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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 43538

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Special Issue Editors

Prof. Dr. Dimitrios Kyritsis

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Guest Editor

Institute of Mechanical Engineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
Interests: ICT for sustainable manufacturing; closed loop lifecycle management; lifecycle performance evaluation; engineering asset lifecycle management; predictive manufacturing; product-process modelling; ontology-based engineering; context aware enterprise applications; knowledge management; industrial ontologies
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Dr. Jinzhi Lu

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Institute of Mechanical Engineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
Interests: domain-specific modeling; architecture design; decision making; semantics modeling and simulation; system modeling; process automation; co-simulation; systems dynamics; systems engineering; cyber-physical system; digital thread; cognitive twin; model-based systems engineering
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaochen Zheng

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Guest Editor

Institute of Mechanical Engineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
Interests: Internet of Things; machine learning; artificial intelligence; human–computer interaction; wearable technology; distributed ledger technology; blockchain; Industry 4.0; semantic engineering

Special Issue Information

Dear Colleagues,

In recent decades, the global manufacturing industries have been reshaped by the rapid development of advanced technologies, such as the Internet of Things, Artificial Intelligence (AI), machine learning, cloud/edge computing, smart sensing, advanced robotics, blockchain/distributed ledger technology, etc. These have fostered the concept of smart manufacturing as the next-generation manufacturing paradigm, which is the core of Industry 4.0.

The worldwide COVID-19 pandemic has significantly impacted the manufacturing industry and requires manufacturing companies to create rapid and radical innovations in both business and operating models to maintain production despite disrupted supply chains. Advanced smart manufacturing and production technologies will play a critical role in making manufacturing systems more resilient and thus able to cope with disruptions due to pandemics or other critical situations of global scale.

This Special Issue invites authors to submit their high-quality research articles that cover different topics of smart manufacturing including, but not limited to, data-driven smart manufacturing, Industrial Internet of Things (IIoT), data sharing for manufacturing, advanced cyber-physical production systems (CPPS), innovative models and reference architectures, industrial semantics and ontologies, process/quality optimization, applications of AI/machine learning in manufacturing, cyber security and blockchains in manufacturing, digital and cognitive twins for manufacturing, etc. This call also welcomes contributions that explore methodologies and practices of resilient manufacturing aiming to address the COVID-19 pandemic’s impact.

Prof. Dr. Dimitrios Kyritsis
Dr. Jinzhi Lu
Dr. Xiaochen Zheng
Guest Editors

Manuscript Submission Information

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Keywords

smart manufacturing
Industry 4.0
Industrial Internet of Things (IIoT)
cyber-physical production systems (CPPS)
resilient manufacturing
autonomous quality
industrial ontologies
digital twin
cognitive twin
industrial AI

Published Papers (12 papers)

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Editorial

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2 pages, 177 KiB

Open AccessEditorial

Special Issue: Smart Resilient Manufacturing

by Jinzhi Lu, Xiaochen Zheng and Dimitris Kiritsis

Appl. Sci. 2023, 13(1), 464; https://doi.org/10.3390/app13010464 - 29 Dec 2022

Viewed by 835

Abstract

During the past decades, the global manufacturing industries have been reshaped by the rapid development of advanced technologies, such as cyber-physical systems, Internet of Things, artificial intelligence (AI), machine learning, cloud/edge computing, smart sensing, advanced robotics, blockchain/distributed ledger technology, etc [...] Full article

(This article belongs to the Special Issue Smart Resilient Manufacturing)

Research

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20 pages, 5013 KiB

Open AccessArticle

A Data-Knowledge Hybrid Driven Method for Gas Turbine Gas Path Diagnosis

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

Appl. Sci. 2022, 12(12), 5961; https://doi.org/10.3390/app12125961 - 11 Jun 2022

Cited by 4 | Viewed by 1990

Abstract

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. Full article

(This article belongs to the Special Issue Smart Resilient Manufacturing)

