Multi-track Geometry Prediction in Powder Fed Laser Additive Manufacturing Using Machine Learning

Abstract

Laser additive manufacturing (LAM) allows for complex geometries to be fabricated without the limitations of conventional manufacturing. However, LAM is highly sensitive to small disturbances, resulting in variation in the geometry of the produced layer (clad). Therefore, in this research a monitoring algorithm is discussed with the capability of predicting the geometry of multiple tracks of added material. Though imaging can be used to measure the geometry of the melt pool during LAM, the appearance of the melt pool changes in multi-track processes due to the previous layers causing measurement errors. Hence, a machine learning algorithm may be able to accommodate for the changing melt pool appearance to improve accuracy. Images can be captured during LAM with visible-light and infrared sensors which may provide sufficient information for the geometry to be predicted. A convolutional neural network (CNN) can then use these images to estimate the geometry (height and width) during LAM processes.