A Feature Extraction Algorithm for Hybrid Manufacturing and Its Application in Robot-Based Additive and Subtractive Processes

Abstract

Establishing efficient, non-traditional manufacturing methods is critical for achieving a circular economy. Remanufacturing, the process in which a part is modified to restore original or like-new functionality, can improve manufacturing sustainability by closing the supply chain loop. A critical remanufacturing technique is hybrid manufacturing: combined additive and subtractive manufacturing processes that enable features to be added or removed from an existing part. This paper proposes a method for automating the feature extraction process for hybrid manufacturing, which is otherwise labor-intensive. The feature extraction is performed between a CAD model of the desired part and a 3D scan of the actual part to be remanufactured. The extracted features are then classified for the optimal manufacturing process and exported as STL files, which can be utilized as inputs for various tool path planning algorithms. An experiment is performed on an end-of-life case study to validate the proposed methodology. Additionally, simulations of the additive and subtractive remanufacturing processes are included to demonstrate one potential application of the proposed feature extraction process.