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24 pages, 7386 KiB

Open AccessArticle

ANN-Based Inverse Goal-Oriented Design Method for Targeted Final Properties of Materials

by Waqas Ahmad, Guoxin Wang and Yan Yan

Appl. Sci. 2022, 12(7), 3420; https://doi.org/10.3390/app12073420 - 28 Mar 2022

Cited by 1 | Viewed by 1432

Abstract

Designing materials for targeted materials properties is the key to tackle the demands for personalized consumer products. The deficiency in the existing linear and nonlinear correlation methods attributed to simplifying assumptions and idealizations, nondeterministic simulations, and limited experimental data due to heavy computational time and cost, necessitates a design method that provides sufficient confidence to designers in decision making. To address this requirement, we propose, in this paper, an inverse goal-oriented materials design method supported by the design space exploration framework (DSEF). Keeping in view the accuracy and precision in the prediction confidence of machine learning-based methods, we developed an Artificial Neural Network based prediction model that supports DSEF. The proposed method for materials design can help designers to (1) explore PSPP spaces starting from end property requirements, (2) adjust the errors being propagated in the PSPP chain as well as in the predictions made by the model, and (3) timely adjust model parameters of the prediction model for accurate predictions. The efficacy of the method is illustrated for the hot stamping process to produce structural components from ultrahigh-strength steels (UHSS). The proposed method and prediction model are generic and applicable to any sequential manufacturing process to realize an end product. Full article

(This article belongs to the Special Issue Smart Resilient Manufacturing)

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15 pages, 2063 KiB

Open AccessArticle

Life-Cycle Oriented Risk Assessment Using a Monte Carlo Simulation

by Simon Züst, Michael Huonder, Shaun West and Oliver Stoll

Appl. Sci. 2022, 12(1), 8; https://doi.org/10.3390/app12010008 - 21 Dec 2021

Cited by 7 | Viewed by 2685

Abstract

State of the art mechatronic systems are complex assemblies of various parts and sub-systems. In such an interconnected system, even relatively cheap parts can have a major impact on the overall performance due to unexpected failure. Hence, lifecycle management has major implications on the successful modification of existing products. Potential savings due to changes in production and procurement must be compared to the implied risk of products failing in the field due to these changes. This work documents a generic approach for risk assessment based on the distribution of the expected savings and incident costs over the whole lifecycle. To do so, a stochastic model is introduced to quantify the expected savings and costs given a non-risk-free product modification. Using a Monte Carlo simulation, the effects of uncertainty are incorporated into the risk management. The model and simulation are deployed within an industrial use case. The application demonstrates both the appropriateness of the tool and its useability. Full article

(This article belongs to the Special Issue Smart Resilient Manufacturing)

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26 pages, 3091 KiB

Open AccessArticle

Cyber-Physical LPG Debutanizer Distillation Columns: Machine-Learning-Based Soft Sensors for Product Quality Monitoring

by Jože Martin Rožanec, Elena Trajkova, Jinzhi Lu, Nikolaos Sarantinoudis, George Arampatzis, Pavlos Eirinakis, Ioannis Mourtos, Melike K. Onat, Deren Ataç Yilmaz, Aljaž Košmerlj, Klemen Kenda, Blaž Fortuna and Dunja Mladenić

Appl. Sci. 2021, 11(24), 11790; https://doi.org/10.3390/app112411790 - 11 Dec 2021

Cited by 4 | Viewed by 3160

Abstract

Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables the provision of equipment state monitoring services and the generation of decision-making for equipment operations. In this paper, we propose two machine learning models: (1) to forecast the amount of pentane (C5) content in the final product mixture; (2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models. Full article

(This article belongs to the Special Issue Smart Resilient Manufacturing)

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18 pages, 18525 KiB

Open AccessArticle

Digital Twin-Enabled Decision Support Services in Industrial Ecosystems

by Jürg Meierhofer, Lukas Schweiger, Jinzhi Lu, Simon Züst, Shaun West, Oliver Stoll and Dimitris Kiritsis

Appl. Sci. 2021, 11(23), 11418; https://doi.org/10.3390/app112311418 - 02 Dec 2021

