Experimental and ANN-Based Prediction of Hardness in FDM-Printed PLA

Adell Amer; Muamar  Ben Isa; Abdusalam fkereen

doi:10.66411/jer.v41i1.282

Authors

Adell Amer Department of Industrial and Manufacturing Engineering, Faculty of Engineering, University of Misurata, Libya Author
Muamar Ben Isa Department of Mechanical & Industrial Engineering, Faculty of Engineering, University of Asmarya, Libya Author
Abdusalam fkereen Libyan Polymer Research Center, Libya Author

DOI:

https://doi.org/10.66411/jer.v41i1.282

Keywords:

Fused Deposition Modelling (FDM), Polylactic Acid (PLA), Hardness, Artificial Neural Network (ANN), Infill Density

Abstract

This study investigates the influence of key FDM 3D-printing parameters, namely infill density and printing speed, on the hardness behavior of Polylactic Acid (PLA) specimens. In addition, an Artificial Neural Network (ANN) model was developed to predict hardness values based on the selected process parameters. A total of 54 specimens were fabricated using a 3D4E–4GenPro printer under different printing conditions. Hardness measurements were performed using a Shore D hardness tester, while outlier values were excluded and average readings were adopted to improve result reliability. The experimental results revealed a clear positive relationship between infill density and hardness values, whereas printing speed exhibited a comparatively lower influence. Analysis of Variance (ANOVA) demonstrated that infill density was the dominant parameter affecting hardness, contributing approximately 70.57% of the total variation, while printing speed contributed about 7.87%. A feed forward ANN model was developed with two input variables, two hidden layers containing (16 and 8 neurons respectively), and a single output layer. The dataset was divided into 80% for training and 20% for testing. The developed model achieved satisfactory predictive performance with a Mean Absolute Error (MAE) of 0.67, a Root Mean Square Error (RMSE) of 0.77, and a coefficient of determination (R²) of 0.787, indicating good agreement between experimental and predicted hardness values. Furthermore, the training and validation curves demonstrated stable learning behavior without noticeable overfitting, while residual analysis confirmed that prediction errors remained within acceptable limits. The findings highlight the capability of ANN-based modeling as an effective tool for predicting the hardness behavior of FDM-printed PLA components while reducing experimental time, effort, and material consumption.

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