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  • 1.
    Antonelli, Dario
    et al.
    Department of Management and Production Engineering, Polytechnic University of Turin, Corso Duca degli Abruzzi 24, 10138 Torino, Italy, Corso Duca degli Abruzzi 24..
    Aliev, Khushid
    Department of Management and Production Engineering, Polytechnic University of Turin, Corso Duca degli Abruzzi 24, 10138 Torino, Italy, Corso Duca degli Abruzzi 24..
    Soriano, Marco
    Department of Management and Production Engineering, Polytechnic University of Turin, Corso Duca degli Abruzzi 24, 10138 Torino, Italy, Corso Duca degli Abruzzi 24..
    Samir, Kousay
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Monetti, Fabio Marco
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Exploring the limitations and potential of digital twins for mobile manipulators in industry2024In: 5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023), Elsevier BV , 2024, Vol. 232, p. 1121-1130Conference paper (Refereed)
    Abstract [en]

    This paper explores the qualification of a digital twin (DT) for a mobile manipulator (MOMA) in industrial applications. We discuss the development of different DT models based on various industrial needs and highlight the dependence of model accuracy on online sensor precision. Limitations of DTs for MOMA are examined, including challenges in respecting qualifiers due to the inability to incorporate unstructured aspects of the factory environment. Through a case study and some examples, we show the latent potential and limitations of DTs for MOMA in industrial contexts. The challenges of fidelity, real-time operation, and environment modeling are discussed. It is emphasized that creating a true digital twin of a mobile manipulator is hindered by the inability to include the complete surrounding environment. Recommendations for future research focus on addressing these limitations to enhance the effectiveness of DTs for MOMA in Industry 4.0 and smart manufacturing.

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  • 2.
    Boffa, Eleonora
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Development and application of an Integrated Business Model framework to describe the digital transformation of manufacturing - a bibliometric analysis2023In: Production & Manufacturing Research, E-ISSN 2169-3277, Vol. 11, no 1, article id 2164952Article in journal (Refereed)
    Abstract [en]

    The digitalisation trend is affecting the manufacturing industry byadopting several emerging technologies that can increase the efficiencyand output of production processes and operations. A growingbody of literature shows that this trend demands a structuralrethink of how companies do business. However, there is a lack ofholistic contributions describing how aspects of manufacturing digitalisationalign with the Business Model Innovation process. Thisstudy uses a bibliometric mapping approach to analyse the literatureon manufacturing digital transformation through the IntegratedBusiness Model (IBM) lens. The results identify the major researchtopics discussed in the analysed domain and propose an enrichedIBM framework with specific descriptions and connections amongthe components and their relative strengths. Holistically, the resultingenhanced model may ultimately assist practitioners in understandingthe innovation process of the BM triggered bytechnological shifts in their manufacturing, enabling an alignmentof the manufacturing strategy with IBM’s components.

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  • 3.
    Boffa, Eleonora
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Investigating the impact of digital transformation on manufacturers’ Business model: Insights from Swedish industry2024In: Journal of Open Innovation: Technology, Market, and Complexity, E-ISSN 2199-8531, Vol. 10, no 2, article id 100312Article in journal (Refereed)
    Abstract [en]

    Digital transformation (DT) triggers a fundamental technological shift in industry enabling the creation of smart and connected factories. DT is not only a technology-driven innovation approach: it also requires changes of the Business Model (BM). Despite this, there is a lack of comprehensive studies that examine how aspects of manufacturing digitalisation align with the Business Model Innovation (BMI) process. To address this gap, this paper analyses the DT journey of several Swedish medium and large firms through semi-structured interviews with staff involved in such process. The empirical findings reveal that the BM elements addressing value creation and strategy aspects are the most affected by DT. Additionally, this study identifies the links among such elements. The findings are compiled in a holistic framework that can serve as a blueprint for practitioners seeking to adopt digital technologies in their production environments. This holistic approach aims at supporting practitioners to understand the BMI process triggered by DT and consequently aligning their manufacturing strategy with the BM's components.

  • 4.
    Buchner, Felix
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industriella produktionssystem.
    Automating the Part Identification Method of Automotive Assembly Lines Through RFID Technology2022Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Barcode scanning has been used for many decades in the assembly process to identify individual parts. Besides the fact that scanning is a non-value-adding operation, it also is prone to error. It cannot beensured with certainty that the scanned part will be the part installed. Furthermore, if any part is interchanged, it leaves the manufacturer with the challenge of detecting this and correcting the data. The genealogy data is important as it enables precisely tracing which parts are built into which vehicle. Strong confidence in the integrity of the genealogy data allows a manufacturer to minimize the scope assembly line uptime. When scan errors occur, the factory execution system could stop the production line to fix the issue and ensure high quality. Therefore, this thesis proposes an alternative and innovative approach to the part identification and verification process in an assembly line. The approach is to replace the traditional barcode with a passive ultra-high frequency RFID label. It automates the identification process when a part is installed in the vehicle, which makes manual scanning redundant. The suggested approach also proposes a final traceability scan. Hereby the completely assembled vehicle and its components with the RFID tags are read again to verify the same parts are still installed. The result would be enhanced genealogy data of each vehicle. This thesis aims to determine the technical feasibility of both processes and investigate the economic feasibility.

