Morphological Image Techniques for Predictive Maintenance Detection

The advancement of modern manufacturing industry increases the need for fast, accurate, and efficient defect detection, especially in PCB (Printed Circuit Board) production. Image morphology techniques offer an effective solution by utilizing basic operations such as erosion, dilation, opening, and closing to extract important features and eliminate noise. This study discusses the application of morphology techniques to detect defects in PCBs as part of a predictive maintenance strategy. The process starts from image acquisition, pre-processing, segmentation, to feature extraction and defect classification using machine learning algorithms. The results show that the use of morphology techniques can improve the accuracy of defect detection and optimize product quality, while reducing manual inspection costs. This approach also allows early prediction of potential failures, thus supporting the improvement of system perfection and operational efficiency. In the future, further exploration of the combination of morphology techniques and learning systems is key to developing a more adaptive automatic inspection system for industrial needs.