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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.
    de Giorgio, Andrea
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Cacace, Stefania
    Department of Mechanical Engineering, Politecnico di Milano, Via La Masa 1, 20156 Milan, Italy.
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Monetti, Fabio Marco
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Digital Smart Production.
    Roci, Malvina
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Manufacturing and Metrology Systems.
    Onori, Mauro
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Assessing the influence of expert video aid on assembly learning curves2021In: Article in journal (Refereed)
    Abstract [en]

    Since the introduction of the concept of learning curves in manufacturing, many articles have been applying the model to study learning phenomena. In assembly, several studies present a learning curve when an operator is trained over a new assembly task; however, when comparisons are made between learning curves corresponding to different training methods, unaware researchers can show misleading results. Often, these studies neglect either or both the stochastic nature of the learning curves produced by several operators under experimental conditions, and the high correlation of the experimental samples collected from each operator that constitute one learning curve. Furthermore, recent studies are testing newer technologies, such as assembly animations or augmented reality, to provide assembly aid, but they fail to observe deeper implications on how these digital training methods truly influence the learning curves of the operators. This article proposes a novel statistical study of the influence of expert video aid on the learning curves in terms of assembly time by means of functional analysis of variance (FANOVA). This method is better suited to compare learning curves than common analysis of variance (ANOVA), due to correlated data, or graphical comparisons, due to the stochastic nature of the aggregated learning curves. The results show that two main effects of the expert video aid influence the learning curves: one in the transient and another in the steady state of the learning curve. The transient effect of the expert video aid, where the statistical tests suffer from a high variance in the data, appears to be a reduction in terms of assembly time for the first assemblies: the operators seem to benefit from the expert video aid. As soon as the steady state is reached, a slower and statistically significant effect appears to favor the learning processes of the operators who do not receive any training aid. Since the steady state of the learning curves represents the long term production efficiency of the operators, the latter effect might require more attention from industry and researchers.

  • 3.
    de Giorgio, Andrea
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Cacace, Stefania
    Politecn Milan, Dept Mech Engn, Via La Masa 1, I-20156 Milan, Italy..
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Monetti, Fabio Marco
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Digital Smart Production.
    Roci, Malvina
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Manufacturing and Metrology Systems.
    Onori, Mauro
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Assessing the influence of expert video aid on assembly learning curves2022In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 62, p. 263-269Article in journal (Refereed)
    Abstract [en]

    Since the introduction of the concept of learning curves in manufacturing, many articles have been applying the model to study learning phenomena. In assembly, several studies present a learning curve when an operator is trained over a new assembly task; however, when comparisons are made between learning curves corresponding to different training methods, unaware researchers can show misleading results. Often, these studies neglect either or both the stochastic nature of the learning curves produced by several operators under experimental conditions, and the high correlation of the experimental samples collected from each operator that constitute one learning curve. Furthermore, recent studies are testing newer technologies, such as assembly animations or augmented reality, to provide assembly aid, but they fail to observe deeper implications on how these digital training methods truly influence the learning curves of the operators. This article proposes a novel statistical study of the influence of expert video aid on the learning curves in terms of assembly time by means of functional analysis of variance (FANOVA). This method is better suited to compare learning curves than common analysis of variance (ANOVA), due to correlated data, or graphical comparisons, due to the stochastic nature of the aggregated learning curves. The results show that two main effects of the expert video aid influence the learning curves: one in the transient and another in the steady state of the learning curve. The transient effect of the expert video aid, where the statistical tests suffer from a high variance in the data, appears to be a reduction in terms of assembly time for the first assemblies: the operators seem to benefit from the expert video aid. As soon as the steady state is reached, a slower and statistically significant effect appears to favor the learning processes of the operators who do not receive any training aid. Since the steady state of the learning curves represents the long term production efficiency of the operators, the latter effect might require more attention from industry and researchers.