    The conducted empirical research of this thesis is based on a literature review about RFID technology and its applications. To prove the technical feasibility, a series of experiments were carried out for the in-station part identification and the final traceability verification. With a determined number of test parts, a total of 498 experiments were conducted in a real production environment. Moreover, the proposed dual-antenna approach and software logic enables accurate part identification. Lastly, for the assessment of the economic feasibility, a comprehensive data model was developed to assess the production impact of scanning. 

    Literature and a theoretical investigation show that most of the already consumed scan results can be related to human errors. The experiments for the automated in-station identification reveal; that it is possible to accurately identify the installed part under at least one setup with the suggested dual-antenna approach. However, every single part needs its setup adjusted to the environment in which it is assembled. There is not one out-of-the-box solution that suits every individual application. The finding from the final traceability scan experiment is that all tested parts are identified by the determined setup. It becomes apparent that reading the individual parts even after a car is completed is possible, despite the interference of the metal chassis and radio frequency waves. The conclusion from the economic feasibility is that although the RFID tags are more expensive than barcode labels, the implementation could still offer significant financial benefits to a manufacturer. To summarize the topic, the proposed method based on RFID technology is an innovative approach that is technically feasible and offers a variety of benefits.

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  • 5.
    Ericsson, Kristian
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Process Management and Sustainable Industry.
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    A Systematic Literature Review on Combinations of Industry 4.0 and Lean Production2023In: Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures: IFIP WG 5.7 International Conference, APMS 2023, Proceedings, Springer Nature , 2023, p. 139-156Conference paper (Refereed)
    Abstract [en]

    Prior literature reviews on combining Industry 4.0 (I4.0) and Lean Production (LP) in production has often described the paradigms as “supportive”, where either “I4.0 supports LP” or “LP supports I4.0”. In this systematic review of 50 studies from this growing area of research, we find evidence of cases where combinations have not been “supportive”. We also find evidence of causal interactions that cannot be subordinated “I4.0 supports LP” nor “LP supports I4.0”, and that plainly go beyond those two categories. Additionally, we find that several studies evaluate the merits of I4.0- and LP combinations without looking at their effects on results in production, which substantially reduces the use of these evaluations to production managers. We encourage future studies to use nomenclature that does not unnecessarily limit the overall perception of the properties of I4.0- and LP combinations, to evaluate the merits of such combinations more in line with the requirements of production managers, and to be more cautious when concluding on causal interactions between I4.0 and LP.

  • 6.
    Jiang, Pei
    et al.
    State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, 400044, China.
    Wang, Zuoxue
    State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, 400044, China.
    Li, Xiaobin
    State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, 400044, China.
    Wang, Xi Vincent
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Yang, Bodong
    State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, 400044, China.
    Zheng, Jiajun
    State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, 400044, China.
    Energy consumption prediction and optimization of industrial robots based on LSTM2023In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 70, p. 137-148Article in journal (Refereed)
    Abstract [en]

    Due to wide distribution and low energy efficiency, the energy-saving of industrial robots draws more and more attention, and a large number of methods have emerged to predict or optimize the energy consumption (EC) of robots. However, many dynamic and electrical parameters are unavailable due to the commercial limitations of industrial robots, which constrains the application of those model-based methods. Therefore, this paper proposes a data-driven method for the prediction and optimization of robot EC. Initially, the cause-and-effect relationship between robot EC and joint motion variables, such as the joint position, velocity, and acceleration, is qualitatively analyzed based on the influence of the capacitive and inductive components in the drive system. And a deep neural network based on long short-term memory (LSTM) is proposed to reveal the nonlinear mapping between the industrial robot EC and the joint motion variables, which can predict EC without the parameters of the industrial robot. Based on the proposed neural network, the adaptive genetic algorithm is adopted to optimize the time-variant scaling function, which can optimize the scaled trajectory to reduce EC without hardware modification. To validate the accuracy and efficacy of the proposed method, experiments are conducted on a KUKA KR60-3 six degree-of-freedom (DOF) industrial robot. The results demonstrate that the proposed neural network can predict EC with a mean absolute percentage error less than 4.21% and the proposed method reduces the EC by 22.35%.

