Interactive 3D visualization of surface roughness predictions in additive manufacturing: A data-driven framework

TL;DR

This paper introduces a data-driven, interactive visualization framework for predicting and analyzing surface roughness in additive manufacturing, aiding process planning and quality control.

Contribution

It presents a novel combination of machine learning, generative modeling, and web-based visualization for surface roughness prediction in 3D printing.

Findings

High prediction accuracy of surface roughness using neural networks

Effective data augmentation with GANs improves model performance

Interactive visualization aids in rapid process decision-making

Abstract

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.