  • 4.
    de Giorgio, Andrea
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production engineering. Artificial Engineering, Via del Rione Sirignano 10, 80121 Naples, Italy.
    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.
    Romero, Mario
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Adopting extended reality?: A systematic review of manufacturing training and teaching applications2023In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 71, p. 645-663Article in journal (Refereed)
    Abstract [en]

    The training of future experts and operators in manufacturing engineering relies on understanding procedural processes that require applied practice. Yet, current manufacturing education and training overwhelmingly continues to depend on traditional pedagogical methods that segregate theoretical studies and practical training. While educational institutes have generally improved theoretical studies, they often lack facilities and labs to properly reproduce the working environments necessary for practice. Even in industrial settings, it is difficult, if not impossible, to halt the actual production lines to train new operators. Recently, applications with extended reality (XR) technologies, such as virtual, augmented, or mixed reality, reached a mature technology readiness level. With this technological advancement, we can envision a transition to a new teaching paradigm that exploits simulated learning environments. Thus, it becomes possible to bridge the gap between theory and practice for both students and industrial trainees. This article presents a systematic literature review of the main applications of XR technologies in manufacturing education, their goals and technology readiness levels, and a comprehensive overview of the development tools and experimental strategies deployed. This review contributes: (1) a state-of-the-art description of current research in XR education for manufacturing systems, and (2) a comprehensive analysis of the technological platforms, the experimental procedures and the analytical methodologies deployed in the body of literature examined. It serves as a guide for setting up and executing experimental designs for evaluating interventions of XR in manufacturing education and training.

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  • 5.
    Iop, Alessandro
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). Karolinska Univ Hosp, Dept Neurosurg, S-14186 Stockholm, Sweden.;Karolinska Inst, Dept Clin Neurosci, S-17177 Stockholm, Sweden.
    El-Hajj, Victor Gabriel
    Karolinska Univ Hosp, Dept Neurosurg, S-14186 Stockholm, Sweden.;Karolinska Inst, Dept Clin Neurosci, S-17177 Stockholm, Sweden..
    Gharios, Maria
    Karolinska Univ Hosp, Dept Neurosurg, S-14186 Stockholm, Sweden.;Karolinska Inst, Dept Clin Neurosci, S-17177 Stockholm, Sweden..
    de Giorgio, Andrea
    Univ Luxembourg, SnT Interdisciplinary Ctr Secur Reliabil & Trust, L-4365 Esch Sur Alzette, Luxembourg..
    Monetti, Fabio Marco
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Digital Smart Production.
    Edström, Erik
    Karolinska Univ Hosp, Dept Neurosurg, S-14186 Stockholm, Sweden.;Karolinska Inst, Dept Clin Neurosci, S-17177 Stockholm, Sweden..
    Elmi-Terander, Adrian
    Karolinska Univ Hosp, Dept Neurosurg, S-14186 Stockholm, Sweden.;Karolinska Inst, Dept Clin Neurosci, S-17177 Stockholm, Sweden..
    Romero, Mario
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Extended Reality in Neurosurgical Education: A Systematic Review2022In: Sensors, E-ISSN 1424-8220, Vol. 22, no 16, article id 6067Article, review/survey (Refereed)
    Abstract [en]

    Surgical simulation practices have witnessed a rapid expansion as an invaluable approach to resident training in recent years. One emerging way of implementing simulation is the adoption of extended reality (XR) technologies, which enable trainees to hone their skills by allowing interaction with virtual 3D objects placed in either real-world imagery or virtual environments. The goal of the present systematic review is to survey and broach the topic of XR in neurosurgery, with a focus on education. Five databases were investigated, leading to the inclusion of 31 studies after a thorough reviewing process. Focusing on user performance (UP) and user experience (UX), the body of evidence provided by these 31 studies showed that this technology has, in fact, the potential of enhancing neurosurgical education through the use of a wide array of both objective and subjective metrics. Recent research on the topic has so far produced solid results, particularly showing improvements in young residents, compared to other groups and over time. In conclusion, this review not only aids to a better understanding of the use of XR in neurosurgical education, but also highlights the areas where further research is entailed while also providing valuable insight into future applications.

  • 6.
    Maffei, Antonio
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Mura, Michela Dalle
    Univ Pisa, Dept Civil & Ind Engn, I-56122 Pisa, Largo Lazzarino, Italy..
    Monetti, Fabio Marco
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Boffa, Eleonora
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Dynamic Mixed Reality Assembly Guidance Using Optical Recognition Methods2023In: Applied Sciences, E-ISSN 2076-3417, Vol. 13, no 3, article id 1760Article in journal (Refereed)
    Abstract [en]

    Augmented (AR) and Mixed Reality (MR) technologies are enablers of the Industry 4.0 paradigm and are spreading at high speed in production. Main applications include design, training, and assembly guidance. The latter is a pressing concern, because assembly is the process that accounts for the biggest portion of total cost within production. Teaching and guiding operators to assemble with minimal effort and error rates is pivotal. This work presents the development of a comprehensive MR application for guiding novice operators in following simple assembly instructions. The app follows innovative programming logic and component tracking in a dynamic environment, providing an immersive experience that includes different guidance aids. The application was tested by experienced and novice users, data were drawn from the performed experiments, and a questionnaire was submitted to collect the users' perception. Results indicate that the MR application was easy to follow and even gave confidence to inexperienced subjects. The guidance support was perceived as useful by the users, though at times invasive in the field of view. Further development effort is required to draw from this work a complete and usable architecture for MR application in assembly, but this research forms the basis to achieve better, more consistent instructions for assembly guidance based on component tracking.