  • 7.
    Leng, Jiewu
    et al.
    Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing, and State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou 510006, China.
    Zhu, Xiaofeng
    Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing, and State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou 510006, China.
    Huang, Zhiqiang
    Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing, and State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou 510006, China.
    Li, Xingyu
    School of Engineering Technology, Purdue University, West Lafayette, IN 47907, USA.
    Zheng, Pai
    Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China.
    Zhou, Xueliang
    Department of Electrical and Information Engineering, HuBei University of Automotive Technology, Shiyan 442002, China.
    Mourtzis, Dimitris
    Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Rio Patras, 26504, Greece.
    Wang, Baicun
    State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China.
    Qi, Qinglin
    School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.
    Shao, Haidong
    College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
    Wan, Jiafu
    School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China.
    Chen, Xin
    Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing, and State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou 510006, China.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Liu, Qiang
    Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing, and State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou 510006, China.
    Unlocking the power of industrial artificial intelligence towards Industry 5.0: Insights, pathways, and challenges2024In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 73, p. 349-363Article, review/survey (Refereed)
    Abstract [en]

    With the continuous development of human-centric, resilient, and sustainable manufacturing towards Industry 5.0, Artificial Intelligence (AI) has gradually unveiled new opportunities for additional functionalities, new features, and tendencies in the industrial landscape. On the other hand, the technology-driven Industry 4.0 paradigm is still in full swing. However, there exist many unreasonable designs, configurations, and implementations of Industrial Artificial Intelligence (IndAI) in practice before achieving either Industry 4.0 or Industry 5.0 vision, and a significant gap between the individualized requirement and actual implementation result still exists. To provide insights for designing appropriate models and algorithms in the upgrading process of the industry, this perspective article classifies IndAI by rating the intelligence levels and presents four principles of implementing IndAI. Three significant opportunities of IndAI, namely, collaborative intelligence, self-learning intelligence, and crowd intelligence, towards Industry 5.0 vision are identified to promote the transition from a technology-driven initiative in Industry 4.0 to the coexistence and interplay of Industry 4.0 and a value-oriented proposition in Industry 5.0. Then, pathways for implementing IndAI towards Industry 5.0 together with key empowering techniques are discussed. Social barriers, technology challenges, and future research directions of IndAI are concluded, respectively. We believe that our effort can lay a foundation for unlocking the power of IndAI in futuristic Industry 5.0 research and engineering practice.

  • 8.
    Li, Dehua
    et al.
    National Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
    Li, Yingguang
    National Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
    Liu, Changqing
    National Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
    Liu, Xu
    School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    An online inference method for condition identification of workpieces with complex residual stress distributions2024In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 73, p. 192-204Article in journal (Refereed)
    Abstract [en]

    The residual stress field of structural components significantly influences their comprehensive performance and service life. Due to the lack of effective representation means and inference methods, existing methods are confined to inspecting local residual stress rather than the entire residual stress field, rendering the inference of complex residual stress fields quite difficult. In response to the challenges associated with the requirement for extensive sets of deformation force data from the current workpiece and the inherent difficulty in establishing a stable relationship between deformation forces and residual stress fields, this paper introduces a novel inference method of residual stress field is proposed based on a data-causal knowledge fusion model, where causal knowledge is introduced to eliminate the coupling effect of geometric change on residual stress, which can make up the drawback of pure data driven model. The proposed approach can accurately inference the residual stress within the workpieces, which provides an important basis for deformation control and part property improvement.

  • 9.
    Lindqvist, Richard
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Metrology and Optics.
    Lundgren, Magnus
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Hedman, Stefan
    Volvo Construction Equipment, Eskilstuna, Sweden.
    Lindahl, Peter
    Vångell, Tomas
    Mattsson, Lars
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    An information-model approach for systematic and holistic geometrical inspection and control planning (GICP)2009In: Journal of the CMSC, ISSN 2328-6067, Vol. 4, no 2, p. 20-26Article in journal (Refereed)
    Abstract [en]

    The purpose of this article is to present research results performed within the field of geometrical inspection and control planning (GICP) applied for complex products. Geometrical and dimensional inspection and control planning are vitally important activities in modern manufacturing of complex products. These functions are interesting to manufacturers due to the demands and focus on quality work and the aim towards zero defects. The GICP model can be extended to a fully automatic process but more likely it will be implemented as a manual iterative and parallel process in the development and industrialization process of complex products. Applications for our new GICP information model will hopefully be implemented and used not only in large manufacturing plants but also in smaller companies that lack easy-to-use instructions and guidelines regarding geometrical inspection and control planning. It will also be instrumental academically, in the education of new industrial metrology engineers. In this article, our new systematic and holistic information model based on the ASTRAKAN modeling language is proposed and presented. The relation and integration of the quality assurance matrix and methodology (QAM) is also discussed and presented.

  • 10.
    Lupi, Francesco
    et al.
    Department of Information Engineering, University of Pisa, Italy.
    Mabkhot, Mohammed M.
    Intelligent Automation Centre, The Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UK.
    Boffa, Eleonora
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Ferreira, Pedro
    Intelligent Automation Centre, The Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UK.
    Antonelli, Dario
    Department of Management and Production Engineering, Politecnico di Torino, 10129 Torino, Italy.
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Lohse, Niels
    Intelligent Automation Centre, The Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UK.
    Lanzetta, Michele
    Department of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy.
    Automatic definition of engineer archetypes: A text mining approach2023In: Computers in industry (Print), ISSN 0166-3615, E-ISSN 1872-6194, Vol. 152, article id 103996Article in journal (Refereed)
    Abstract [en]

    With the rapid and continuous advancements in technology, as well as the constantly evolving competences required in the field of engineering, there is a critical need for the harmonization and unification of engineering professional figures or archetypes. The current limitations in tymely defining and updating engineers' archetypes are attributed to the absence of a structured and automated approach for processing educational and occupational data sources that evolve over time. This study aims to enhance the definition of professional figures in engineering by automating archetype definitions through text mining and adopting a more objective and structured methodology based on topic modeling. This will expand the use of archetypes as a common language, bridging the gap between educational and occupational frameworks by providing a unified and up-to-date engineering professional figure tailored to a specific period, specialization type, and level. We validate the automatically defined industrial engineer archetype against our previously manually defined profile.

