Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products

In the era of Industry 4.0, idea 3D printed products has gained momentum and is also proving to be beneficial in terms financial time efforts. These are physically built layer-by-layer based on digital Computer Aided Design (CAD) inputs. Nonetheless, still subjected defects due variation properties structure, which leads deterioration quality products. Detection these errors at each layer level product prime importance. This paper provides methodology for layer-wise anomaly detection using an ensemble machine learning algorithms pre-trained models. The proposed combination trained offline implemented online fault detection. current work experimental comparative study different models with monitoring Fused Deposition Modelling (FDM). results showed that Alexnet SVM algorithm given maximum accuracy. approach low computing costs, can easily real-time