  • 7. Mo, Fan
    et al.
    Chaplin, Jack C.
    Sanderson, David
    Rehman, Hamood Ur
    Monetti, Fabio Marco
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Digital Smart Production.
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Digital Smart Production.
    Ratchev, Svetan
    A Framework for Manufacturing System Reconfiguration Based on Artificial Intelligence and Digital Twin2022In: Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus: Proceedings of FAIM 2022, June 19–23, 2022, Detroit, Michigan, USA / [ed] Kyoung-Yun Kim, Leslie Monplaisir, Jeremy Rickli, Detroit, MI: Springer Nature , 2022Conference paper (Refereed)
    Abstract [en]

    The application of digital twins and artificial intelligence to manufacturing has shown potential in improving system resilience, responsiveness, and productivity. Traditional digital twin approaches are generally applied to single, static systems to enhance a specific process. This paper proposes a framework that applies digital twins and artificial intelligence to manufacturing system reconfiguration, i.e., the layout, process parameters, and operation time of multiple assets, to enable system decision making based on varying demands from the customer or market. A digital twin environment has been developed to simulate the manufacturing process with multiple industrial robots performing various tasks. A data pipeline is built in the digital twin with an API (application programming interface) to enable the integration of artificial intelligence. Artificial intelligence methods are used to optimise the digital twin environment and improve system decision-making. Finally, a multi-agent program approach shows the communication and negotiation status between different agents to determine the optimal configuration for a manufacturing system to solve varying problems. Compared with previous research, this framework combines distributed intelligence, artificial intelligence for decision making, and production line optimisation that can be widely applied in modern reactive manufacturing applications.

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  • 8.
    Mo, Fan
    et al.
    Institute for Advanced Manufacturing, University of Nottingham, Nottingham, NG8 1BB, UK.
    Monetti, Fabio Marco
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Torayev, Agajan
    Institute for Advanced Manufacturing, University of Nottingham, Nottingham, NG8 1BB, UK.
    Rehman, Hamood Ur
    Institute for Advanced Manufacturing, University of Nottingham, Nottingham, NG8 1BB, UK;TQC Automation Ltd., Nottingham, NG3 2NJ, UK.
    Mulet Alberola, Jose A.
    Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council (CNR), Milan, 20133, Italy;Department of Mechanical and Industrial Engineering, University of Brescia, Piazza del Mercato, 15, 25121, Brescia, Italy.
    Rea Minango, Nathaly
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Nguyen, Hien Ngoc
    Design Innovation Center (DBZ) - Faculty of Engineering, Mondragon Unibertsitatea, Arrasate-Mondragon, 20500, Spain.
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Chaplin, Jack C.
    Institute for Advanced Manufacturing, University of Nottingham, Nottingham, NG8 1BB, UK.
    A maturity model for the autonomy of manufacturing systems2023In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 126, no 1-2, p. 405-428Article in journal (Refereed)
    Abstract [en]

    Modern manufacturing has to cope with dynamic and changing circumstances. Market fluctuations, the effects caused by unpredictable material shortages, highly variable product demand, and worker availability all require system robustness, flexibility, and resilience. To adapt to these new requirements, manufacturers should consider investigating, investing in, and implementing system autonomy. Autonomy is being adopted in multiple industrial contexts, but divergences arise when formalizing the concept of autonomous systems. To develop an implementation of autonomous manufacturing systems, it is essential to specify what autonomy means, how autonomous manufacturing systems are different from other autonomous systems, and how autonomous manufacturing systems are identified and achieved through the main features and enabling technologies. With a comprehensive literature review, this paper provides a definition of autonomy in the manufacturing context, infers the features of autonomy from different engineering domains, and presents a five-level model of autonomy — associated with maturity levels for the features — to ensure the complete identification and evaluation of autonomous manufacturing systems. The paper also presents the evaluation of a real autonomous system that serves as a use-case and a validation of the model.

