Multidisciplinary topology optimization incorporating process-structure-property-performance relationship of additive manufacturing

Abstract

Multidisciplinary topology optimization (TO) for additive manufacturing (AM) exhibits unprecedented design and manufacturing freedom as an effective lightweight methodology. In order to fully utilize the AM advantages of materialized arrangement in spatial and voxel-by-voxel digitalized printing, multidisciplinary TO for AM requires a full understanding of the process-structure-property-performance (PSPP) relationship to establish the quantitative correlation of part property and multiple process factors, including laser-, process-, and material-related parameters. This study incorporates the PSPP relationships of laser sintered material with topology optimizer so as to provide a guideline for TO collaborative AM design. The proposed workflow involves mapping functions of PSPP relationships via a data-driven approach and then optimizes the multiple process parameters and structural topology by the gradientbased algorithm. Compared with conventional optimization approaches, three case-dependent optimizations demonstrate the effectiveness of the proposed approach yielding encouraging improvements of lightweight performance. The multidisciplinary TO can be generally applied to types of AM technologies, establishing a strong linkage of physical product and digital design.

Investigation and Optimization of the Impact of Printing Orientation on Mechanical Properties of Resin Sample in the Low-Force Stereolithography Additive Manufacturing

“…In order to further promote the actual application of the SLA, much research has been conducted to improve the mechanical properties of the fabricated resin sample [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. Wang et al [ 12 ] conducted topological optimization to achieve high-strength nodes in additive manufacturing by applying the bidirectional evolutionary structural optimization method.…”

“…Wang et al [ 12 ] conducted topological optimization to achieve high-strength nodes in additive manufacturing by applying the bidirectional evolutionary structural optimization method. An AM-driven topology optimization method coupled with a transversely isotropic material model and a solid anisotropic material with penalization was proposed by Li et al [ 13 , 14 ] to establish the quantitative correlation between the process-related parameters and mechanical properties of printed materials. Valencia et al [ 15 ] studied the influences of the concentration of AgClO 4 , post-curing times, and other parameters on the performances of a metal/polymer nanocomposite with enhanced optical/electrical behavior.…”