic-driven tradespace framework to accelerate aircraft manufacturing system design

Authors

Xiaochen Zheng,Xiaodu Hu,Rebeca Arista,Jinzhi Lu,Jyri Sorvari,Joachim Lentes,Fernando Ubis,Dimitris Kiritsis

Journal

Journal of Intelligent Manufacturing

Published Date

2024/1

During the design phase of an aircraft manufacturing system, different industrial scenarios need to be evaluated according to key performance indicators to achieve the optimal system performance. It is a highly complex process involving multidisciplinary stakeholders, various digital tools and protocols. To address the digital discontinuity challenge during this process, this paper proposes a tradespace framework based on semantic technology and model-based systems engineering. It aims at functionality integration of requirement management, architecture definition, manufacturing system design, solution verification and visualization. An application ontology is developed to integrate assembly system domain knowledge, industrial requirements and system architecture model information. The proposed framework is implemented in a case study to support the fuselage orbital joint process design, which is part of the …

Design ontology for cognitive thread supporting traceability management in model-based systems engineering

Authors

Shouxuan Wu,Guoxin Wang,Jinzhi Lu,Zhenchao Hu,Yan Yan,Dimitris Kiritsis

Journal

Journal of Industrial Information Integration

Published Date

2024/4/30

Industrial information integration engineering (IIIE) is an interdisciplinary field to facilitate the industrial information integration process. In the age of complex and large-scale systems, model-based systems engineering (MBSE) is widely adopted in industry to support IIIE. Traceability management is considered the foundation of information management in MBSE. However, a lack of integration between stakeholders, development processes, and models can decrease the effectiveness and efficiency of the system development. A modified MBSE toolchain prototype has been developed to implement traceability management; however, a lack of formal and structured specifications makes it difficult to describe the complex topology in traceability management scenarios using this MBSE toolchain, such as creating traceability between heterogeneous models, which leads to poor reusability of this MBSE toolchain in other …

An aircraft assembly process formalism and verification method based on semantic modeling and MBSE

Authors

Xiaochen Zheng,Xiaodu Hu,Jinzhi Lu,Rebeca Arista,Joachim Lentes,Dimitris Kiritsis

Journal

Advanced Engineering Informatics

Published Date

2024/4/1

The aircraft assembly system is highly complex involving different stakeholders from multiple domains. The design of such a system requires comprehensive consideration of various industrial scenarios aiming to optimize key performance indicators. Traditional design methods heavily rely on domain expert knowledge using documents to define assembly solutions which are later verified through simulations. However, these document-centric approaches cannot provide graphical notations for engineers to efficiently understand the entire assembly process. Moreover, it is difficult to analyze the performance of the designed assembly processes using simulations since the simulation models have to be developed based on the documents manually rather than be generated automatically from the design models. In this paper, a semantic-driven approach is proposed to support aircraft assembly process formalism and …

Exploring the effectiveness of using internal CNC system signals for chatter detection in milling process

Authors

Xiaochen Zheng,Pedro Arrazola,Roberto Perez,Daniel Echebarria,Dimitris Kiritsis,Patxi Aristimuño,Mikel Sáez-de-Buruaga

Journal

Mechanical Systems and Signal Processing

Published Date

2023/2/15

Chatter is a harmful self-excited vibration that commonly occurs during milling processes. Data-driven chatter detection and prediction is critical to achieve high surface quality and process efficiency. Most existing chatter detection approaches are based on external sensors, such as accelerometers and microphones, which require installation of extra devices. Some recent studies have proved the feasibility of online chatter detection using internal signals such as drive motor current. This study aims to investigate the effectiveness of different internal signals extracted from CNC system for chatter detection and compare them with external acceleration signals. The external and internal signals are first compared with time–frequency analysis using Discrete Fourier Transform and Ensemble Empirical Mode Decomposition approaches. Two chatter detection methods are then presented based on manually and …

Zero defect manufacturing in the era of industry 4.0 for achieving sustainable and resilient manufacturing

Authors

Foivos Psarommatis,Francisco Fraile,Joao Pedro Mendonca,Olga Meyer,Oscar Lazaro,Dimitris Kiritsis

