Special Issues
Table of Content

Applications of Artificial Intelligence in Smart Manufacturing

Submission Deadline: 01 July 2025 (closed) View: 1609 Submit to Journal

Guest Editors

Dr. Prince Waqas Khan, West Virginia University, USA
Dr. Thorsten Wuest, West Virginia University, USA
Dr. Imran, King Fahd University of Petroleum and Minerals, Saudi Arabia

Summary

Smart manufacturing, also called Industry 4.0, involves combining advanced technologies like artificial intelligence (AI), Machine Learning (ML), Internet of Things (IoT), and cyber-physical systems in manufacturing in order to improve efficiency, productivity, and flexibility. AI has a lot of practical applications in smart manufacturing. It is essential in facilitating intelligent decision-making, predictive maintenance, process optimization, and automated quality control for smart manufacturing. This special issue aims to gather the most recent advancements in applications of AI and ML within Smart Manufacturing. The special issue will cover various topics, including but not limited to:


· ML and deep learning techniques for predictive maintenance and condition monitoring

· AI-powered process optimization and control for efficient resource utilization

· Computer vision and robotics applications in smart manufacturing

· AI-driven supply chain management and logistics optimization

· Integration of AI with IoT, cyber-physical systems, and digital twins

· AI-based quality control and defect detection

· Explainable AI and trustworthy AI for transparent decision-making

· Case studies and real-world applications of AI in various manufacturing domains

· AI for cybersecurity in Industry 4.0

· AI for cyber resilience in Industry 4.0

· AI for prediction of climate change from Industry 4.0


Keywords

Artificial Intelligence, Smart Manufacturing, Industry 4.0, Machine Learning, Deep Learning, Computer Vision, Robotics, Predictive Maintenance, Process Optimization, Supply Chain Management, Quality Control, Explainable AI, Trustworthy AI

Published Papers


  • Open Access

    ARTICLE

    Multi-Level Subpopulation-Based Particle Swarm Optimization Algorithm for Hybrid Flow Shop Scheduling Problem with Limited Buffers

    Yuan Zou, Chao Lu, Lvjiang Yin, Xiaoyu Wen
    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2305-2330, 2025, DOI:10.32604/cmc.2025.065972
    (This article belongs to the Special Issue: Applications of Artificial Intelligence in Smart Manufacturing)
    Abstract The shop scheduling problem with limited buffers has broad applications in real-world production scenarios, so this research direction is of great practical significance. However, there is currently little research on the hybrid flow shop scheduling problem with limited buffers (LBHFSP). This paper deeply investigates the LBHFSP to optimize the goal of the total completion time. To better solve the LBHFSP, a multi-level subpopulation-based particle swarm optimization algorithm (MLPSO) is proposed, which is founded on the attributes of the LBHFSP and the shortcomings of the basic PSO (particle swarm optimization) algorithm. In MLPSO, firstly, considering the… More >

  • Open Access

    ARTICLE

    An SAC-AMBER Algorithm for Flexible Job Shop Scheduling with Material Kit

    Bo Li, Xiaoying Yang, Zhijie Pei, Xin Yang, Yaqi Wu
    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3649-3672, 2025, DOI:10.32604/cmc.2025.066267
    (This article belongs to the Special Issue: Applications of Artificial Intelligence in Smart Manufacturing)
    Abstract It is well known that the kit completeness of parts processed in the previous stage is crucial for the subsequent manufacturing stage. This paper studies the flexible job shop scheduling problem (FJSP) with the objective of material kitting, where a material kit is a collection of components that ensures that a batch of components can be ready at the same time during the product assembly process. In this study, we consider completion time variance and maximum completion time as scheduling objectives, continue the weighted summation process for multiple objectives, and design adaptive weighted summation parameters… More >

  • Open Access

    ARTICLE

    Prediction of Assembly Intent for Human-Robot Collaboration Based on Video Analytics and Hidden Markov Model

    Jing Qu, Yanmei Li, Changrong Liu, Wen Wang, Weiping Fu
    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3787-3810, 2025, DOI:10.32604/cmc.2025.065895
    (This article belongs to the Special Issue: Applications of Artificial Intelligence in Smart Manufacturing)
    Abstract Despite the gradual transformation of traditional manufacturing by the Human-Robot Collaboration Assembly (HRCA), challenges remain in the robot’s ability to understand and predict human assembly intentions. This study aims to enhance the robot’s comprehension and prediction capabilities of operator assembly intentions by capturing and analyzing operator behavior and movements. We propose a video feature extraction method based on the Temporal Shift Module Network (TSM-ResNet50) to extract spatiotemporal features from assembly videos and differentiate various assembly actions using feature differences between video frames. Furthermore, we construct an action recognition and segmentation model based on the Refined-Multi-Scale… More >

  • Open Access

    ARTICLE

    AI-Driven Resource and Communication-Aware Virtual Machine Placement Using Multi-Objective Swarm Optimization for Enhanced Efficiency in Cloud-Based Smart Manufacturing

    Praveena Nuthakki, Pavan Kumar T., Musaed Alhussein, Muhammad Shahid Anwar, Khursheed Aurangzeb, Leenendra Chowdary Gunnam
    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4743-4756, 2024, DOI:10.32604/cmc.2024.058266
    (This article belongs to the Special Issue: Applications of Artificial Intelligence in Smart Manufacturing)
    Abstract Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manufacturing environments, enabling scalable and flexible access to remote data centers over the internet. In these environments, Virtual Machines (VMs) are employed to manage workloads, with their optimal placement on Physical Machines (PMs) being crucial for maximizing resource utilization. However, achieving high resource utilization in cloud data centers remains a challenge due to multiple conflicting objectives, particularly in scenarios involving inter-VM communication dependencies, which are common in smart manufacturing applications. This manuscript presents an AI-driven approach utilizing a modified Multi-Objective Particle More >

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