Deep Vision in Smart Manufacturing: MODERN Framework for Intelligent Quality Monitoring and Diagnosis

Predictive Maintenance in Smart Manufacturing: A Deep Learning Approach for Equipment Anomaly Forecasting

Automated visual defect detection has become an important technology for improving quality control in modern industrial manufacturing. Traditional manual inspection is costly, inefficient, subjective, and difficult to sustain in high-speed production, while rule-based vision systems often fail under changing lighting conditions, surface variations, and new defect types. This paper reviews the development of AI-powered visual defect detection, focusing on machine learning, convolutional neural networks, and YOLO-based real-time detection methods. It explains how deep learning replaces hand-crafted feature design with data-driven representation learning, enabling more accurate recognition of scratches, cracks, pits, edge defects, and other surface anomalies. The paper further discusses a physics-assisted framework that combines heat conduction modelling for synthetic defect generation, anisotropic diffusion for structure-preserving image preprocessing, and gradient descent analysis for training stabilisation under imbalanced datasets. Although these methods improve detection accuracy, robustness, and real-time performance, challenges remain in defect data scarcity, environmental domain shifts, model interpretability, computational cost, and edge deployment. Future research should focus on lightweight models, open datasets, explainable AI, and physics-informed learning.

Multimodal Sensor Data Fusion for Robust Deep Learning Models in Manufacturing Applications

Hybrid deep learning approaches for multiple fault diagnostics in intelligent manufacturing systems

Early detection of surface defects in smart systems within industrial production processes is critical for automating quality control processes, reducing costs, and maintaining quality standards. Smart manufacturing systems enable the detection of different types of defects through multiple defect diagnosis. With the increase in data volume on production lines due to Industry 4.0, classical machine learning approaches have proven insufficient for defect diagnosis. This has led to a growing need for more advanced analysis methods. In this thesis, hybrid deep learning architectures are used to classify different types of defective data samples with high accuracy. This thesis examines hybrid deep learning approaches for identifying multiple defects on metal and fabric surfaces. In the proposed method, different deep learning architectures are used together to perform both object detection and segmentation. In this study, features obtained from defective images are extracted using convolutional neural networks, and then these features are analyzed with different classifiers. The deep architectures used in the classification phase are innovative approaches such as Vision Transformer (ViT) and attention-based deep neural networks. Experimental studies have shown that the proposed method achieves high performance in detecting different types of failures. This study contributes to the improvement of quality control processes in intelligent manufacturing systems.

A systematic review of deep learning based machine vision for quality control in industry 4.0

The increasing need for automated quality control techniques has been driven by the rapid advancement of smart manufacturing systems within the Industry 4.0 framework. Machine vision using deep learning technologies has emerged as a key enabler of streamlined inspection and decision-making workflows across various production environments. This systematic review analyzes the evolution of deep learning models from traditional Convolutional Neural Networks (CNNs) to transformer-based architectures, examining how these developments address challenges in defect detection and classification on modern industrial production lines. Several key findings emerge from our analysis. First, while CNNs continue to dominate due to well-established performance in local feature extraction, transformer models demonstrate superior accuracy for complex defect geometries through global contextual reasoning, albeit at higher computational costs. Second, real-time detection methods such as YOLO have proven viable for high-velocity production environments, though they typically sacrifice accuracy for speed. Third, integration with edge computing, digital twins, and IoT infrastructure is essential for developing scalable quality control systems. Additionally, ongoing challenges include data format inconsistency, model interpretability, domain transfer limitations, and the gap between controlled development conditions and actual factory environments. This review concludes by outlining future research priorities: developing hybrid CNN-Transformer models, establishing standard evaluation criteria, creating resource-efficient edge applications, and building collaborative human-AI frameworks, thereby providing guidance for researchers and engineers advancing automated visual inspection systems.

Experimental Evaluation of Deep Learning Enabled IoT Technology for Industrial Machinery Fault Detection and Performance Improvement

Industrial machinery fault detection is a cornerstone of predictive maintenance, directly influencing operational reliability, safety, and production efficiency. Conventional rule-based and machine learning approaches often struggle to handle non-stationary sensor signals, cross-machine variability, and early-stage fault manifestations. This paper proposes a Quantum-Inspired Neuro-Federated Spatio-Temporal Autoencoding Transformer (QNF-STAEFormer) to smartly, scalably and privately diagnose faults in industries. The model incorporates self-adaptive multi-modal sensing, neuromorphic event-driven signal conditioning, physics-directed multi-resolution decomposition, graph-wavelet spatio-temporal encoding, hyperdimensional latent representation learning, and self-supervised predictive modeling. A quantum-inspired reasoning layer facilitates the parallel consideration of various hypotheses on faults and a neuro-federated learning policy supports decentralized collaborative learning at industrial locations. The experimental analysis reveals that the proposed framework has a general fault classification error of 98.4%, F1-score, 98.1%, and AUC, 99.0% which is better than standard CNN, LSTM and transformer-based baselines. The model also has a high early-fault detection ability, where it has a 98.5% detected rate at a 60-minute prediction horizon. These findings prove that the proposed architecture can be used as a strong, precise, and future-proof solution to intelligent monitoring of industrial conditions.

Edge AI-Based Autonomous Monitoring System for Smart Manufacturing Environments

An Edge AI-based autonomous monitoring framework that integrates Industrial Internet of Things sensors, edge computing, deep learning models, and cloud platforms for efficient industrial monitoring that improves prediction accuracy, minimizes downtime, enhances product quality, strengthens cybersecurity, and supports sustainable manufacturing.