Understanding the Incident Wave Errors in Split Hopkinson Pressure Bar Test with Machine Learning Method

The article delves into the challenges of waveform errors in Split Hopkinson Pressure Bar (SHPB) tests due to striker bar imperfections. It highlights the application of machine learning to classify and correct these errors, providing valuable insights and practical solutions for improving the accuracy of dynamic mechanical data in materials testing. The study involves extensive numerical simulations and feature engineering to develop a robust machine learning model capable of real-time error correction and quantification.