Journal

Frontiers in Manufacturing Technology

Published Date

2023/1/13

The manufacturing sector has experienced steady growth and change in recent years as businesses have been able to fulfill increased client expectations, mostly as a result of the innovations and technologies brought from the Industry-4.0 era (Powell et al., 2022). Additionally, new guidelines for the economic success and sustainable production are imposed due to the shift to digital and sustainable manufacturing as well as the rising desire for mass customisation. Therefore, efficient “quality” and “waste” management at both the product and process levels have become crucial to industry competitiveness (Psarommatis et al., 2020a).To ensure that every consumer is happy, businesses pay close attention to product quality. Lean Manufacturing (LM), Six Sigma (SS), Theory of Constraints (TOC), Total Quality Management (TQM), and Lean Six Sigma (L6S) are examples of traditional quality improvement (QI) methods. These QI methods aim to improve product quality without learning from defects as they simply trace and remove them (Psarommatis et al., 2020b). Additionally, they do not fully utilize modern and cutting-edge data-driven technologies, imposed by Industry 4.0 concept. Finally, the key component of those methods does not consider prediction or its consequences. To accomplish a successful digital and green transition, these strategies can only offer a limited response and assistance to the new scenarios that must be addressed.

Horizons in manufacturing technology

Authors

Dimitris Kiritsis

Published Date

2023/10/11

During the last few years we are observing a continuous progress of Manufacturing driven by new emerging technological innovations which allow to reinforce sustainable and resilient growth. In order for this progress of the manufacturing industry to be sustained and reach more end-users, create new jobs and align with the Circular Economy paradigm, the following challenges need to be addressed in order to cover the needs for Sustainable & Resilient Value Chains:

Governance framework for autonomous and cognitive digital twins in agile supply chains

Authors

Kostas Kalaboukas,Dimitris Kiritsis,George Arampatzis

Journal

Computers in Industry

Published Date

2023/4/1

Cognitive Digital twins (CDT) are Digital Twins with cognition capabilities and ensure a proper monitoring and optimization of a specific asset’s behaviour. In the context of supply chains, there are different entities involved from assets, processes, up to organizations, which can be modelled as CDTs that collaborate each other, share and process information, learn and optimize behaviour to improve supply chain sustainability. This paper emphasises on the concept of supply chain CDT and proposes a holistic governance approach integrating three different views: i) business and sustainability; ii) data governance and ii) cognition (AI) models governance. A detailed description of the main questions/ issues for each of the three views is provided along with an example of how it can be modelled in a case of a product with different circular strategies of its critical components.

Model-based system engineering supporting production scheduling based on satisfiability modulo theory

Authors

Jingqi Chen,Guoxin Wang,Jinzhi Lu,Xiaochen Zheng,Dimitris Kiritsis

Journal

Journal of Industrial Information Integration

Published Date

2022/5/1

Production scheduling enables a production system to allocate resources for the efficient and low-cost production. However, pure mathematical methods are typically utilized for production scheduling, which are not understandable and practical for different domain engineers. Moreover, heterogeneous information during production scheduling may result in communication ambiguity or an information delay. Furthermore, the interaction and reuse of production data is limited, owing to the lack of a unified expression of the production information. To overcome these challenges, a model-based systems engineering (MBSE) approach based on the satisfiability modulo theory (SMT) is proposed to support production scheduling. A multiple architectural view modeling language, KARMA, is used as the basis to construct production scheduling elements and formalize the production scheduling processes using architecture …

A semantic model in the context of maintenance: a predictive maintenance case study

Authors

Gokan May,Sangje Cho,AmirHossein Majidirad,Dimitris Kiritsis

Journal

Applied Sciences

Published Date

2022/6/15

Advanced technologies in modern industry collect massive volumes of data from a plethora of sources, such as processes, machines, components, and documents. This also applies to predictive maintenance. To provide access to these data in a standard and structured way, researchers and practitioners need to design and develop a semantic model of maintenance entities to build a reference ontology for maintenance. To date, there have been numerous studies combining the domain of predictive maintenance and ontology engineering. However, such earlier works, which focused on semantic interoperability to exchange data with standardized meanings, did not fully leverage the opportunities provided by data federation to elaborate these semantic technologies further. Therefore, in this paper, we fill this research gap by addressing interoperability in smart manufacturing and the issue of federating different data formats effectively by using semantic technologies in the context of maintenance. Furthermore, we introduce a semantic model in the form of an ontology for mapping relevant data. The proposed solution is validated and verified using an industrial implementation.

