Support structure tomography using per-pixel signed shadow casting in human manikin 3D printing

Abstract This study proposes an advanced algorithm for predicting the optimal orientation in human manikin 3D printing. We can print mesh data on any scale depending user’s needs. Once printing was determined, were dissected based printer’s maximal volume using our previous work. Then, we applied newly proposed algorithm, designated as “per-pixel signed-shadow casting,” to each part calculate volumes of object and support structure. Our method classified original triangles into three groups—alpha, beta, top-covering—to eliminate need special hardware such graphic cards. The result is shown a two-dimensional bitmap file, “tomograph”. tomograph represents local structure distribution information visual quantitative basis. Repeating this tomography rotational axes resulted four-dimensional (4D) box-shaped graph. arbitrary easily determined from lowest-valued pixel 4D box several basic primitive shapes with different degrees symmetry complex shapes, famous “Stanford Bunny”. Finally, manikins scales. theoretical values compared those obtained analytical or g-code-based experimental volumes.