Linking Ultrasound Data to Manufacturing Parameters of 3D-printed Polymers Using Supervised Learning

Additive manufacturing is used to produce complex and tailored products that cannot be achieved using conventional manufacturing approaches. The products can be made from different materials including polymers, metals, etc. The material is added layer by layer to create a final product. The mechanical properties of the final part depend on the process parameters. To improve the quality of the product these manufacturing parameters need to be optimised and for this purpose machine learning along with ultrasound measurements can be used. In this paper, the manufacturing parameters of 50 mm thick polymer cubes are linked to the ultrasound data using partial least squares regression. Three cubes with varying layer heights are made from PLA and ABS each, backscattered responses of ultrasound are recorded from these six cubes. The ultrasound data is used in partial least squares algorithm to estimate the layer height and the filament type. The clusters that are formed using the first few components obtained from the algorithm show that the data points of the six cubes can be distinguished and the manufacturing parameters are estimated with good accuracy.