Review on Artificial Intelligence Applications in Manufacturing Industrial Supply Chain – Industry 4.0's Perspective

Teerasoponpong, Siravat; Sugunnasil, Prompong

doi:10.1109/ectidamtncon53731.2022.9720417

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…The contemporary industrial landscape, particularly in the era of Industry 4.0, has witnessed a significant transformation driven by the integration of advanced technologies such as artificial intelligence (AI), 5G, and mechatronics. This integration has revolutionized the manufacturing industry, leading to the development of intelligent robotic systems, smart manufacturing processes, and industrial automation, fundamentally altering the dynamics of the industrial supply chain (Teerasoponpong & Sugunnasil, 2022). The application of AI in manufacturing extends beyond the factory floor, impacting the entire supply chain, thereby creating a more intelligent manufacturing environment.…”

Section: Scope and Relevance In Contemporary Industrial Applicationsmentioning

confidence: 99%

Mechatronics in modern industrial applications: Delving into the integration of electronics, mechanics, and informatics

Abimbola Oluwatoyin Adegbite,

Chinedu Nnamdi Nwasike,

Nwabueze Kelvin Nwaobia

et al. 2023

World J. Adv. Res. Rev.

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This study delves into the integration of electronics, mechanics, and informatics within the realm of mechatronics, emphasizing its pivotal role in modern industrial applications. The primary objective is to dissect the synergistic interplay of these disciplines and assess their collective impact on various industrial sectors. Employing a systematic literature review as the core methodology, the research draws on a wide array of peer-reviewed articles and academic journals, focusing on publications from the last two decades to ensure relevance and contemporaneity. Key findings reveal that mechatronics has significantly evolved from its conceptual inception to a cornerstone technology in industries such as manufacturing, automotive, and healthcare. The study highlights the advancements in mechatronic systems, including the integration of AI and IoT, which have led to enhanced efficiency, precision, and adaptability in industrial processes. Furthermore, the research identifies the economic, technological, and environmental impacts of these systems, underscoring their role in driving innovation and sustainability. The study concludes with actionable recommendations for industry stakeholders and policymakers, emphasizing the need for continuous investment in R&D, workforce training, and the development of ethical standards and regulations. It also proposes future research directions, particularly focusing on the scalability, sustainability, and long-term societal impacts of mechatronic systems. This research contributes to a deeper understanding of mechatronics in the industrial domain, offering insights into its current applications and future potential.

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Section: Scope and Relevance In Contemporary Industrial Applicationsmentioning

confidence: 99%

Mechatronics in modern industrial applications: Delving into the integration of electronics, mechanics, and informatics

Abimbola Oluwatoyin Adegbite,

Chinedu Nnamdi Nwasike,

Nwabueze Kelvin Nwaobia

et al. 2023

World J. Adv. Res. Rev.

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“…ML is further divided into supervised learning, unsupervised learning, and reinforced learning for use in power electronics. Some of the research trends and an overview of artificial intelligence in power electronics can be found in [22][23][24][25][26][27][28][29][30].…”

Section: Motivation and Related Workmentioning

confidence: 99%

Towards Data-Driven Fault Diagnostics Framework for SMPS-AEC Using Supervised Learning Algorithms

Kareem

Hur

2022

Electronics

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The service life of aluminium electrolytic capacitors is becoming a critical design factor in power supplies. Despite rising power density demands, electrolytic capacitors and switching devices are the two most common parts of the power supply that age (deteriorate) under normal and diverse working conditions. This study presents a fault diagnostics framework integrated with long-term frequency for a switched-mode power supply aluminium electrolytic capacitor (SMPS-AEC). Long-term frequency condition monitoring (CM) was achieved using the advanced HIOKI LCR meter at 8 MHz. The data acquired during the experimental study can help to achieve the needed paradigm from various measured characteristics of the SMPS/power converter component to detect anomalies between the capacitors selected for analysis. The CM procedure in this study was bound by the electrical parameters—capacitance (Cs), equivalent series resistance (ESR), dissipation factor (DF), and impedance (Z)—-acting as degradation techniques during physical and chemical changes of the capacitors. Furthermore, the proposed methodology was carried out using statistical feature extraction and filter-based correlation for feature selection, followed by training, testing and validation using the selected supervised learning algorithms. The resulting assessment revealed that with increased data capacity, an improved performance was achieved across the chosen algorithms out of which the k-nearest neighbors (KNN) had the best average accuracy (98.40%) and lowest computational cost (0.31 s) across all the electrical parameters. Further assessment was carried out using the fault visualization aided by principal component analysis (PCA) to validate and decide on the best electrical parameters for the CM technique.

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Cognitive Digital Twin in Manufacturing: A Heuristic Optimization Approach

Rehman

Ahmed

Begum

2023

IFIP Advances in Information and Communication Technology

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Review on Artificial Intelligence Applications in Manufacturing Industrial Supply Chain – Industry 4.0's Perspective

Cited by 4 publications

References 25 publications

Mechatronics in modern industrial applications: Delving into the integration of electronics, mechanics, and informatics

Mechatronics in modern industrial applications: Delving into the integration of electronics, mechanics, and informatics

Towards Data-Driven Fault Diagnostics Framework for SMPS-AEC Using Supervised Learning Algorithms

Cognitive Digital Twin in Manufacturing: A Heuristic Optimization Approach

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