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This paper introduces a data-driven framework for predicting surface roughness (Ra) in Material Extrusion Additive Manufacturing by training a multilayer perceptron regressor on experimental data and synthetic samples generated by a conditional GAN. The framework uses process parameters and surface inclination to predict Ra, addressing the challenge of anticipating roughness variations across a printed part. The key result is a web-based decision-support interface that visualizes predicted Ra on a 3D model, enabling interactive process planning and optimization of printing parameters and part orientation.
See how tweaking your 3D print orientation and parameters *before* printing can slash surface roughness, thanks to this interactive roughness prediction tool.
Surface roughness in Material Extrusion Additive Manufacturing varies across a part and is difficult to anticipate during process planning because it depends on both printing parameters and local surface inclination, which governs the staircase effect. A data-driven framework is presented to predict the arithmetic mean roughness (Ra) prior to fabrication using process parameters and surface angle. A structured experimental dataset was created using a three-level Box-Behnken design: 87 specimens were printed, each with multiple planar faces spanning different inclination angles, yielding 1566 Ra measurements acquired with a contact profilometer. A multilayer perceptron regressor was trained to capture nonlinear relationships between manufacturing conditions, inclination, and Ra. To mitigate limited experimental data, a conditional generative adversarial network was used to generate additional condition-specific tabular samples, thereby improving predictive performance. Model performance was assessed on a hold-out test set. A web-based decision-support interface was also developed to enable interactive process planning by loading a 3D model, specifying printing parameters, and adjusting the part's orientation. The system computes face-wise inclination from the model geometry and visualizes predicted Ra as an interactive colormap over the surface, enabling rapid identification of regions prone to high roughness and immediate comparison of parameter and orientation choices.