Systematic literature review of MBSE tool-chains

Authors

Junda Ma,Guoxin Wang,Jinzhi Lu,Hans Vangheluwe,Dimitris Kiritsis,Yan Yan

Published Date

2022/3/28

Currently, the fundamental tenets of systems engineering are supported by a model-based approach to minimize risks and avoid design changes in late development stages. The models are used to formalize, analyze, design, optimize, and verify system development and artifacts, helping developers integrate engineering development across domains. Although model-based development is well established in specific domains, such as software, mechanical systems, and electrical systems, its role in integrated development from a system perspective is still a challenge for industry. The model-based systems engineering (MBSE) tool-chain is an emerging technique in the area of systems engineering and is expected to become a next-generation approach for supporting model integration across domains. This article presents a literature review to highlight the usage and state of the art to generally specify the current understanding of MBSE tool-chain concepts. Moreover, the results are used for identifying the usage, advantages, barriers, concerns, and trends of tool-chain development from an MBSE perspective.

? Correspondence to: E-mail address:(G. May). 1 Address:. 2

Authors

Gokan May,Sangje Cho,Ana Teresa Correia,Rebecca Siafaka,Dragan Stokic,Dimitris Kiritsis

Journal

COMPUTERS IN INDUSTRY

Published Date

2022/11/1

Recently, products and services have become joint commodities, in contrast to the traditional focus on products alone. Thus, modern industrial companies need to manage product-service systems (PSSs) as part of a competitive strategy. PSSs are comprised of complex, related products and services; stakeholders; processes; and environments. It is therefore important to identify, integrate, and manage information from different data-sources. This paper introduces the critical issues facing PSSs and provides reference terminology for PSSs in the form of an ontology. This ontology represents a generic PSS that not only adds value to the PSS design but also offers the potential to improve individual products and services in the system. Thus, in this paper, we aim to facilitate communication between stakeholders from different domains and integrate heterogeneous data from various data-sources during the PSS lifecycle …

Code generation approach supporting complex system modeling based on graph pattern matching

Authors

Jie Ding,Jinzhi Lu,Guoxin Wang,Junda Ma,Dimitris Kiritsis,Yan Yan

Journal

IFAC-PapersOnLine

Published Date

2022/1/1

Code generation is an effective way to drive the complex system development in model-based systems engineering. Currently, different code generators are developed for different modeling languages to deal with the development of systems with multi-domain. There are a lack of unified code generation approaches for multi-domain heterogeneous models. In addition, existing methods lack the ability to flexibly query and transform complex model structures to the target code, resulting in poor transformation efficiency. To solve the above problems, this paper proposes a unified approach which supports the code generation of heterogeneous models with complex model structure. First, The KARMA language based on GOPPRR-E meta-modeling approach is used for the unified formalism of models built by different modeling languages. Second, the code generation approach based on graph pattern matching is …

Cost-based decision support system: a dynamic cost estimation of key performance indicators in manufacturing

Authors

Foivos Psarommatis,Morad Danishvar,Alireza Mousavi,Dimitris Kiritsis

Journal

IEEE Transactions on Engineering Management

Published Date

2022/1/7

An attempt is made to translate five generic key performance indicators (KPIs) into a continuous real-time cost function in a batch order-based manufacturing environment. The challenge of controlling and optimizing resource utilization, production efficiency, product-process quality, environmental impact, and inventory was specified by microelectronics and hard metal composite manufacturers. The motivation is to facilitate decision-making by converting operations management data into dynamic financial cost models. The process of interpreting engineering data of the physical level and operations management level into financial metrics creates a common language between engineers, managers, and financial departments of the company whose common objective is the profitability of the company, each with their own priorities. The proposed method provides a realistic representation of the performance of the …

A data-knowledge hybrid driven method for gas turbine gas path diagnosis

Authors

Jinwei Chen,Zhenchao Hu,Jinzhi Lu,Xiaochen Zheng,Huisheng Zhang,Dimitris Kiritsis

