Fracture Prediction of Additively Manufactured PLA Notched Specimens Using Modified Energy-Based Failure Models
摘要
Nowadays, precise and efficient failure prediction is an important and active area in the field of notch fracture mechanics. This study aims to introduce new failure energy-based model criteria by implementing the nonlinearity behaviour into the combination of Strain Energy Density (SED) and the concept of the Theory of Critical Distances (TCD). As a result, four novel failure models have been developed. The first two, referred to as the Modified Point Energy (MPE) and the Modified area energy (MAE) failure models, are derived analytically by integrating the concept of nonlinear behavior and TCD into the SED criterion. The remaining two failure models, known as the Calibrated Point Energy (CPE) and Calibrated area energy (CAE), are established through experimental calibration within the SED framework. In this way, a series of experimental data from the tested additively manufactured (AM) Polylactide (PLA) notched specimens are utilized to validate the suggested criteria. The selected experimental results take into account four factors, including specimen geometry, types of notches, raster orientation, and notch tip radius. Ultimately, it was found that the MPE model offers rapid execution, robustness, and satisfactory accuracy in predicting the fracture of additively manufactured notched specimens. This performance is superior to previous classical approaches such as TCD and ASED, which showed similar performance to each other on these datasets. The MPE criterion demonstrates a remarkable consistency between empirical and theoretical results, with an average discrepancy of approximately 8%. Also, the MAE criterion, with an average discrepancy of approximately 11%, indicates its suitability as the preferred solution in scenarios where designers seek to balance computational efficiency and accuracy. Although the two other failure criteria also demonstrated reasonable accuracy in predictions, they still have the potential to attract the interest of researchers in this field due to their unique characteristics.