Despite recent advances in improving mechanical properties of parts fabricated by Additive Manufacturing (AM) systems, optimizing geometry accuracy of AM parts is still a major challenge for pushing this cutting-edge technology into the mainstream. This work proposes a novel approach for improving geometry accuracy of AM parts in a systematic and efficient manner. Initial experimental data show that different part geometric features are not necessary positively correlated. Hence, it may not be possible to optimize them simultaneously. The proposed methodology formulates the geometry accuracy optimization problem as a multi-objective optimization problem. The developed method targeted minimizing deviations within part’s major Geometric Dimensioning and Tolerancing (GD&T) features (i.e., Flatness, Circularity, Cylindricity, Concentricity and Thickness). First, principal component analysis (PCA) is applied to extract key components within multi-geometric features of parts. Then, experiments are sequentially designed in an accelerated and integrated framework to achieve sets of process parameters resulting in acceptable level of deviations within principal components of multi-geometric features of parts. The efficiency of proposed method is validated using simulation studies coupled with a real world case study for geometry accuracy optimization of parts fabricated by fused filament fabrication (FFF) system. The results show that optimal designs are achieved by fewer numbers of experiments compared with existing methods.

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