Cited by 17 | Viewed by 5110

Abstract

The goal of this paper is to further elaborate a new concept for value creation by decision support services in industrial service ecosystems using digital twins and to apply it to an extended case study. The aim of the original model was to design and integrate an architecture of digital twins derived from business needs that leveraged the potential of the synergies in the ecosystem. The conceptual framework presented in this paper extends the semantic ontology model for integrating the digital twins. For the original model, technical modeling approaches were developed and integrated into an ecosystem perspective based on a modeling of the ecosystem and the actors’ decision jobs. In a service ecosystem comprising several enterprises and a multitude of actors, decision making is based on the interlinkage of the digital twins of the equipment and the processes, which is achieved by the semantic ontology model further elaborated in this paper. The implementation of the digital twin architecture is shown in the example of a manufacturing SME (small and medium-sized enterprise) case that was introduced in. The mixed semantic modeling and model-based systems engineering for this implementation is discussed in further detail in this paper. The findings of this detailed study provide a theoretical concept for implementing digital twins on the level of service ecosystems and integrating digital twins based on a unified ontology. This provides a practical blueprint to companies for developing digital twin based services in their own operations and beyond in their ecosystem. Full article

(This article belongs to the Special Issue Smart Resilient Manufacturing)

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22 pages, 1739 KiB

Open AccessArticle

Implementation of Cognitive Digital Twins in Connected and Agile Supply Networks—An Operational Model

by Kostas Kalaboukas, Joze Rožanec, Aljaž Košmerlj, Dimitris Kiritsis and George Arampatzis

Appl. Sci. 2021, 11(9), 4103; https://doi.org/10.3390/app11094103 - 30 Apr 2021

Cited by 32 | Viewed by 3600

Abstract

Supply chain agility and resilience are key factors for the success of manufacturing companies in their attempt to respond to dynamic changes. The circular economy, the need for optimized material flows, ad-hoc responses and personalization are some of the trends that require supply chains to become “cognitive”, i.e., able to predict trends and flexible enough in dynamic environments, ensuring optimized operational performance. Digital twins (DTs) is a promising technology, and a lot of work is done on the factory level. In this paper, the concept of cognitive digital twins (CDTs) and how they can be deployed in connected and agile supply chains is elaborated. The need for CDTs in the supply chain as well as the main CDT enablers and how they can be deployed under an operational model in agile networks is described. More emphasis is given on the modelling, cognition and governance aspects as well as on how a supply chain can be configured as a network of connected CDTs. Finally, a deployment methodology of the developed model into an example of a circular supply chain is proposed. Full article

(This article belongs to the Special Issue Smart Resilient Manufacturing)

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33 pages, 2779 KiB

Open AccessArticle

Digital Twin Providing New Opportunities for Value Co-Creation through Supporting Decision-Making

by Shaun West, Oliver Stoll, Jürg Meierhofer and Simon Züst

Appl. Sci. 2021, 11(9), 3750; https://doi.org/10.3390/app11093750 - 21 Apr 2021

Cited by 35 | Viewed by 7482

Abstract

The application of digital twins provides value creation within the fields of operations and service management; existing research around decision-making and value co-creation is limited at this point. Prior studies have provided insights into the benefits of digital twins that combined both data and simulation approaches; however, there remains a managerial gap. The purpose of this paper is to explore this research gap using input from a multiple case study research design from both manufacturing environments and non-manufacturing environments. The authors use ten cases to explore how digital twins support value co-creation through decision-making. The authors were all involved in the development of the ten cases. Individual biases were removed by using the literature to provide the assessment dimensions and allowing a convergence of the results. Drawing on the lessons from the ten cases, this study empirically identified eight managerial issues that need to be considered when developing digital twins to support multi-stakeholder decision-making that leads to value co-creation. The application of digital twins in value co-creation and decision-making is a topic that has developed from practice and is an area where a research gap exists between theory and practice. A cross-case analysis was developed based on the literature and the ten cases (eight industrial and two pilot-scale cases) providing the empirical findings. The findings describe how firms can design, develop, and commercialize digital-twin-enabled value propositions and will initiate future research. Full article

(This article belongs to the Special Issue Smart Resilient Manufacturing)