  • 11.
    Lupi, Francesco
    et al.
    Department of Information Engineering, University of Pisa, 56122 Pisa, Italy.
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Lanzetta, Michele
    Department of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy.
    CAD-based Autonomous Vision Inspection Systems2024In: 5th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2023, Elsevier BV , 2024, p. 2127-2136Conference paper (Refereed)
    Abstract [en]

    Automated industrial Visual Inspection Systems (VIS) are typically customized for specific applications, limiting their flexibility. They are characterized by a demanding setup, high capital investments, and significant knowledge barriers. In this paper, we propose an alternative architecture for the visual inspection of 3D printed parts or complex assemblies using a robotic arm equipped with hand-eye sensors and controllable lighting system. The core of the proposed Flexible Vision Inspection System (FVIS) is the self-extraction of 3D text annotations from STandard for the Exchange of Product model (STEP) AP242 files. The system self-selects and parametrizes the most suitable inspection algorithm, including lighting settings. Additionally, it autonomously performs self-localization, self-referencing of physical products, and self-planning of robot inspection path based on CAD information. This framework, characterized by self-X, cost-effective, non-invasive, and plug-and-play architecture has the potential to disrupt the business model of vision inspection, enabling an as-a-service solution aligned with the next generation of flexible manufacturing.

  • 12.
    Maffei, Antonio
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Enoksson, Fredrik
    KTH, School of Industrial Engineering and Management (ITM), Learning, Digital Learning.
    What is the optimal blended learning strategy throughout engineering curricula? Lesson learned during Covid-19 pandemic2023In: EDUCON 2023 - IEEE Global Engineering Education Conference, Proceedings, Institute of Electrical and Electronics Engineers (IEEE) , 2023Conference paper (Refereed)
    Abstract [en]

    The recent Covid-19 pandemic has forced HEI to quickly adjust their approach to teaching and learning by massively adopting, basically overnight, approaches based on digital learning. Covid-19 gave a huge impulse to the spread of digital tools and the consequent literature is rich in details and information about more and less successful experiences with digital learning. This unexpected 'experiment' exposed the effectiveness and efficiency of well-conceived learning strategies that blend digital and traditional learning approach. While blended learning per se is an established discipline, a unified framework to go from theory to practice is still elusive. Given the above, the knowledge developed and acquired during the pandemic has a huge potential to reveal what are the features of a good blended learning strategy in different educational situation. This work aims at investigating what would be an optimal blended learning strategy for program design in the context of engineering education. The contribution is based on a focus group discussion involving teacher from 6 different European HEI and results indicates that the level of understanding, as presented in the Bloom taxonomy, of the focal activity and the correct balance between the educational and social dimensions of student's university life are the two critical drivers for blended learning design. The study confirms also the main findings in literature, related to the use of digital tools before and during the pandemic and suggests that the Covid-19 had a positive impact on the readiness to adopt digital tool of HEI. Finally a few problems experienced in the post covid phase are presented along with the identified future directions of research.

  • 13.
    Monetti, Fabio Marco
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Bertoni, Marco
    Blekinge Institute of Technology, Karlskrona, Sweden.
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    A Systematic Literature Review:Key Performance Indicatorson Feeding-as-a-Service2024In: Sustainable Production through Advanced Manufacturing, Intelligent Automation and Work Integrated Learning: Proceedings of the 11th Swedish Production Symposium (SPS2024), IOS Press , 2024, Vol. 52, p. 256-267Conference paper (Refereed)
    Abstract [en]

    In the evolving landscape of modern manufacturing, a novel concept known as Feeding-as-a-Service (FaaS) is emerging, part of the larger Automationas-a-Service (AaaS) framework. FaaS aims to optimize feeding systems in cloud manufacturing environments to meet the demands of mass customization and allow for quick responses to production changes. Therefore, it fits into the Manufacturing as-a-Service (MaaS) system as well. As the manufacturing industry undergoes significant transformations through automation and service-oriented models, understanding how FaaS fits into the other frameworks is essential.This study presents a systematic literature review with two primary objectives: first, to contextualize FaaS within AaaS and MaaS, highlighting similarities, differences,and distinctive characteristics; second, to identify and clarify the essential Key Performance Indicators (KPIs) crucial for its strategic implementation.KPIs are pivotal metrics guiding organizations toward manufacturing excellence.In this context, common KPIs focus on efficiency and quality, such as resource utilization, and error rates. Other KPIs are also crucial, such as the ones related tocost reduction and customer satisfaction. For FaaS, the most relevant include also data security, data management, and network speed.This research provides a valuable KPI framework for FaaS developers, aidingin strategic decision making and deployment in industrial settings. It also contributes to a broader understanding of KPIs in manufacturing, which benefits both researchers and industrial practitioners.The results of the review, though, fail to address other crucial indicators for ‘asa-Service’ business, such as Churn Rate and Total Contract Value. Future research will address these limitations through methods ranging from questionnaires to practitioner interviews, with the aim of gathering the knowledge needed for real-world implementations.