Journal

Applied Sciences

Published Date

2022/6/11

Gas path fault diagnosis of a gas turbine is a complex task involving field data analysis and knowledge-based reasoning. In this paper, a data-knowledge hybrid driven method for gas path fault diagnosis is proposed by integrating a physical model-based gas path analysis (GPA) method with a fault diagnosis ontology model. Firstly, a physical model-based GPA method is used to extract the fault features from the field data. Secondly, a virtual distance mapping algorithm is developed to map the GPA result to a specific fault feature criteria individual described in the ontology model. Finally, a fault diagnosis ontology model is built to support the automatic reasoning of the maintenance strategy from the mapped fault feature criteria individual. To enhance the ability of selecting a proper maintenance strategy, the ontology model represents more abundant knowledge from several sources, such as fault criteria analysis, physical structure analysis, FMECA (failure mode, effects, and criticality analysis), and the maintenance logic decision tool. The availability of the proposed hybrid driven method is verified by the field fault data from a real GE LM2500 PLUS gas turbine unit. The results indicate that the hybrid driven method is effective in detecting the path fault in advance. Furthermore, diversified fault information, such as fault effects, fault criticality, fault consequence, and fault detectability, could be provided to support selecting a proper maintenance strategy. It is proven that the data-knowledge hybrid driven method can improve the capability of the gas path fault detection, fault analysis, and maintenance strategy selection.

Toward a reference terminology for product-service systems in the manufacturing domain

Authors

Gökan May,Sangje Cho,Ana Teresa Correia,Rebecca Siafaka,Dragan Stokic,Dimitris Kiritsis

Journal

Computers in Industry

Published Date

2022/11/1

Recently, products and services have become joint commodities, in contrast to the traditional focus on products alone. Thus, modern industrial companies need to manage product-service systems (PSSs) as part of a competitive strategy. PSSs are comprised of complex, related products and services; stakeholders; processes; and environments. It is therefore important to identify, integrate, and manage information from different data-sources. This paper introduces the critical issues facing PSSs and provides reference terminology for PSSs in the form of an ontology. This ontology represents a generic PSS that not only adds value to the PSS design but also offers the potential to improve individual products and services in the system. Thus, in this paper, we aim to facilitate communication between stakeholders from different domains and integrate heterogeneous data from various data-sources during the PSS lifecycle …

A hybrid Decision Support System for automating decision making in the event of defects in the era of Zero Defect Manufacturing

Authors

Foivos Psarommatis,Dimitris Kiritsis

Journal

Journal of Industrial Information Integration

Published Date

2022/3/1

Defects are unavoidable during manufacturing processes, and a tremendous amount of research aimed at improving defect prevention has been conducted by scholars. Zero Defect Manufacturing (ZDM) seeks to eliminate defects in production. In addition, technological advancements now allow the repair of defective products. This creates the need to re-schedule productions more frequently in order to take into account the actions necessary to fix defective parts. This study focuses on detection and repair-based ZDM strategies. It implements a newly developed, hybrid Decision Support System (DSS) that uses data-driven and knowledge-based approaches to detect defects and then automate the necessary decision-making processes. The system uses an ontology based on the MASON ontology in order to describe the production domain and enrich the available data with contextual information. Real time …

Model-based engineering and semantic interoperability for trusted digital twins big data connection across the product lifecycle

Authors

Oscar Lázaro,Jesús Alonso,Roxana-Maria Holom,Katharina Rafetseder,Stefanie Kritzinger,Fernando Ubis,Gerald Fritz,Alois Wiesinger,Harald Sehrschön,Jimmy Nguyen,Tomasz Luniewski,Wojciech Zietak,Jerome Clavel,Roberto Perez,Marlene Hildebrand,Dimitris Kiritsis,Hugues-Arthur Garious,Silvia de la Maza,Antonio Ventura-Traveset,Juanjo Hierro,Gernot Boege,Ulrich Ahle

