Research on Deep Learning Applied to Portrait Bas-relief

Abstract

3D printing also known as additive manufacturing (AM) can be expressed as any process of printing 3D objects There are seven major methods for 3D printing Fused deposition modeling (FDM) is the most common method of 3D printing Although there is a new modeling that it could directly converts two-dimensional images into G-code for 3D printing in the laboratory the printing is still determined by the gray scale value of the pixel to assign the fixed value for the printing height When printing the portrait bas-relief the height of the eyes will be higher than the nose and the mouth it is different from the actual portrait This research design a portrait bas-relief software which can find the characteristic in portraits automatically by using convolutional neural network (CNN) to train a feature detection modeling and apply feature coordinate to help the design for layer height as well to make the printing height more reasonable After recording the path planning of the coordinate on each layer it will generate G-code directly skipping the procedure of saving as STL files to avoid the disadvantages In addition to the above this results also regenerate the new modeling [1] to make the software function more complete

Date of Award	2020
Original language	English
Supervisor	Wei-Hsiang Lai (Supervisor)