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

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

Automated summary

In the era of Industry 4.0, the concept of 3D printed products has gained attention, but issues with quality still exist due to variations in properties and structure. By using machine learning algorithms and pre-trained models for layer-wise anomaly detection, real-time fault detection can be achieved. Experimental results show that the combination of Alexnet and SVM algorithm has the highest accuracy, with low costs for experiments and computations.