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International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 06 | Jun 2024

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p-ISSN: 2395-0072

Real-time quality control monitoring should be facilitated by artificial intelligence. Shubham Prashant Bhambar, Shubham Sitaram Thete, Maheshwari Keshav Tarle, Yash Arun Bhamare 1MS – Industrial and System Engineering From University Of Minnesota, Twin Cities 2BE – Mechanical From SNJB College Of Engineering, Chandwad

3BE – Mechanical From K.K. Wagh Institute Of Engineering Education And Research, Nashik 4BE – Mechanical From D Y Patil College Of

Engineering, Akurdi, Pune ---------------------------------------------------------------------***---------------------------------------------------------------------

Abstract - Computer Vision (CV) is a field of artificial

hyperspectral imaging can provide information on the chemical composition of processed materials, and computer imaging can combine a series of images to highlight details not visible using traditional MV techniques. Additionally, polarization cameras can visualize stresses in materials. These advancements enable improved performance, integration, and automation in the manufacturing industry. The high demands of Industry 4.0 necessitate varying levels of integration, from supporting manual assembly to full integration with original equipment manufacturers.

intelligence (AI) that enables computers to interpret and process visual data from the world, emulating human vision. By using digital images from cameras and videos, along with deep learning models, computers can be trained to perform tasks such as image recognition, object detection, and image generation.

Machine Vision (MV), closely related to CV, is a technology used in industrial automation that employs cameras and image processing software to inspect and analyze objects automatically. While CV is more focused on algorithms and the science behind visual recognition, MV is typically applicationdriven and focuses on integrating hardware and software for specific industrial tasks.

2. Importance of Quality in Industry 4.0 To ensure continued customer satisfaction, the industry must continuously innovate, accelerate, and improve. Crucially, the evolution of the sector must be accompanied by the evolution of the quality function. Neglecting "Quality 4.0" could jeopardize the apparent progress, potentially compromising product quality and the customer experience. Quality controls must transition from paper-based to cloudbased systems, aligning with the technological advancements of Industry 4.0 while maintaining their core purpose. Digital tools now enable companies to connect more directly with their contractors, suppliers, and customers, fostering stronger relationships across the entire ecosystem. The growth enabled by AI can significantly bolster business operations, facilitating real-time collaboration, enhanced client engagement, and optimized reporting. This interconnectedness contributes to overall quality and risk prevention.

Key Words: Artificial Intelligence, machine learning, deep learning, DL, Industry 4.0, Quality 4.0, real-time quality management, and computer vision.

1.Introduction In recent years, computer vision (CV) technologies have seen significant advancements, becoming integral to many industrial processes. According to the latest Gartner Hype Cycle 2023, CV is nearing its peak productivity, particularly in the realm of artificial intelligence (AI) tools. This trend signals a forthcoming expansion of CV applications across various industries. Presently, CV plays a crucial role in the industrial sector, especially in automated inspection functions as part of quality control procedures. However, the growing complexity of automation, driven by Industry 4.0, the Internet of Things (IoT), cloud computing, AI, machine learning (ML), and other technologies, presents significant challenges for users and developers of CV systems in selecting the most suitable technology for specific applications.

3. Role of Machine Vision in Quality Control Machine vision (MV) systems offer unparalleled speed, precision, and consistency in the quantitative assessment of structured scenes. For example, an MV system on a production line can inspect hundreds or even thousands of components per minute. With appropriate optics and camera resolution, such systems can easily detect defects that are invisible to the naked eye. MV excels in quantitative tasks, while human inspection is better suited for qualitative interpretation of complex and unstructured scenes. By leveraging both human and machine capabilities, industries can achieve comprehensive quality control.

1. Evolution and Integration of Computer Vision in Industry With rapid advancements in numerous fields—such as CV techniques, CMOS sensors, integrated machine vision (MV), ML, deep learning (DL), robotic interfaces, data transfer standards, and MV functionality—new MV technologies are unlocking new application potentials. For instance,

Impact Factor value: 8.226

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