A Study on Deep Learning Methods in the Concept of Digital Industry 4.0

Şimşek, Mehmet Ali; Orman, Zeynep

doi:10.4018/978-1-7998-5015-1.ch016

Cited by 4 publications

(1 citation statement)

References 34 publications

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“…Chaoub et al [25] emphasized the need for models that can perform well with limited data, particularly in the context of prognosis and health management of equipment. Şimşek et al [26] further emphasized the role of deep learning in solving various industrial challenges, focusing on the ability of machines to communicate and make decisions. Kapusi et al [27] provided a practical example of this, demonstrating the application of deep learning in real-time object detection for an industrial SCARA robot.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Developing a YOLOv3-Based Neural Network to Enable Digital Cameras to Detect Features on the Reflective Surface of Objects in Robotic Applications: A Low-Computational Approach with a New Public Dataset

Bajrami,

Palpaceli

2024

Preprint

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This study explores the integration of Artificial Intelligence (AI) and collaborative robotics in industrial surface finishing, focusing on feature detection on object reflective surfaces using neural networks in computationally constrained environments. Central to this investigation is the utilization of cameras considered as sensors that can capture detailed images of reflective surfaces. These images are then analyzed by a custom neural network, demonstrating the feasibility of feature detection with non-powerful computers. The public dataset for recognizing light reflection defects, available in the SPADD repository, supports this approach. Employing open-source tools like Darknet YOLOv3 and Yolo\_mark, this method proves particularly advantageous for small and medium-sized enterprises (SMEs) due to its low cost and maintenance requirements. This research highlights the challenges of AI and collaborative robotics in industrial applications, especially in adapting to varying conditions and the need for continuous training with new data coming from several sensors. Looking ahead, plans are in place to integrate this system with cobot-assisted polishing tasks, using cameras to enhance manufacturing efficiency and quality. This work contributes to the broader understanding of practical applications of AI in industrial automation, surface finishing processes, and the effective use of sensor technology.

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Section: Dataset Descriptionmentioning

confidence: 99%

Developing a YOLOv3-Based Neural Network to Enable Digital Cameras to Detect Features on the Reflective Surface of Objects in Robotic Applications: A Low-Computational Approach with a New Public Dataset

Bajrami,

Palpaceli

2024

Preprint

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Feature-Based Transfer Learning for IoT-Enabled Defect Detection for Quality Control in Industrial Manufacturing Processes: A DenseNet Evaluation

P. P. Abdul Majeed,

Ateeq,

et al. 2024

Lecture Notes in Networks and Systems

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From Dataset Creation to Defect Detection: A Proposed Procedure for a Custom CNN Approach for Polishing Applications on Low-Performance PCs

Bajrami,

Palpacelli

2024

Machines

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This study focuses on training a custom, small Convolutional Neural Network (CNN) using a limited dataset through data augmentation that is aimed at developing weights for subsequent fine-tuning on specific defects, namely improperly polished aluminum surfaces. The objective is to adapt the network for use in computationally restricted environments. The methodology involves using two computers—a low-performance PC for network creation and initial testing and a more powerful PC for network training using the Darknet framework—after which the network is transferred back to the initial low-performance PC. The results demonstrate that the custom lightweight network suited for a low-performance PC effectively performs object detection under the described conditions. These findings suggest that using tailored lightweight networks for recognizing specific types of defects is feasible and warrants further investigation to enhance the industrial defect detection processes in limited computational settings. This approach highlights the potential for deploying AI-driven quality control in environments with constrained hardware capabilities.

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A Study on Deep Learning Methods in the Concept of Digital Industry 4.0

Cited by 4 publications

References 34 publications

Developing a YOLOv3-Based Neural Network to Enable Digital Cameras to Detect Features on the Reflective Surface of Objects in Robotic Applications: A Low-Computational Approach with a New Public Dataset

Developing a YOLOv3-Based Neural Network to Enable Digital Cameras to Detect Features on the Reflective Surface of Objects in Robotic Applications: A Low-Computational Approach with a New Public Dataset

Feature-Based Transfer Learning for IoT-Enabled Defect Detection for Quality Control in Industrial Manufacturing Processes: A DenseNet Evaluation

From Dataset Creation to Defect Detection: A Proposed Procedure for a Custom CNN Approach for Polishing Applications on Low-Performance PCs

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