Optimizing novel multi-scaled simulation method for deviation analysis of generatively designed aileron bracket using laser powder bed fusion

This research is devoted to forecast the distortion of aileron brackets by means of generative design (GD) and multi-scaled numerical simulation comprising meso- and macro-scaled simulation based on thermomechanical method (TMM) and inherent strain method (ISM), respectively. The multi-scaled simulation began with TMM-based virtual calibration test (VCT) including mesh sensitivity and volume fraction analysis to identify the best meshing voxel size. In finding inherent strain tensors, optimization was implemented using pattern search algorithm referring to the minimum relative error. Further, macro-scaled simulation was implemented to estimate bracket distortion behavior by applying the inherent strain tensors in ISM. For experiment, the conventional aileron bracket shape was first improved by complying the internal rules of GD throughout the desired design space with respect to stress goal and weight reduction based on iterative material distribution. After obtaining the new generatively designed component, linear static analysis was implemented to improve the stress magnitude and surface smoothness level by mesh and material sculpting. Then, the component is manufactured using laser powder bed fusion with manual postprocessing of support structure followed by sand blasting. The finished aileron bracket was then measured using a 3D scanner GOM Atos Q. As conclusion, this novel multi-scaled simulation method based on GD, static stress, and virtual calibration test allows a forecast of an acceptable surface deviation within relative single point and mean errors up to 11% and 5% respectively. By neglecting the tedious and time-consuming procedure of real calibration, a huge time reduction for preparation up to a few days and for computation up to 35% compared to pure TMM can be achieved.