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  • 14.
    Monetti, Fabio Marco
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Feeding-as-a-Service in a cloud manufacturing environment2023In: 56th CIRP International Conference on Manufacturing Systems, CIRP CMS 2023, Elsevier BV , 2023, p. 1387-1392Conference paper (Refereed)
    Abstract [en]

    The shift towards a mass customization paradigm in production requires the development of new concepts for manufacturing systems. Manufacturing system producers need to address the investment gap between large companies and SMEs to open new market shares and generate new revenue streams. Cloud technologies offer new service models and business opportunities: combined with Product Service Systems ideas, they can have a significant impact on both customers and suppliers. The paper proposes a new concept called Feeding-as-a-Service, which aims to connect servitization and cloud technology to explore how a feeding system can be deployed within an efficient and sustainable Configure-to-Order paradigm in a cloud manufacturing environment. The article outlines the potential system architecture, necessary technologies, and business model for the proposed Feeding-as-a-Service concept and highlights the advantages that the system offers through the enhancement of autonomous robotics capabilities for a cloud-deployed feeding service.

  • 15.
    Monetti, Fabio Marco
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Towards the definition of assembly-oriented modular product architectures: a systematic review2024In: Research in Engineering Design, ISSN 0934-9839, E-ISSN 1435-6066, Vol. 35, no 2, p. 137-169Article in journal (Refereed)
    Abstract [en]

    The success of a product in the market is largely defined by the quality of design decisions made during the early stages of development. The product design requires designers to balance multiple objectives such as functionality, cost, and user satisfaction, while addressing the challenges posed by increasing product variants and customization demands. To tackle these challenges, one approach is to structure a comprehensive model that incorporates design for assembly (DFA) guidelines during the formulation of product architecture in the conceptual phase of development. While numerous strategies have been proposed in the literature, information is often scattered, making it difficult for readers to gain a comprehensive understanding of the topic. This paper systematically reviews the role and impact of DFA in product development, consolidating and presenting the information coherently. The review provides an overview of the methods developed, along with their potential benefits and limitations. A common framework is identified that defines the structure of the models, helping designers integrate assembly consideration into their design processes, thus reducing assembly time, cost, and complexity. The framework describes the operational setting, including the domain and context in which models operate, and offers a classification of possible methods and desired outputs. Additionally, the review identifies the industry in which case studies have been most frequently presented, and the software used to facilitate the process. By connecting with such a framework, future models can be created following a structured approach, and existing models can be classified and upgraded accordingly.

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  • 16.
    Monetti, Fabio Marco
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Martínez, Pablo Zaguirre
    KTH.
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Assessing sustainable recyclability of battery systems: a tool to aid design for disassembly2024In: Proceedings of the Design Society, Design 2024, Cambridge University Press (CUP) , 2024, Vol. 4, p. 1389-1398Conference paper (Refereed)
    Abstract [en]

    This study, conducted with Northvolt, examines battery system recyclability and disassembly dynamics. It introduces indices for material and product recyclability, along with disassembly time assessment. The goal is to create a design tool to streamline the evaluation of battery disassembly, aiding in designing recyclable and serviceable components. These methodologies serve as a blueprint for enhancing battery systems' overall sustainability and circularity design, presenting a base for future product development in alignment with environmental and economic objectives.

  • 17.
    Mourtzis, Dimitris
    et al.
    Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, 26504, Rio Patras, Greece.
    Ong, S. K.
    Mechanical Engineering Department, Faculty of Engineering, National University of Singapore, Singapore, Singapore.
    Wang, Xi Vincent
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Panopoulos, Nikos
    Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, 26504, Rio Patras, Greece.
    Stark, Rainer
    Institute for Machine Tools and Factory Management, Technische Universitat Berlin, Berlin, Germany.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Modelling, Design and Simulation as-a-Service Based on Extended Reality (XR) in Industry 4.02024In: CIRP Novel Topics in Production Engineering: Volume 1, Springer Nature , 2024, Vol. Part F2256, p. 99-143Chapter in book (Other academic)
    Abstract [en]

    Modelling and Simulation (M&S) are critical capabilities for Cloud Computing. M&S products and services are valuable resources that have to be easily accessible and available on demand in a cost-effective way to users; they provide the required level of agility so that capabilities can be integrated quickly and easily. To address new design and manufacturing challenges in Industry 4.0, digital-driven technologies use simulation tools, Computer Aided Design (CAD), Product Lifecycle Management (PLM) systems and Extended Reality (XR) services to support digital design and information flow throughout a product lifecycle. Thus, XR creates new business value by improving the customer journey, optimizing employee performance, and developing new content and services. The vision of Modelling and Simulation as a Service (MSaaS) aims to make products, data, and processes easily accessible and available on-demand to all users to improve operational effectiveness. The scope of this essay is to provide a comprehensive vision of MSaaS for products, data, and processes in combination with XR services to improve operational effectiveness under the framework of Industry 4.0.

