Reliability-Based Topology Optimization Considering Overhang Constraints for Additive Manufacturing Design

Abstract

This study examines the combination of overhang constraints and Reliability-Based Topology Optimization (RBTO) in additive manufacturing (AM). AM offers intricate component production but faces challenges due to support structures. Incorporating overhang constraints in topology optimization enables self-supporting structures. RBTO addresses uncertainties in design variables to enhance reliability. This research investigates build direction parameter solutions using deterministic and RBTO algorithms. Topological properties, compliance, sensitivity, and density filters are assessed, alongside optimization techniques like Method of Moving Asymptotes (MMA) criterion and Optimality Criteria (OC). In numerical experiments on the MBB beam, the AM-RBTO algorithm reduced 3D printing time by approximately 18.3% and improved structural performance by lowering the objective function value by 1.85% compared to conventional RBTO. Results contribute to merging overhang constraints and RBTO in AM topology optimization, improving design by considering uncertainties. The study enhances computational efficiency and stability in optimizing build direction parameters, offering valuable insights for future AM applications.