Visual inspection based systems are important tools to ensure the quality of manufactured parts in industry. This work presents an automatic visual inspection approach for defect detection in turbo vanes in the investment casting industry. The proposed method uses RANSAC for robust line and circle detection to extract relevant information to discriminate between a good part and a defected one. Then, using this data a feature vector is created serving as input to a SVM classifier that after the training phase is able to discriminate and classify between a good sample or not. To test the proposed approach a private database was created containing 650 turbo vanes (which gives 2600 different samples to train and test). On this database the proposed method achieved an average accuracy of 99.96%, an average false negative rate of 0.00% and an average false positive rate of 0.05%, using a 5-fold cross validation protocol, which demonstrates the success of the proposed method. Moreover, the proposed image processing pipeline was deployed into Raspberry Pi 4 Model B part of a visual inspection machine, and is working daily at ZCP – Zollern and Comandita Portugal, which proves the method's robustness.