  • 18.
    Rea Minango, Nathaly
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Functional information integration in product development by using assembly features2023In: Procedia CIRP, Elsevier BV , 2023, Vol. 119, p. 254-259Conference paper (Refereed)
    Abstract [en]

    Product development is a collaboration-intensive process resulting in a novel or enhanced product that satisfies the customers' needs. To meet those needs, functional requirements are defined, which ultimately determine the product's physical characteristics and manufacturing. However, the functional and other non-geometrical information becomes less noticeable as the process advances, since the primary representation of the product design in the latter stages of the product development is often a purely geometrical model, causing information fragmentation. This fragmentation hinders the collaboration between different stakeholders while neglecting valuable product information that could facilitate its manufacturing. Research to date has not yet provided a suitable way to link the geometrical model of a product with its non-geometrical information. Focused on the assembly domain, this work proposes a way to integrate functional information into the product's geometrical model by using assembly features, which could be employed in the latter stages to extract process constraints and additional product details relevant to assembly process planning, as shown in the developed case study. This paper provides new insights into the value of the assembly feature as a functional information carrier and a tool for improving the collaboration between different stakeholders during the product development process.

  • 19.
    Rea Minango, Nathaly
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Using physical interfaces for product design: from design to assembly planning2023In: Procedia CIRP, 56th CIRP International Conference on Manufacturing Systems, CIRP CMS 2023, Elsevier BV , 2023, p. 1303-1308Conference paper (Refereed)
    Abstract [en]

    Mass customization demands a wide range of product variants being provided by the manufacturer in a short time. To respond to that, companies embrace approaches such as product platforms and modularization. This impacts the product development process (PDP), especially at the design stage. A major concern is the definition of component and system interfaces since these become essential inputs for later stages. Yet, a suitable method to describe the interfaces, represent them, and share them within a product model is still elusive. Since the information enclosed in the interface is multi-faceted, ranging from geometry to function or kinematics, it can be adjusted or enriched in every stage of product development. Then, it is crucial to understand how this information can support product preparation and planning, as well as how it can be reused in product design or redesign. Thus, this work aims to assess the extent to which these factors were considered in the assembly domain. The study started by presenting current practices on representing the interfaces in the product design and continued by exploring how the information on the interfaces can be used for assembly planning. Subsequently, it proposed a way to represent the knowledge enclosed in the interface and concluded by explaining how this knowledge can be exploited by different stakeholders to improve the process planning and the overall assembly system. A case study shows the application of the proposed approach. Overall, the results showed the benefits of a better representation of interfaces in product design, specifically for the assembly domain, aiming to contribute to defining a strategy for knowledge reuse in manufacturing.

  • 20.
    Urgo, Marcello
    et al.
    Department of Mechanical Engineering, Politecnico di Milano, Milano, Italy.
    Berardinucci, Francesco
    Department of Mechanical Engineering, Politecnico di Milano, Milano, Italy.
    Zheng, Pai
    Department of Industrial and Systems Engineering (ISE), The Hong Kong Polytechnic University, Hong Kong SAR, China.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    AI-Based Pose Estimation of Human Operators in Manufacturing Environments2024In: Lecture Notes in Mechanical Engineering, Springer Nature , 2024, Vol. Part F2256, p. 3-38Chapter in book (Other academic)
    Abstract [en]

    The fast development of AI-based approaches for image recognition has driven the availability of fast and reliable tools for identifying the human body in captured videos (both 2D and 3D). This has increased the feasibility and effectiveness of approaches for human pose estimation in industrial environments. This essay will cover different approaches for estimating the human pose based on neural networks (e.g., CNN, LSTM, etc.), addressing the workflow and requirements for their implementation and use. A brief analysis and comparison of the existing AI-based frameworks and approaches will be carried out (e.g. OpenPose, MediaPipe) together with a listing of the related hardware and software requirements. Finally, two case studies presenting applications in the manufacturing sector are provided.

