Neurosymbolic Artificial Intelligence (NSAI) based Algorithm for predicting the Impact Strength of Additive Manufactured Polylactic Acid (PLA) Specimens. (arXiv:2305.05668v1 [cs.LG])

In this study, we introduce application of Neurosymbolic Artificial
Intelligence (NSAI) for predicting the impact strength of additive manufactured
polylactic acid (PLA) components, representing the first-ever use of NSAI in
the domain of additive manufacturing. The NSAI model amalgamates the advantages
of neural networks and symbolic AI, offering a more robust and accurate
prediction than traditional machine learning techniques. Experimental data was
collected and synthetically augmented to 1000 data points, enhancing the
model’s precision. The Neurosymbolic model was developed using a neural network
architecture comprising input, two hidden layers, and an output layer, followed
by a decision tree regressor representing the symbolic component. The model’s
performance was benchmarked against a Simple Artificial Neural Network (ANN)
model by assessing mean squared error (MSE) and R-squared (R2) values for both
training and validation datasets. The results reveal that the Neurosymbolic
model surpasses the Simple ANN model, attaining lower MSE and higher R2 values
for both training and validation sets. This innovative application of the
Neurosymbolic approach in estimating the impact strength of additive
manufactured PLA components underscores its potential for optimizing the
additive manufacturing process. Future research could investigate further
refinements to the Neurosymbolic model, extend its application to other
materials and additive manufacturing processes, and incorporate real-time
monitoring and control for enhanced process optimization.



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