Effect of process parameters on the strength of ABS based FDM prototypes: novel machine learning based hybrid optimization technique

Ramiah, Karthikeyan; Pandian, R. Sudhakara

doi:10.1088/2053-1591/acb909

Cited by 4 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the effectiveness of the developed model was confirmed with an R 2 score of 0.67. Ramiah and Pandian investigated the effects of 3D printing parameters and determined the optimal combination of these parameters [27]. The abovementioned process parameters were found to have a good effect on the strength of the constructed models.…”

Section: Introductionmentioning

confidence: 99%

Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm

Ulkir,

Bayraklılar,

Kuncan

2024

Applied Sciences

View full text Add to dashboard Cite

As additive manufacturing (AM) processes become integrated with artificial intelligence systems, the time and cost of the fabrication process decrease. In this study, the raster angle, an important parameter in the manufacturing process, was examined using fused deposition modeling (FDM), an AM method. The optimal value of this parameter varies depending on the designed product geometry. By changing the raster angle, the distribution of stresses and strains within the printed object can be modified, potentially influencing the mechanical behavior of the object. Thus, the correct estimation of the raster angle is essential for obtaining parts with high mechanical properties. The focus of this study is to reduce the fabrication time and cost of products by intertwining machine learning (ML) systems with mechanical systems. Its novelty is that ML has never been applied for FDM raster angle estimation. The estimation and modeling of the raster angle were performed using five different ML algorithms. These algorithms include a support vector machine (SVM), Gaussian process regression (GPR), an artificial neural network (ANN), decision tree regression (DTR), and random forest regression (RFR). Data for training were generated using various shapes and geometries, then trained in the MATLAB software, and a prediction model between the input parameters and the raster angle was created. The predicted model was evaluated using five performance criteria. The RFR model predicts the raster angle in the FDM test data with R-squared (R2) = 0.92, an explained variance score (EVS) = 0.92, a mean absolute error (MAE) = 0.012, a root mean square error (RMSE) = 0.056, and a mean squared error (MSE) = 0.0032. These values are R2 = 0.93, EVS = 0.93, MAE = 0.010, RMSE = 0.051, and MSE0.0025 for the training data. RFR is significantly superior to the other prediction algorithms. The proposed model predicts the optimum raster angle for any geometry.

show abstract

Section: Introductionmentioning

confidence: 99%