Pore Detection

Pore detection focuses on accurately identifying and locating pores within various materials and structures, a crucial task for quality control and performance prediction in manufacturing and other fields. Current research emphasizes the use of machine learning, particularly convolutional neural networks (CNNs), and Gaussian kernel density estimation to analyze diverse data sources, including acoustic signals from additive manufacturing processes and image data from fingerprint analysis. These advanced techniques aim to improve the precision and efficiency of pore detection, leading to enhanced product quality and reduced reliance on time-consuming post-processing methods. The development of robust and reliable pore detection methods has significant implications for various industries, improving manufacturing processes and enabling more effective quality assurance.