Surface roughness prediction in micro-plasma transferred arc metal additive manufacturing process using K-nearest neighbors algorithm

Abstract

Micro-plasma transfer arc metal additive manufacturing (μ-PTAMAM) process is a unique direct energy deposition (DED) type additive manufacturing (AM) process capable of manufacturing three-dimensional metallic components through simultaneous use of wire and powder forms of AM material. DED process yields an uneven surface on the manufactured components which necessitates their further post-processing to attain the required surface finish, dimensional accuracy, and geometrical accuracy. This research focuses on using machine learning algorithm, namely, K-nearest neighbors (KNN), to predict the surface roughness. The data of surface roughness for training KNN algorithm was generated by depositing multi-layer single-track depositions yielding wall-like structures using Stellite-6 as AM material in powder and wire forms. It was found that surface roughness increases with an increase in power supply to micro-plasma and AM material feed rate whereas it decreases with an increase in traverse speed of the deposition head for both powder and wire of the AM material. Surface roughness of the walls for powder form of the AM material is smaller (i.e., 118 to 149 μm) than that obtained by the wire form (i.e., 195 to 227 μm). Surface roughness prediction error for KNN algorithm is found to be from − 6.2 to 2.8% for powder form and − 5.8 to 2.3% for wire form of the AM material proving capability of KNN algorithm for accurate prediction of surface roughness produced by μ-PTAMAM process. Since the prediction error depends on number of data set used to train KNN algorithm, therefore, it can be further reduced by increasing number of training data set. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.