Envisioning maintenance 5.0: Insights from a systematic literature review of Industry 4.0 and a proposed framework

Psarommatis, Foivos; May, Gökan; Azamfirei, Victor

doi:10.1016/j.jmsy.2023.04.009

Cited by 42 publications

(14 citation statements)

References 295 publications

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“…Furthermore, the swift expansion of interconnected devices and sensors, along with enhanced computing capabilities, has significantly eased the real-time collection and analysis of data. This, in turn, is crucial for predictive maintenance, optimizing production processes, and improving manufacturing decision-making [11]. Industry 1.0 to 4.0 represent four stages of industrial development.…”

Section: Definition and Evolution Of Industry 40mentioning

confidence: 99%

Application of Digital Twin in Electric Vehicle Powertrain: A Review

Li,

Niu,

Tian

2024

WEVJ

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Digital Twin (DT) is widely regarded as a highly promising technology with the potential to revolutionize various industries, making it a key trend in the Industry 4.0 era. In a cost-effective and risk-free setting, digital twins facilitate the interaction and merging of the physical and informational realms. The application of digital twins spans across different sectors, including aerospace, healthcare, smart manufacturing, and smart cities. As electric vehicles have experienced rapid growth, there is a growing demand for the development of innovative technologies. One potential area for digital twins application is within the automotive sector. The powertrain system of electric vehicles (EVs) consists of three parts, power source, power electronic system, and electric motor, which are considered as the core components of electric vehicles. The focus of this paper is to conduct a methodical review regarding the use of digital twins in the powertrain of electric vehicles (EVs). While reviewing the development of digital twin technology, its main application scenarios and its use in electric vehicle powertrains are analysed. Finally, the digital twins currently encounter several challenges that need to be addressed, and so the future development of their application to electric vehicles are summarized.

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Section: Definition and Evolution Of Industry 40mentioning

confidence: 99%

Application of Digital Twin in Electric Vehicle Powertrain: A Review

Li,

Niu,

Tian

2024

WEVJ

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“…The entire energy consumption and effectiveness of AM processes are significantly influenced by machine consumables and maintenance [205]. However, the extant research does not go into great detail on these aspects.…”

Section: Machine Consumables and Maintenancementioning

confidence: 99%

“…The energy implications of routine maintenance and calibration should also be investigated, as well as the energy usage of machine consumables such print heads, nozzles, and build plates. The reduction in energy consumption in AM might be considerably aided by the identification of energy-efficient consumables and maintenance techniques [205].…”

Section: Machine Consumables and Maintenancementioning

confidence: 99%

Maximizing Energy Efficiency in Additive Manufacturing: A Review and Framework for Future Research

May

Psarommatis

2023

Energies

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Additive manufacturing (AM) offers unique capabilities in terms of design freedom and customization, contributing to sustainable manufacturing. However, energy efficiency remains a challenge in the widespread adoption of AM processes. In this paper, we present a comprehensive review of the current research on energy efficiency in AM, addressing challenges, opportunities, and future directions. Our analysis reveals a lack of standardization in the measurement and reporting of energy consumption, making it difficult to evaluate and compare the energy performance of various systems. We propose a holistic framework to address energy efficiency throughout the entire life cycle of the AM process, highlighting the importance of design optimization, material selection, advanced control systems, and energy management strategies. The paper also emphasizes the need for further research on the interactions between process parameters, along with the potential of integrating renewable energy sources into AM systems. This review offers valuable insights for both academics and industry professionals, calling for standardized methodologies and a focus on energy management to optimize energy efficiency in AM processes, ultimately enhancing competitiveness and sustainability in modern manufacturing.

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“…In the maintenance and repair sector, AR could support the traditional method of work and ease the process. Various factors such as the rising number of AR hardware, as well as the lower cost of development, are expected to drive up innovations in this area [16]. For instance, a cloud-based database was developed to collect information about equipment maintenance and inspection.…”

Section: Ar Technologies In Maintenance and Repairmentioning

confidence: 99%

Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer

Alatawi,

Albalawi,

Shahata

et al. 2023

Sensors

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The use of augmented reality (AR) technology is growing in the maintenance industry because it can improve efficiency and reduce costs by providing real-time guidance and instruction to workers during repairs and maintenance tasks. AR can also assist with equipment training and visualization, allowing users to explore the equipment’s internal structure and size. The adoption of AR in maintenance is expected to increase as hardware options expand and development costs decrease. To implement AR for job aids in mobile applications, 3D spatial information and equipment details must be addressed, and calibrated using image-based or object-based tracking, which is essential for integrating 3D models with physical components. The present paper suggests a system using AR-assisted deep reinforcement learning (RL)-based model for NanoDrop Spectrophotometer training and maintenance purposes that can be used for rapid repair procedures in the Industry 4.0 (I4.0) setting. The system uses a camera to detect the target asset via feature matching, tracking techniques, and 3D modeling. Once the detection is completed, AR technologies generate clear and easily understandable instructions for the maintenance operator’s device. According to the research findings, the model’s target technique resulted in a mean reward of 1.000 and a standard deviation of 0.000. This means that all the rewards that were obtained in the given task or environment were exactly the same. The fact that the reward standard deviation is 0.000 shows that there is no variability in the outcomes.

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Envisioning maintenance 5.0: Insights from a systematic literature review of Industry 4.0 and a proposed framework

Cited by 42 publications

References 295 publications

Application of Digital Twin in Electric Vehicle Powertrain: A Review

Application of Digital Twin in Electric Vehicle Powertrain: A Review

Maximizing Energy Efficiency in Additive Manufacturing: A Review and Framework for Future Research

Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer

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