Towards a Comprehensive Visual Quality Inspection for Industry 4.0*

Rožanec, Jože M.; Zajec, Patrik; Trajkova, Elena; Šircelj, Beno; Brecelj, Bor; Novalija, Inna; Dam, Paulien; Fortuna, Blaž; Mladenić, Dunja

doi:10.1016/j.ifacol.2022.09.486

Cited by 11 publications

(2 citation statements)

References 14 publications

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“…Nowadays, the leading practice for quality inspection seems to be that of the visual examination which, in most cases, involves a human operator responsible for the overall controlling and calculation. The visual examination by operators results in several challenges such as limited scalability and operator-to-operator inconsistencies [27]. The shortcomings of these practices are already being noticed, with several vision-based inspection methods being proposed over the last few years [28].…”

Section: Human-robot Collaboration (Hrc) In Quality Inspection Processesmentioning

confidence: 99%

A Voice-Enabled ROS2 Framework for Human–Robot Collaborative Inspection

Papavasileiou,

Nikoladakis,

Basamakis

et al. 2024

Applied Sciences

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Quality inspection plays a vital role in current manufacturing practice since the need for reliable and customized products is high on the agenda of most industries. Under this scope, solutions enhancing human–robot collaboration such as voice-based interaction are at the forefront of efforts by modern industries towards embracing the latest digitalization trends. Current inspection activities are often based on the manual expertise of operators, which has been proven to be time-consuming. This paper presents a voice-enabled ROS2 framework towards enhancing the collaboration of robots and operators under quality inspection activities. A robust ROS2-based architecture is adopted towards supporting the orchestration of the process execution flow. Furthermore, a speech recognition application and a quality inspection solution are deployed and integrated to the overall system, showcasing its effectiveness under a case study deriving from the automotive industry. The benefits of this voice-enabled ROS2 framework are discussed and proposed as an alternative way of inspecting parts under human–robot collaborative environments. To measure the added value of the framework, a multi-round testing process took place with different parameters for the framework’s modules, showcasing reduced cycle time for quality inspection processes, robust HRI using voice-based techniques and accurate inspection.

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Section: Human-robot Collaboration (Hrc) In Quality Inspection Processesmentioning

confidence: 99%

A Voice-Enabled ROS2 Framework for Human–Robot Collaborative Inspection

Papavasileiou,

Nikoladakis,

Basamakis

et al. 2024

Applied Sciences

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“…Bendaouia et al used the real time montoring with ConvLSTM in flotation monitoting techniques for froth inspection [15]. Rozanec et al discussed the visual quality control with industry 4.0 [16]. They developed the automatic visual inspector.…”

Section: Literature Surveymentioning

confidence: 99%

Improving Quality Inspections with Image Analysis and Artificial Intelligence

2023

acccm

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This study delves into the pivotal role of artificial intelligence (AI) and image processing techniques in revolutionizing quality control processes across diverse industries. The modern landscape of manufacturing and quality assurance demands advanced methodologies that go beyond human capability, and AI-powered image analysis has emerged as a transformative solution. In this research, we explore the integration of machine learning, computer vision, and image processing algorithms to enhance the accuracy, efficiency, and comprehensiveness of quality control procedures. The study begins by elucidating the theoretical foundations of image processing and AI, highlighting their synergy and applicability in quality control. It examines the pivotal components of this technology, including deep learning, convolutional neural networks (CNNs), and object recognition algorithms. We also investigate the practical challenges and considerations associated with implementing AI-driven image processing systems in industrial settings, such as data acquisition, hardware, and real-time processing. A central focus of this research is the evaluation of AI's capacity to inspect and analyze images, patterns, and anomalies across various sectors, from automotive and electronics to pharmaceuticals and food processing. We present case studies and empirical results demonstrating the significant improvements in defect detection, yield enhancement, and production efficiency achieved by integrating AI-powered image analysis. In conclusion, this study underscores the transformative potential of AI and image processing techniques in quality control, offering a glimpse into the future of manufacturing and production. The integration of AI and image analysis not only accelerates defect detection but also ensures consistent product quality, ultimately strengthening competitiveness and customer satisfaction.

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Development of a Computer-Assisted Vision-Based Inspection Monitoring System for Quality Assurance

Bhide,

Manivannan

2024

Lecture Notes in Mechanical Engineering

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Towards a Comprehensive Visual Quality Inspection for Industry 4.0*

Cited by 11 publications

References 14 publications

A Voice-Enabled ROS2 Framework for Human–Robot Collaborative Inspection

A Voice-Enabled ROS2 Framework for Human–Robot Collaborative Inspection

Improving Quality Inspections with Image Analysis and Artificial Intelligence

Development of a Computer-Assisted Vision-Based Inspection Monitoring System for Quality Assurance

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