In capital goods industry, there are some components that are employed for safety purposes and, due to this fact, parts are subjected to high quality control demands. This is especially relevant for the case safety components that contain welds because of the inherent complex process and the likelihood of defects appearance. In this context, this work presents a machine vision system that was employed for replacing costly quality control procedures based on visual inspection. This was possible thanks to the proper design of all the machine vision system components including the image processing algorithm. As a special feature of the system, it has to be highlighted the low cycle time of the production process (<2s), which stablished some requirements on the image processing algorithms. During the inspection system development, the main efforts were concentrated for obtaining a reliable and balanced database of defective and non-defective parts images useful to train the classification model. At this respect, the main contributions consisted of image analysis software development and visual curation of data. As a result, tailor made filters were developed that allowed together with color information the identification of common flaws, as Lacks of Fusion (LoF). Due to the high amount of inspected samples, a preliminary deep learning based model was developed that included these filters with the aim of increasing defect detection accuracy.