Advanced Manufacturing in Industry 5.0: A Survey of Key Enabling Technologies and Future Trends

Wang, Xiang; Yu, Kuanxin; Han, Fengling; Le, Fang; He, Dehua; Han, Qing‐Long

doi:10.1109/tii.2023.3274224

Cited by 46 publications

(18 citation statements)

References 68 publications

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“…Blockchain technology improves decision-making in payment and settlement processes by enabling faster, secure, and cost-effective transactions [36]. Traditional payment systems usually involve intermediaries, such as payment processors or banks, which can introduce delays and increase expenses [37].…”

Section: Payment and Settlementmentioning

confidence: 99%

Harnessing the power of blockchain technology to support decision-making in e-commerce processes

Al-Moghrabi,

Al-Ghonmein

2024

IJ-AI

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Technology, such as blockchain, has emerged as a promising solution for addressing the challenges of e-commerce decision-making. In this study, we explore the potential benefits of integrating blockchain technology into e-commerce and its role in supporting decision-making in e-commerce. We also examine blockchain’s benefits in terms of enhanced security, transparency, and efficiency for e-commerce platforms. Furthermore, the study discusses the challenges of implementing blockchain for e-commerce, including scalability, integration, regulatory frameworks, user experience, privacy, interoperability, and sustainability. By analyzing these challenges, the study provides valuable insights for future research and development efforts to facilitate a seamless adoption of blockchain technology in e-commerce decisions. Blockchain technology holds the potential to transform an e-commerce ecosystem by overcoming these challenges and unlocking its transformative potential.

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Section: Payment and Settlementmentioning

confidence: 99%

Harnessing the power of blockchain technology to support decision-making in e-commerce processes

Al-Moghrabi,

Al-Ghonmein

2024

IJ-AI

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“…Table 5 presents the influence of different cores and ranks on prediction accuracy. Employing the TT-SVD in Algorithm 1, when the input data consists of 14 dimensions, setting the approximate accuracy ε = 0.05 enables us to determine the range of TT rank and TT core that impact model parameters and training time which are (1,4) and (1,5), respectively. Based on the experimental findings, it can be concluded that Tensor-Train decomposition significantly contributes to parameter reduction and shorter training times.…”

Section: Ablation Analysis the Ablation Experiments In Tablementioning

confidence: 99%

“…Process intelligence plays a vital role in the polyester industry by facilitating the accurate prediction of crucial variables such as reaction rates, temperature profiles, and product quality [3]. This information allows for the understanding of trends, evaluation of system state, and early detection of any abnormalities [4]. With the availability of data acquisition and monitoring systems, significant volumes of data are being accumulated, creating ample opportunities for datadriven approaches to effectively model and optimize polyester production processes.…”

Section: Introductionmentioning

confidence: 99%

Multi-level deep domain adaptive adversarial model based on tensor-train decomposition for industrial time series forecasting

Yang,

Peng,

Chen

et al. 2023

Meas. Sci. Technol.

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The polyester industry is a complex process industry, building a time series prediction model for new production lines or equipment with new sensors can be challenging due to a lack of historical data. The time-series data collected from sensors cross-production-line often exhibit varying distributions. Current domain adaptation (DA) approaches in data-driven time series forecasting primarily concentrate on adjusting either the features or the models, neglecting the intricacies of industrial time series data. Furthermore, constructing deep neural networks for industrial data necessitates substantial computational resources and runtime due to their large and high-dimensional nature. In order to tackle these obstacles, we propose a novel Multi-level deep domain adaptive adversarial model based on tensor-train decomposition (TT-MDAM). Our model aims to strike a dynamic balance between prediction accuracy and runtime efficiency. By integrating multiple perspectives at the feature, trend, and model levels, we leverage DA to enhance the prediction accuracy of our model in the target domain. Additionally, by analyzing the weight matrix of the neural network, we generate a low-rank model to improve operational efficiency. The application of the proposed TT-MDAM approach to both the three-phase flow facility process (TPFF) dataset and a real-world polyester esterification process dataset reveals promising results, outperforming state-of-the-art methodologies in terms of prediction performance. The results indicate that the approach provides a viable solution for building time series prediction models in industrial processes with new equipment or production lines.

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“…The adoption of Industry 5.0 principles necessitates a paradigm shift in the way businesses operate (Turner and Oyekan, 2023). It requires the controlled and well-governed integration of cutting-edge technologies, such as Artificial Intelligence (AI), machine learning, and the Internet of Things (IoT), to create smart and interconnected systems that optimize resource utilization, reduce waste, and promote socio-environmentally responsible practices (Xian et al, 2024). Furthermore, Industry 5.0 demands a reimagining of corporate social responsibility, encompassing ethical business practices and active engagement in addressing global challenges, such as climate change, resource depletion, and social inequalities (Zhang and Li, 2023).…”

Section: Introductionmentioning

confidence: 99%

Beyond Industry 4.0: a systematic review of Industry 5.0 technologies and implications for social, environmental and economic sustainability

Ghobakhloo,

Iranmanesh,

Fathi

et al. 2024

APJBA

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PurposeThe study seeks to understand the possible opportunities that Industry 5.0 might offer for various aspects of inclusive sustainability. The study aims to discuss existing perspectives on the classification of Industry 5.0 technologies and their underlying role in materializing the sustainability values of this agenda.Design/methodology/approachThe study systematically reviewed Industry 5.0 literature based on the PRISMA protocol. The study further employed a detailed content-centric review of eligible documents and conducted evidence mapping to fulfill the research objectives.FindingsThe advancement of Industry 5.0 is currently underway, with noteworthy initial contributions enriching its knowledge base. Although a unanimous definition remains lacking, diverse viewpoints emerge concerning the recognition of fundamental technologies and the potential for yielding sustainable outcomes. The expected contribution of Industry 5.0 to sustainability varies significantly depending on the context and the nature of underlying technologies.Practical implicationsIndustry 5.0 holds the potential for advancing sustainability at both the firm and supply chain levels. It is envisioned to contribute proportionately to the three sustainability dimensions. However, the current discourse primarily dwells in theoretical and conceptual domains, lacking empirical exploration of its practical implications.Originality/valueThis study comprehensively explores diverse perspectives on Industry 5.0 technologies and their potential contributions to economic, environmental and social sustainability. Despite its promise, the practical evidence supporting the effectiveness of Industry 5.0 remains limited. Certain conditions are necessary to realize the benefits of Industry 5.0 fully, yet the mechanisms behind these conditions require further investigation. In this regard, the study suggests several potential areas for future research.

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Advanced Manufacturing in Industry 5.0: A Survey of Key Enabling Technologies and Future Trends

Cited by 46 publications

References 68 publications

Harnessing the power of blockchain technology to support decision-making in e-commerce processes

Harnessing the power of blockchain technology to support decision-making in e-commerce processes

Multi-level deep domain adaptive adversarial model based on tensor-train decomposition for industrial time series forecasting

Beyond Industry 4.0: a systematic review of Industry 5.0 technologies and implications for social, environmental and economic sustainability

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