Wykład pt. AI in Engineering: Advanced Methods for Assessing Surface Quality in Manufacturing Processes wygłosi prof. Marcin Hinz z Wydziału Inżynierii Mechanicznej, Samochodowej i Lotniczej Munich University of Applied Sciences.
Spotkanie poprowadzą:
- prof. dr hab. inż. Ewa Niewiadomska-Szynkiewicz, Prodziekan ds Nauki, Wydział Elektroniki I Technik Informacyjnych, Politechnika Warszawska
- prof. dr hab. inż. Robert Nowak, Zastępca Dyrektora ds. Nauki, Instytut Informatyki, Wydział Elektroniki I Technik Informacyjnych, Politechnika Warszawska
Termin: 27.03.2025 r. (czwartek), godz. 15.00 - 16:30
Miejsce: Wydział Elektroniki i Technik Informacyjnych (WEiTI), Audytorium Centralne (I piętro).
Wymagana wcześniejsza rejestracja z podaniem adresu mailowego w domenie PW.
Formularz rejestracyjny: TUTAJ.
Abstrakt:
The integration of Artificial Intelligence (AI) in engineering quality assessment is transforming manufacturing and production processes by enhancing efficiency, accuracy, and scalability. Traditional quality control methods, which rely on manual inspections and tactile measurements, are often time-consuming, subjective, and prone to human error. AI-driven solutions, leveraging machine learning and computer vision, enable automated, precise, and real-time quality assessment, reducing costs and minimizing production downtime.
This study focuses on the application of machine learning for surface quality analysis, particularly in evaluating surface roughness in high-precision manufacturing. Conventional surface measurement techniques, such as tactile roughness testers and optical 3D microscopes, are limited by their time-intensive and costly nature. To address these challenges, an AI-based framework is introduced, integrating image-based analysis, computer vision, and machine learning techniques for efficient surface inspection.
The experimental setup involves capturing high-resolution images of product surfaces under controlled lighting conditions, followed by image pre-processing and feature extraction. Various machine learning models—including supervised, unsupervised, and semi-supervised approaches—are assessed for their effectiveness in surface classification and regression tasks. Supervised learning methods such as Random Forest, Support Vector Machines, Neural Networks, and Long Short-Term Memory (LSTM) models are employed to enhance classification accuracy, while convolutional neural networks (CNNs) demonstrate strong performance in identifying surface characteristics. As AI continues to advance, its role in quality assessment is expected to further improve manufacturing efficiency, adaptability, and reliability. This research contributes to the development of AI-driven surface inspection systems, providing a scalable and precise alternative to traditional measurement techniques in engineering and production environments.