Optimization of design parameters and 3D-printing orientation to enhance the efficiency of topology-optimized components in additive manufacturing

Abstract

Computer-aided design (CAD) is revolutionizing 3D object production in Additive Manufacturing (AM), especially enhancing the creation of complex optimized structures. However, due to the difficulties of testing the thickness or size of structural components using physical testing in topology-optimized parts, optimizing parts for high efficiency remains a significant challenge. In addition to optimizing 3D printing orientation, this study uses Response Surface Methodology (RSM) and optimization algorithms to optimize design parameters such as safety factor and thickness for predicting stress and deformation in topology optimization. The results show that RSM, Artificial Neural Network (ANN) and observed simulation results strongly correlate, confirming their reliability for determining the most optimal design parameters for topology optimization. Maximum displacement and Von-Mises stress at 0.101 mm and 29.1 MPa were found to be the optimal responses. In contrast, the optimum input parameters include a minimum safety factor of 1.2, a minimum thickness of 3.85 mm, and a maximum thickness of 8.42 mm, which are identified for optimal topology optimization using Genetic Algorithms (GA). These results were verified using optimization software, confirming the effectiveness of the study's methodology. Vertical orientation is the optimal printing orientation to develop parts with greater hardness, according to X-ray Diffraction (XRD), hardness, wear resistance, and morphological tests. Testing confirmed that the topology-optimized result, which utilized the most optimal process condition and orientation, effectively produced a stronger 3D-printed part. Therefore, this method saves material and the time waste usually caused by trial and error by predicting the optimal 3D printing orientation and design parameters in providing more effective and intended results.