  • 21.
    Wang, Binbin
    et al.
    School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.
    Zheng, Lianyu
    School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China; MIIT Key Laboratory of Intelligent Manufacturing Technology for Aeronautics Advanced Equipment, Ministry of Industry and Information Technology, Beijing 100191, China.
    Wang, Yiwei
    School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China; MIIT Key Laboratory of Intelligent Manufacturing Technology for Aeronautics Advanced Equipment, Ministry of Industry and Information Technology, Beijing 100191, China.
    Fang, Wei
    School of Automation, Beijing University of Posts and Telecommunications, Beijing, China.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Towards the industry 5.0 frontier: Review and prospect of XR in product assembly2024In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 74, p. 777-811Article, review/survey (Refereed)
    Abstract [en]

    As an emerging manufacturing paradigm, Industry 5.0 emphasizes human-centric intelligent manufacturing. XR technology (a general term of virtual reality, augmented reality and mixed reality) brings unprecedented opportunities for assembly in such manufacturing paradigm. We provide a comprehensive review, in-depth analysis, and prospect on XR in product intelligent assembly from two points of views of technology and application. Subsequently, the benefits and potential of XR in assembly are discussed from three perspectives of users, enterprises and industries. Finally, challenges and future research directions for XR are outlined from the perspectives of hardware issues and technological maturity. This review is expected to provide useful references for XR-related research and application in the future.

  • 22.
    Wang, Yong
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Huang, Keming
    Wuhan Univ Sci & Technol, Sch Management, Wuhan 430080, Peoples R China..
    Hu, Yi
    Wuhan Univ Sci & Technol, Sch Management, Wuhan 430080, Peoples R China..
    Lu, Qian
    Wuhan Univ Sci & Technol, Ctr Serv Sci & Engn, Wuhan 430065, Peoples R China.;Wuhan Univ Technol, Sanya Sci & Educ Innovat Pk, Sanya 572000, Peoples R China..
    Hou, Wenjie
    Wuhan Univ Sci & Technol, Sch Management, Wuhan 430080, Peoples R China..
    Zhang, Jiamin
    Wuhan Univ Sci & Technol, Sch Management, Wuhan 430080, Peoples R China..
    Research on Pharmaceutical Supply Chain Decision-Making Model Considering Output and Demand Fluctuations2024In: IEEE Access, E-ISSN 2169-3536, Vol. 12, p. 61629-61641Article in journal (Refereed)
    Abstract [en]

    In response to the evolving pharmaceutical supply chain market, which is shifting from a seller's to a buyer's market, this study introduces an innovative decision-making model that addresses the volatility in output and demand. Our primary innovation lies in the development of a "revenue-sharing plus margin" collaborative decision-making optimization model, designed to enhance supply chain coordination amidst uncertainty. By establishing a revenue function, we propose a coordinated approach that leverages both revenue-sharing coefficients and profit margins to achieve Pareto improvements for supply chain partners. Through numerical analysis, we identify an optimal value range for the model's parameters, demonstrating the model's efficacy in balancing the expected returns for both suppliers and retailers. The study's contributions are threefold: (1) the introduction of a novel decision-making model tailored to the pharmaceutical supply chain, (2) the provision of a clear framework for comparing collaborative versus decentralized decision-making outcomes, and (3) the empirical validation of the model through case studies, showcasing its superiority in overall supply chain efficiency. This research significantly extends the current literature by providing a nuanced understanding of how supply chain coordination can be optimized under conditions of uncertainty. The findings underscore the importance of collaborative decision-making in achieving supply chain stability and offer practical insights for supply chain management in the pharmaceutical industry.

  • 23.
    Wei, Xudong
    et al.
    Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China.
    Liu, Xianli
    Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China.
    Yue, Caixu
    Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Liang, Steven Y.
    George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta 30332, USA.
    Qin, Yiyuan
    Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China.
    A multi-sensor signals denoising framework for tool state monitoring based on UKF-CycleGAN2023In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 200, article id 110420Article in journal (Refereed)
    Abstract [en]

    The denoising of mechanical system is always an indispensable process in sensor signal analysis. It directly affects the result of subsequent tool state monitoring and identification. Therefore, a denoising framework is proposed to solve this problem. Bayesian nonparametric estimation instead of the Gaussian fitting distribution of CycleGAN can ensure the quality of denoising data to the greatest extent. The experiment of milling 42CrMo steel was carried out, and the proposed method was verified. Compared with the wavelet packet threshold, the signal-to-noise ratio (SNR) obtained by the propose model is increased by 4.71 dB on average, and RMSE ranges from 0.0210 to 0.0642. UKF-CycleGAN model has better denoising effect than other methods. The model proposed in this paper improves the accuracy of tool wear identification. At the same time, the process of selecting the parameters for denoising model by manual experience can be reduced. This provides the possibility for online denoising of sensor signals in milling process, which has certain guiding significance for tool state monitoring in machinery industry.

