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.

本研究介绍了神经符号人工智能（NSAI）在预测增材制造聚乳酸（PLA）零件的撞击强度方面的应用，该应用结合了神经网络和符号人工智能的优势，比传统机器学习技术提供更强大和准确的预测，从而彰显了优化增材制造过程的潜力。

Neurosymbolic Artificial Intelligence (NSAI) based Algorithm for
  predicting the Impact Strength of Additive Manufactured Polylactic Acid (PLA)
  Specimens

基于神经符号人工智能(NSAI)算法的预测增材制造聚乳酸(PLA)试样冲击强度