Bayesian optimisation of part orientation in additive manufacturing

Abstract

Additive manufacturing (AM) remains slow in terms of volumetric processing rates. Minimising support for overhanging faces is an effective method of reducing material wastage and postprocessing cost. Mindful design can remove much of this support; however, well-selected build orientations are still essential. Searching all feasible orientations is inefficient due to the large number of faces in many mesh files. Nevertheless, support structure generation forms a critical part of the AM process planning stage. This research uses novel combinations of proxy evaluation criteria models for support estimation and optimisation methods to minimise support structure. The number of overhanging facets, overall support structure length and a new estimate for the volume of support structure are used in place of a precise calculation of support quantity. These proxies are used within three different optimisation schemes: grid search, random search and Bayesian optimisations (BO). BO is found to out-perform random and grid search techniques, with grid search having the worst performance in most cases, requiring up to 17-times fewer optimisation iterations. The overall length of support is the most effective proxy model, even outperforming the volume of support estimation, and is shown to perform within 3.5% of results benchmarked against commercial software.

Minimizing Dimensional Defects in FFF Using a Novel Adaptive Slicing Method Based on Local Shape Complexity

“…Several research investigations have been focused on the improvement of AM processes and overcoming the limitations and challenges imposed by several factors and key parameters, such as the mechanical proprieties of raw materials [17][18][19][20], dimensional and geometrical tolerances [21,22], non-assembly mechanisms [23], build orientation [24][25][26][27], thermal behavior [20,28], and microstructural characterization [28].…”

“…The model shows that the dimensional deviation is pared to zero for the vertical walls, and it increases for deposition angles less and greater than 90 • . Goguelin et al optimized the build orientation and the support structure implementation using the Bayesian method [25]. The findings proved the method's efficiency compared to the grid method, especially with random build angles.…”

Data‐informed uncertainty quantification for laser‐based powder bed fusion additive manufacturing

“…Several surrogate-based optimisation approaches have been successfully implemented in numerical simulations of various additive manufacturing processes including PBF-LB/M. [17][18][19] Tamellini et al 20 emphasised the effectiveness of using surrogate models for parametric optimisation in the field of combined additive-subtractive manufacturing.…”

“…Surrogate‐based modelling is also a valuable tool for optimising PBF‐LB/M processes, as it provides an effective vehicle for improving overall process efficiency, again by providing cheap approximations that can efficiently replace the expensive evaluations of the full physics‐based model. Several surrogate‐based optimisation approaches have been successfully implemented in numerical simulations of various additive manufacturing processes including PBF‐LB/M 17–19 . Tamellini et al 20 emphasised the effectiveness of using surrogate models for parametric optimisation in the field of combined additive‐subtractive manufacturing.…”

Hyperparameter Bayesian Optimization of Gaussian Process Regression Applied in Speed-Sensorless Predictive Torque Control of an Autonomous Wind Energy Conversion System

“…While grid search and random search are common hyperparameter optimization strategies, they can be computationally expensive, as they require a large number of function evaluations. In contrast, hyperparameter Bayesian optimization (HBO) offers a more promising approach [27][28][29][30]. It has emerged as a powerful solution to obtain the best possible configuration using a small number of function evaluations.…”