Programmatic generation of computationally efficient lattice structures for additive manufacture

Abstract

Purpose Additive manufacturing (AM) enables the fabrication of complex geometries beyond the capability of traditional manufacturing methods. Complex lattice structures have enabled engineering innovation; however, the use of traditional computer-aided design (CAD) methods for the generation of lattice structures is inefficient, time-consuming and can present challenges to process integration. In an effort to improve the implementation of lattice structures into engineering applications, this paper aims to develop a programmatic lattice generator (PLG). Design/methodology/approach The PLG method is computationally efficient; has direct control over the quality of the stereolithographic (STL) file produced; enables the generation of more complex lattice than traditional methods; is fully programmatic, allowing batch generation and interfacing with process integration and design optimization tools; capable of generating a lattice STL file from a generic input file of node and connectivity data; and can export a beam model for numerical analysis. Findings This method has been successfully implemented in the generation of uniform, radial and space filling lattices. Case studies were developed which showed a reduction in processing time greater than 60 per cent for a 3,375 cell lattice over traditional CAD software. Originality/value The PLG method is a novel design for additive manufacture (DFAM) tool with unique advantages, including full control over the number of facets that represent a lattice strut, allowing optimization of STL data to minimize file size, while maintaining suitable resolution for the implemented AM process; programmatic DFAM capability that overcomes the learning curve of traditional CAD when producing complex lattice structures, therefore is independent of designer proficiency and compatible with process integration; and the capability to output both STL files and associated data for numerical analysis, a unique DFAM capability not previously reported.