Decision support in investment casting manufacturing: a convolutional neural network-driven approach

Abstract In manufacturing, and particularly in manually driven processes, diagnostics and decision support tools that utilize data-driven methods are key factors for reliable production processes. The investment casting manufacturing process relies on quality assessment through microscope examinations of cross-sections (cutups) of produced pieces, traditionally depending on operator judgment to manually approve or reject parts, which may introduce bias. This work focuses on identifying and addressing the need for reliability and efficiency in the investment casting manufacturing process by proposing a decision support tool to assist the operator in defect detection and fault identification in a semi-automated way. Initially, we explore the machine learning classifier Random Forest and then propose the use of a convolutional neural network, a deep learning method, for improving binary classification accuracy when predicting the presence of a defect in a microscope-derived image. The model presents classification accuracy between faulty and non-faulty images at 98% as a key finding and also tested on new, never-before-seen images from the production process. The results demonstrate the transformative potential of introducing data-driven methods such as convolutional neural networks into manual manufacturing processes, paving the path for more reliable production methods in the investment casting manufacturing industry.