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

Abstract

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.

Study of Transmission Line Boundary Protection Using a Multilayer Perceptron Neural Network with Back Propagation and Wavelet Transform

“…The use of neural network models finds application in several fields of research in engineering [35,36]. Kadam et al [37] opined that machine learning offers a broad range of algorithms that can be adapted to help with fault detection. There are numerous types of machine learning and artificial intelligence structures to choose from when implementing an automated decision-making program.…”

Fracture Load Predictions in Additively Manufactured ABS U-Notched Specimens Using Average Strain Energy Density Criteria

“…As mentioned above, one of the advantages of AM is its capacity to manufacture complex parts. These parts may contain a wide variety of stress risers, such as defects generated during the manufacturing process (such as warping, poor surface finish or porosity [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]), defects caused by operational damage, or structural details included in the original design (e.g., notches, holes, corners, cut-outs, etc.). These types of stress risers may be analysed as cracks when following traditional fracture mechanics criteria.…”

Recovery of Waste Material from Biobags: 3D Printing Process and Thermo-Mechanical Characteristics in Comparison to Virgin and Composite Matrices

“…In this regard, a commercially available filament from waste recycling of biobags has been considered and characterized through thermal analysis (thermogravimetric analysis, TGA and differential scanning calorimetry DSC) and infrared spectroscopy (ATR). Several printing attempts have been made using variations in the printing process parameters (i.e., bed temperature, layer thickness, top surface layers, retraction speed and distance) to achieve a satisfactory quality of the final 3D printing products without evident macroscopic defects and imperfections appreciable to the eye [ 25 , 26 , 27 , 28 , 29 ] (i.e., poor surface finish, stringing, oozing, delamination, wrapping, misalignment of the print platform and nozzle, clogging of the nozzle, depletion of printing material or disrupted material flow, and lack or loss of adhesion to the print platform). The thermo-mechanical properties of 3D printed parts were developed under optimal printing conditions and measured through dynamic mechanical analysis.…”