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21 pages, 3467 KiB

Open AccessArticle

Robust Engineering for the Design of Resilient Manufacturing Systems

by Dimitris Mourtzis, John Angelopoulos and Nikos Panopoulos

Appl. Sci. 2021, 11(7), 3067; https://doi.org/10.3390/app11073067 - 30 Mar 2021

Cited by 25 | Viewed by 2878

Abstract

As the industrial requirements change rapidly due to the drastic evolution of technology, the necessity of quickly investigating potential system alternatives towards a more efficient manufacturing system design arises more intensely than ever. Production system simulation has proven to be a powerful tool for designing and evaluating a manufacturing system due to its low cost, quick analysis, low risk and meaningful insight that it may provide, improving the understanding of the influence of each component. In this research work, the design and evaluation of a real manufacturing system using Discrete Event Simulation (DES), based on real data obtained from the copper industry is presented. The current production system is modelled, and the real production data are analyzed and connected. The impact identification of the individual parameters on the response of the system is accomplished towards the selection of the proper configurations for near-optimum outcome. Further to that, different simulation scenarios based on the Design of Experiments (DOE) are studied towards the optimization of the production, under predefined product analogies. Full article

(This article belongs to the Special Issue Smart Resilient Manufacturing)

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20 pages, 5429 KiB

Open AccessEditor’s ChoiceArticle

Digital Twin and Reinforcement Learning-Based Resilient Production Control for Micro Smart Factory

by Kyu Tae Park, Yoo Ho Son, Sang Wook Ko and Sang Do Noh

Appl. Sci. 2021, 11(7), 2977; https://doi.org/10.3390/app11072977 - 26 Mar 2021

Cited by 30 | Viewed by 4695

Abstract

To achieve efficient personalized production at an affordable cost, a modular manufacturing system (MMS) can be utilized. MMS enables restructuring of its configuration to accommodate product changes and is thus an efficient solution to reduce the costs involved in personalized production. A micro smart factory (MSF) is an MMS with heterogeneous production processes to enable personalized production. Similar to MMS, MSF also enables the restructuring of production configuration; additionally, it comprises cyber-physical production systems (CPPSs) that help achieve resilience. However, MSFs need to overcome performance hurdles with respect to production control. Therefore, this paper proposes a digital twin (DT) and reinforcement learning (RL)-based production control method. This method replaces the existing dispatching rule in the type and instance phases of the MSF. In this method, the RL policy network is learned and evaluated by coordination between DT and RL. The DT provides virtual event logs that include states, actions, and rewards to support learning. These virtual event logs are returned based on vertical integration with the MSF. As a result, the proposed method provides a resilient solution to the CPPS architectural framework and achieves appropriate actions to the dynamic situation of MSF. Additionally, applying DT with RL helps decide what-next/where-next in the production cycle. Moreover, the proposed concept can be extended to various manufacturing domains because the priority rule concept is frequently applied. Full article

(This article belongs to the Special Issue Smart Resilient Manufacturing)

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18 pages, 2528 KiB

Open AccessArticle

Application of Quality Function Deployment for Product Design Concept Selection

by Olga A. Shvetsova, Sung Chul Park and Jang Hee Lee

Appl. Sci. 2021, 11(6), 2681; https://doi.org/10.3390/app11062681 - 17 Mar 2021

Cited by 13 | Viewed by 3897

Abstract

For business-to-business (B2B) companies, selecting new product concepts is vital to new product development (NPD), since it significantly contributes to the ultimate success and reputation of the product in terms of quality and function. The research problem is defining the best solution of new product’s design concept selection within high competition and resources’ limitation by mathematical approaches. The main objective of this study is developing an integrated analytical approach, combining quality function deployment (QFD) and analytic hierarchy process (AHP) approach, and data envelopment analysis (DEA) to enhance the effectiveness of design product decisions. The proposed approach focuses on mathematical methods to comprehensively evaluate and strategically select the best new product concept while considering the features of the B2B product and the available information during concept selection. The best new concept is selected by the combined scores derived from the concept competitiveness (quality function deployment—analytic hierarchy process) and the design development efficiency (data envelopment analysis). Finally, the design alternatives are classified into four categories by quadrant analysis for design concept management. The benefit of this approach—combining three mathematical models together for the best concept’s solution. Full article

(This article belongs to the Special Issue Smart Resilient Manufacturing)

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Review

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21 pages, 1877 KiB

Open AccessReview

Systematic Literature Review of MBSE Tool-Chains

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

Appl. Sci. 2022, 12(7), 3431; https://doi.org/10.3390/app12073431 - 28 Mar 2022

Cited by 12 | Viewed by 4252

Abstract

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. Full article

(This article belongs to the Special Issue Smart Resilient Manufacturing)

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