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  • 9.
    Mo, Fan
    et al.
    Univ Nottingham, Inst Adv Mfg, Nottingham NG8 1BB, Notts, England..
    Rehman, Hamood Ur
    Univ Nottingham, Inst Adv Mfg, Nottingham NG8 1BB, Notts, England.;TQC Automation Ltd, Nottingham NG3 2NJ, Notts, England..
    Monetti, Fabio Marco
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Chaplin, Jack C.
    Univ Nottingham, Inst Adv Mfg, Nottingham NG8 1BB, Notts, England..
    Sanderson, David
    Univ Nottingham, Inst Adv Mfg, Nottingham NG8 1BB, Notts, England..
    Popov, Atanas
    Univ Nottingham, Inst Adv Mfg, Nottingham NG8 1BB, Notts, England..
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Ratchev, Svetan
    Univ Nottingham, Inst Adv Mfg, Nottingham NG8 1BB, Notts, England..
    A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence2023In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 82, p. 102524-, article id 102524Article in journal (Refereed)
    Abstract [en]

    Digital twins and artificial intelligence have shown promise for improving the robustness, responsiveness, and productivity of industrial systems. However, traditional digital twin approaches are often only employed to augment single, static systems to optimise a particular process. This article presents a paradigm for combining digital twins and modular artificial intelligence algorithms to dynamically reconfigure manufacturing systems, including the layout, process parameters, and operation times of numerous assets to allow system decision -making in response to changing customer or market needs. A knowledge graph has been used as the enabler for this system-level decision-making. A simulation environment has been constructed to replicate the manufacturing process, with the example here of an industrial robotic manufacturing cell. The simulation environment is connected to a data pipeline and an application programming interface to assist the integration of multiple artificial intelligence methods. These methods are used to improve system decision-making and optimise the configuration of a manufacturing system to maximise user-selectable key performance indicators. In contrast to previous research, this framework incorporates artificial intelligence for decision -making and production line optimisation to provide a framework that can be used for a wide variety of manufacturing applications. The framework has been applied and validated in a real use case, with the automatic reconfiguration resulting in a process time improvement of approximately 10%.

  • 10.
    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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  • 11.
    Monetti, Fabio Marco
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Digital Smart Production.
    Boffa, Eleonora
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Digital Smart Production.
    de Giorgio, Andrea
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Digital Smart Production.
    The Impact of Learning Factories on Teaching Lean Principles in an Assembly Environment2022In: Proceedings of FAIM 2022: Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus / [ed] Kyoung-Yun Kim, Leslie Monplaisir, Jeremy Rickli, Detroit, MI: Springer Nature , 2022, p. 271-283Conference paper (Refereed)
    Abstract [en]

    Learning factories are realistic manufacturing environments built for education; many universities have recently introduced learning factories in engineering programs to tackle real industrial problems; however, statistical studies on its effectiveness are still scarce. This paper presents a statistical study on the impact of learning factories on the students’ learning process, when teaching the lean manufacturing concepts in an assembly environment. The analysis is carried out through the Lean Manufacturing Lab at KTH, a learning factory supporting the traditional educational activities. In the lab, the students assemble a product on an assembly line; during three rounds, they identify problems on the line, apply the appropriate lean tools to overcome the problems, and try to achieve a higher productivity. The study is based on the analysis of the times recorded during the sessions of the lab. A questionnaire submitted to the students after the course evaluates the level of knowledge of lean production principles that the students achieved. The results are twofold: the improvement of the assembly times through the implementation of the lean tools and the positive effect of a hands-on experience on the students’ understanding of the lean principles, highlighted by the answers to the questionnaire. The main contributions are that applying the lean tools on an assembly line improves the productivity even with inexperienced operators, implementing a learning factory is effective in enhancing the learning process, and, lastly, that a first-hand experience applying the lean tools in a real assembly environment is an added value to the students’ education.

