Additive manufacturing (AM) has brought positive opportunities with phenomenal changes to traditional manufacturing. Consistent efforts and novel studies into AM use have resolved critical issues in manufacturing and broadened technical boundaries. Build time estimation is one of the critical issues in AM that still needs attention. Accurate build time estimation is key for feasibility studies, preliminary design, and process/production planning. Recent studies have provided the possibility of neural network (NN)-based build time estimation. In particular, traditional artificial NN (ANN)- and convolutional NN (CNN)-based methods have been demonstrated. However, very little has been done on the performance comparison for build time estimation among the different types of NNs. This study is aimed at filling this gap by designing various NNs for build time estimation and comparing them. Two types of features are prepared as inputs for the NNs by processing three-dimensional (3D) models: (1) representative features (RFs) including dimensions, part volume, and support volume; and (2) the set of voxels generated from designating the cells occupied by the workpiece in a mesh grid. With the combination of NN types and input feature types, we design three NNs: (1) ANN with RFs; (2) ANN with voxels; and (3) CNN with voxels. To obtain large enough label data for reliable training, we consider simulation build time from commercial slicing applications rather than actual build time. The simulation build time is calculated based on a material extrusion process. To address various cases for input models, two design factors (scale and rotation) are considered by controlling the size and build orientation of 3D models. In computational experiments, we reveal that the CNN-based estimation is often more accurate than others. Furthermore, the design factors affect the performance of build time estimation. In particular, the CNN-based estimation is strongly influenced by changing the size of 3D models.