  • 24.
    Wu, Wenbo
    et al.
    School of Mechano-Electronic Engineering, Xidian University, Xi'an 710071, China.
    Liu, Yongkui
    School of Mechano-Electronic Engineering, Xidian University, Xi'an 710071, China.
    Zhang, Lin
    School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.
    Xu, Xun
    Department of Mechanical Engineering, The University of Auckland, Auckland 1142, New Zealand.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Deep discriminative clustering and structural constraint for cross-domain fault diagnosis of rotating machinery2023In: Manufacturing Letters, ISSN 2213-8463, Vol. 35, p. 1072-1080Article in journal (Refereed)
    Abstract [en]

    With the rapid development of intelligent manufacturing, fault diagnostic methods based on deep learning have achieved impressive results. However, most methods require plentiful annotated samples and are based on the assumption that data from the source and target domains has the same distribution. These two conditions are difficult to satisfy in practical engineering. In light of these problems, an unsupervised domain adaptation approach named Deep Discriminative Clustering network with Structural Constraint (DDCSC) is proposed in this article. In our method, a Convolutional Neural Network (CNN) module is exploited for learning feature representations of raw data. Then a softmax module is employed to simultaneously predict class probabilities and cluster assignments of the source and target data, respectively. The learnable cluster centroids are introduced into the latent feature space to alleviate the data distribution discrepancy while better capturing the discriminative structure of the target data. In addition, geometric properties of the source data in a feature space are constrained to expand the scope of each category, which facilitates to improve prediction accuracy. An information-theoretic metric is considered as the objective function of discriminative clustering. Diagnostic experiments on a rolling bearing dataset demonstrate that our approach outperforms other popular intelligent approaches and confirms the effectiveness of discriminative clustering.

  • 25.
    Zhang, Bowen
    et al.
    Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education. Harbin University of Science and Technology, Harbin 150080, PR China.
    Liu, Xianli
    Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education. Harbin University of Science and Technology, Harbin 150080, PR China.
    Yue, Caixu
    Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education. Harbin University of Science and Technology, Harbin 150080, PR China.
    Liang, Steven Y.
    George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta 30332, USA.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Meta-learning-based approach for tool condition monitoring in multi-condition small sample scenarios2024In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 216, article id 111444Article in journal (Refereed)
    Abstract [en]

    Tool Condition Monitoring (TCM) technology in machining is crucial for maintaining safety and optimizing costs. However, its practical application faces two significant challenges: difficulties in data collection and a decline in generalization performance across different monitoring tasks. To this end, a hybrid feature boundary-enhanced meta-learning network with adaptive gradients (HFBEAML) is proposed. This method combines cutting process parameters with time-series signal features, employing a multimodal one-dimensional Convolutional Neural Network (1D-CNN) based on the DenseNet architecture and a multi-head self-attention mechanism (MHSA) to mine multi-dimensional features sensitive to tool wear. To further enhance the model's feature discrimination capability, a multi-loss joint optimization strategy is introduced, combining task-level loss with an improved triplet loss. This approach incorporates strategies for adaptive boundaries and handling the hardest positive and negative sample sets, thereby enhancing the robustness of feature metrics. Additionally, this research innovatively proposes an adaptive meta-level gradient update mechanism, dynamically adjusting gradient weights according to the characteristics of multiple tasks, aiming to improve the model's generalization ability in multi-task learning environments. The effectiveness of the proposed method is demonstrated through experiments in two different scenarios, comparing its results with five other models showcasing its significant advantages in multi-task, small-sample data monitoring environments.

  • 26.
    Zhao, Zhiheng
    et al.
    Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China; Research Institute for Advanced Manufacturing, The Hong Kong Polytechnic University, Hong Kong, China; State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China.
    Zhang, Mengdi
    Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Kong, China.
    Wu, Wei
    College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, China.
    Huang, George Q.
    Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China; Research Institute for Advanced Manufacturing, The Hong Kong Polytechnic University, Hong Kong, China.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Spatial-temporal traceability for cyber-physical industry 4.0 systems2024In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 74, p. 16-29Article in journal (Refereed)
    Abstract [en]

    The COVID-19 outbreak has posed significant challenges to end-to-end global supply chain visibility and transparency, with city lockdowns, factory shutdowns, flight cancellations, cross-border closures, and other uncertainties, disruptions, and disturbances. To address these challenges, reliable and accurate spatial-temporal information of physical objects and processes is essential to understand the industrial context and predict potential risks or bottlenecks for further decision-making. Product traverse both indoor (e.g., shopfloors and warehouses) and outdoor (during transportation) contexts. Despite significant advances in spatial-temporal traceability for outdoor environments using Global Positioning System (GPS) and Geographic Information Systems (GIS), satisfactory performance has not yet been achieved in indoor context, which accounts for the majority of operations. This limitation results in disjointed visibility and inaccessible transparency across the holistic supply chain. This research introduces universal and interoperable spatial-temporal elements for cyber-physical industrial 4.0 systems (CPIS) and develops a multi-modal bionic learning (MMBL) method for accurate and enduring indoor positioning. Proximity, mobility, and contextual reasoning mechanisms are designed to capture the interplay, evolution, and synchronization among objects at the operations level. To validate and evaluate the effectiveness of the proposed solution, we first conduct laboratory experiment and then apply the method in a real-life case company. Comparative analysis is conducted. MMBL clearly outperforms the other methods with 95% of the errors are within 3.41 m and maintains effectiveness after a year of use, which represents a significant step forward in achieving spatial-temporal traceability in CPIS.

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