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  • 12.
    Monetti, Fabio Marco
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Digital Smart Production.
    de Giorgio, Andrea
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Digital Smart Production.
    Industrial transformation and assembly technology: context and research trends2022In: Procedia CIRP: Leading manufacturing systems transformation – Proceedings of the 55th CIRP Conference on Manufacturing Systems 2022 / [ed] Emanuele Carpanzano, Claudio Boër, Anna Valente, Lugano, Switzerland: Elsevier BV , 2022, Vol. 107, p. 1427-1432Conference paper (Refereed)
    Abstract [en]

    The fourth industrial revolution is based on a few technological advancements that promise an industrial transformation based on achieving sharing and circular economies. Selecting and applying these advancements correctly, i.e., following relevant value drivers, is a key to the success of manufacturing firms. This results in an increasing body of knowledge from academy and practitioners in the domain of the adoption of digital technology in industry. Given the breadth of the topic, the literature deals with both a vast amount of promising technologies and related existing and prospect industrial application. This work focuses on the contributions in the production sub-domain of assembly systems and technology. In detail, relevant high-impact scientific and engineering works have been identified and analyzed with the purpose of highlighting the innovation patterns in term of the prominent technological advancement (push) and related application (pull). The results of the present study show that the most relevant areas of research are: (1) the Industrial Internet of Things, (2) Augmented and Virtual Reality as assistance to the assembly and applied to the training of operators, and (3) the horizontal and vertical system integration through Digital Twins (DT) and Cyber Physical Systems (CPS). The prominent value drivers are the improvement of resources and processes as well as asset utilization and labor. Moreover, this work represents a first step towards a unitary framework to synchronize different research efforts in the domain of assembly and support the envisaged green industrial transformation.

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  • 13.
    Monetti, Fabio Marco
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Digital Smart Production.
    de Giorgio, Andrea
    SnT, University of Luxembourg, Luxembourg.
    Yu, Haisheng
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Romero, Mario
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    An experimental study of the impact of virtual reality training on manufacturing operators on industrial robotic tasks2022In: Procedia CIRP, Elsevier BV , 2022, Vol. 106, p. 33-38Conference paper (Refereed)
    Abstract [en]

    Despite the recent increase in Virtual Reality (VR) technologies employed for training manufacturing operators on industrial robotic tasks, the impact of VR methods compared to traditional ones is still unclear. This paper presents an experimental comparison of the two training approaches, with novice operators performing the same manufacturing tasks with a VR robot and with a real robot. The hardware selected is an ABB IRB 120 industrial robot, a HTC Vive head mounted display to operate it, besides a corresponding VR model developed in Unity. Twenty-four students performed two actions — drawing and “pick and place” -– in tasks with increasing difficulty, with both the VR model and the real robot. Completion time and task pass rate are adopted to estimate the learning efficiency, while a questionnaire evaluates the users’ satisfaction. The results show that students using VR overall need less elapsed time to complete all tasks, and they record a higher pass rate. The questionnaire answers show that 83% of participants find the VR model helpful in familiarizing with the real robot, and 75% are in favor of using the virtual tool for training novice operators. Users also report that moving the real robot is more complex than the virtual one; adjusting the speed is harder and the possibility of causing damage is worrisome, whereas the VR robot feels safer to operate and easier to drive. The majority of students are satisfied with the design of the tasks, and feel content with the experience. The main finding is that learning from a VR model allows to master driving a real robot quickly and easily. VR training is more useful than conventional methods because it reduces the learning time, allows for training without hindering production, lowers the risk perception, and improves safety for operators and industrial equipment.

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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.

    Download full text (pdf)
    fulltext
  • 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.
    Theissen, Nikolas Alexander
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Manufacturing and Metrology Systems.
    Monetti, Fabio Marco
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Digital Smart Production.
    Gonzalez, Monica
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Manufacturing and Metrology Systems.
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Towards quasi-static kinematic calibration of serial articulated industrial manipulators2022In: MED 2022 30th Mediterranean Conference on Control and Automation, Institute of Electrical and Electronics Engineers (IEEE), 2022, p. 872-877Conference paper (Refereed)
    Abstract [en]

    Research on kinematic calibration of industrial robots has focused on applying different measurement instruments into open- and closed-loop approaches and optimising calibration configurations through various cost functions. Such ways are either expensive or time-consuming. This work presents essential steps towards realising quasi-static kinematic calibration of industrial manipulators. This approach employs measurement data from a quasi-static measurement instead of a static one to identify the model parameters and has the potential of considerably reducing the measurement phase time during calibration. The focus lies on the technological challenges needed to achieve a successful quasi-static kinematic calibration, such as the trajectory generation, the measurement instrument and the controller data synchronisation. A case study assess the data obtained from a quasi-static kinematic measurement with a robot/tracker configuration of 100 mm/s and 100 Hz. The average positioning accuracy is similar for the static and the quasi-static measurement. The time for the quasi-static trajectory is reduced to almost one-third of the static trajectory time without considering the setup time.

1 - 17 of 17
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