<p>This research explores the application of Python-driven automated machine vision algorithms for inspection in sheet metal forming, a critical manufacturing process. The study addresses the need for advanced, reliable, and efficient inspection techniques to enhance quality control, thereby improving product performance and manufacturing efficiency. The methodology used in this research involves inspecting formed sheet metal products using Python-based methods, namely the Structural Similarity Index Measure (SSIM) and Normalized Cross Correlation (NCC), along with MATLAB for image correlation, are applied directly for contour inspection. In addition to contour inspection, feature detection, which includes dimensional measurement, is also carried out as a critical part of assessing the quality and performance of the formed sheet metal products. This research integrates machine vision algorithms with Python, offering a comprehensive inspection of sheet metal products. The use of Python-based methods and the Hough Transform (HT) algorithm for inspecting sheet metal formed components introduces a novel approach with immense potential for enhancing efficiency in the quality control of the sheet metal inspection process. This signifies a notable breakthrough in automated inspection within the sheet metal forming industry, allowing comprehensive inspection of both features and dimensional measurements. By adopting the most effective method, manufacturers in the sheet metal fabrication field can enhance inspection efficiency and accuracy, thereby improving product quality and operational performance.</p>