Published Date

2022/4/29

With the rising complexity of modern products and a trend from single products to Systems of Systems (SoS) where the produced system consists of multiple subsystems and the integration of multiple domains is a mandatory step, new approaches for development are demanded. This chapter explores how Model-Based Systems Engineering (MBSE) can benefit from big data technologies to implement smarter engineering processes. The chapter presents the Boost 4.0 Testbed that demonstrates how digital twin continuity and digital thread can be realized from service engineering, production, product performance, to behavior monitoring. The Boost 4.0 testbed demonstrates the technical feasibility of an interconnected operation of digital twin design, ZDM subtractive manufacturing, IoT product monitoring, and spare part 3D printing services. It shows how the IDSA reference model for data sovereignty, blockchain …

Analysis of plastic waste circularity through LCA

Authors

Andres Font,Oksana Horodytska,Dimitris Kiritsis

Published Date

2022

Upcycling processes are better aligned with the Circular Economy model, which defends that the plastic waste is a valuable resource with the potential to be recirculated in a new material cycle. To ensure the highest number of cycles, products, components and material should be kept at their highest utility and value (Webster, 2017). However, this is not what is happening in the recycling sector because upcycling processes are more complex, and energy and resource-intensive. As a result, the environmental benefits of plastic upcycling are frequently called into question and downcycling methods are implemented owing to their lower complexity and costs, regardless of the irreversible and meaningful loss of quality. In this work, three plastic waste management scenarios have been assessed to determine their potential to contribute to the implementation of the Circular Economy. The chosen waste treatment methods are upcycling of plastic scrap through deinking technology, downcycling by re-extrusion and, finally, incineration. The environmental impacts have been computed through LCA methodology. The results show that depending on the assumptions made, LCA can lead to conclusions which are opposite to the Circular Economy principles, thus favouring the downcycling and incineration of plastic waste with high potential to be recirculated. Therefore, to make a fairer comparison between upcycling and other waste treatment options, two modifications have been suggested. First, the target market for recycled pellets should be included in the computation since it is reliant on the materials quality. Downcycled dark pellets can be used in …

See List of Professors in Dimitris Kiritsis University(École Polytechnique Fédérale de Lausanne)

Dimitris Kiritsis FAQs

What is Dimitris Kiritsis's h-index at École Polytechnique Fédérale de Lausanne?

The h-index of Dimitris Kiritsis has been 32 since 2020 and 46 in total.

What are Dimitris Kiritsis's top articles?

The articles with the titles of

Ecosystem integration: the use of ontologies in integrating knowledge across manufacturing value networks

A semantic model-based systems engineering approach for assessing the operational performance of metal forming process

A semantic-driven tradespace framework to accelerate aircraft manufacturing system design

Design ontology for cognitive thread supporting traceability management in model-based systems engineering

An aircraft assembly process formalism and verification method based on semantic modeling and MBSE

Exploring the effectiveness of using internal CNC system signals for chatter detection in milling process

Zero defect manufacturing in the era of industry 4.0 for achieving sustainable and resilient manufacturing

Horizons in manufacturing technology

...

are the top articles of Dimitris Kiritsis at École Polytechnique Fédérale de Lausanne.

What are Dimitris Kiritsis's research interests?

The research interests of Dimitris Kiritsis are: Circular Manufacturing, Closed Loop Lifecycle Management, Applied Semantics, Ontology Based Engineering, Industrial Ontologies

What is Dimitris Kiritsis's total number of citations?

Dimitris Kiritsis has 8,176 citations in total.

What are the co-authors of Dimitris Kiritsis?

The co-authors of Dimitris Kiritsis are Sergio Terzi, jim browne, Klaus-Dieter Thoben, Marco Taisch, Benoît Eynard, Marco Garetti.

    Co-Authors

    H-index: 40
    Sergio Terzi

    Sergio Terzi

    Politecnico di Milano

    H-index: 40
    jim browne

    jim browne

    National University of Ireland, Galway

    H-index: 40
    Klaus-Dieter Thoben

    Klaus-Dieter Thoben

    Universität Bremen

    H-index: 36
    Marco Taisch

    Marco Taisch

    Politecnico di Milano

    H-index: 34
    Benoît Eynard

    Benoît Eynard

    Université de Technologie de Compiègne

    H-index: 29
    Marco Garetti

    Marco Garetti

    Politecnico di Milano

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