A high-confidence geometric compensation approach for improving downward surface accuracy

Abstract

Overhangs are usually inevitable in Additive Manufacturing (AM). Moreover, making their downward surfaces reach higher accuracy after printing has an imperative impact on improving the accuracy of them and their corresponding as-printed parts. Although extensive studies have been devoted to improving the accuracy of as-printed parts, the effective, general, reliable, and less-consumption (in time and material) approach to make an overhang (especially on its downward surfaces) reach its ideal accuracy is still rare. Hence, a new machine-leaning-based geometric compensation approach is proposed in this study. First, based on the Taguchi method, a series of (overhang) benchmarks are designed and printed to collect the (geometric) deviations of the downward surfaces. With these data, a new deviation predictor is established based on the gaussian process regression model. The predictor can effectively predict the deviation(s) (as well as its corresponding quantified uncertainty) of each downward surface (after printing) by using a pointwise manner. Meanwhile, to make the downward surface of an overhang meet its ideal accuracy (after printing) with high confidence, a specific stochastic chance-constrained programming problem is first formulated to evaluate the new and suitable overhang height of each point on the downward surface. Based on the above-mentioned predictor, the programming problem gets solved by developing a new overhang-height optimization method that integrates the Monte Carlo simulation with the particle swarm optimization. After that, a support structure-associated compensation method is presented to make a pointwise compensation (according to the above-evaluated overhang height) on the downward surface and determine the support structures’ optimized number and anchor positions. Finally, experiments on several representative overhangs are also implemented based on a typical material extrusion machine to validate the effectiveness of the proposed approach. The results show that the proposed approach reduces the deviations of the downward surface by up to 63.92% on average, saving up to 40% of material (less support structures). In addition, methodological comparisons with state-of-the-art approaches are also implemented. The comparisons show that the proposed approach has the great potential to reliably improve the accuracy of the overhang’s downward surface(s) after printing in a less-consumption manner.