Intelligent molecular orientation transformation in 3D printing system

Proper orientation of a molecular structure in three-dimensional (3D) printing could increase successful printing rates, reduce the amount of supporting material required, and shorten printing time. In traditional approaches, manual adjustment of a target object for its optimal orientation is inefficient and inconsistent and often requires several trials. Hence, manually and visually rotated results for molecular protein structures with complex, intertwined, rugged, and asymmetric surfaces are not satisfactory. In this study, we apply a grid-based principal component analysis (GPCA) method for an automatic object orientation prior to the physical printing stage. First, a down-sampled three-dimensional protein structure is constructed by lattice-space simulations, and then the proposed GPCA technology is applied to identify possible plane candidates with the largest projection area. Second, a vertical flipping operation is performed and evaluated for a smaller buried volume. Finally, the orientation of the rotated object is iteratively inspected and modified with subtle angle changes in order to further reduce the required supporting material. Several testing cases were used to illustrate the superior performance of the proposed algorithm. Specifically, 140 representative protein structures categorized into seven different groups were selected from the well-known Structural Classification of Proteins—extended database. As the results show, the protein structures were theoretically and heuristically rotated to their optimal orientations for the corresponding 3D printings. The proposed automatic orientation procedure could reduce 38.15% of the required supporting material on average. Furthermore, the expected printing time could be reduced by an average of 17.2 min for small-scale protein structures.