Height Measurement for Solder Ball Point Cloud

In modern manufacturing, precise point cloud data processing methods are crucial for quality control and product inspection. This study focuses on the height measurement of high-precision solder ball point clouds and proposes an unsupervised machine learning algorithm. Solder ball point clouds are pre-sented in a three-dimensional data format and are commonly used in the field of electronic manufacturing and welding processes for quality control. However, height measurement of solder ball point clouds poses a challenging task. Therefore, this study aims to introduce an innovative method that can accurately separate individual solder ball point clouds and measure their heights, providing a reliable quality control tool for the manufac-turing industry. Our algorithm initially employs KMeans and the least squares method for coordinate axis alignment. Subsequently, it applies plane segmentation techniques and the DBSCAN algorithm to separate the point cloud into individual solder balls. Finally, we calculate the height of each solder ball. Through validation on existing datasets, we demonstrate the feasibility and accuracy of this method, significantly enhancing the efficiency and precision of solder ball point cloud measurement.