• Non-destructive testing (NDT) is the process of inspecting, testing or evaluating materials components or assemblies for characteristics differences or welding defects, discontinuities etc. without causing damage to the serviceability of such material components or assemblies.
• The technical problem underlying the invention is stated that “how to provide accurate detection and classification of defects in NDT/NDE.”
• Present Invention provides the technical solution to the technical problem of the existing NDT method by integrating the artificial intelligence (AI) automation system for generating a large volume of NDT datasets.

Present invention describes an AI based time efficient method and system for generating synthetic non-destructive training datasets.

• The system determines a CAD model representing the actual physical defect sample based on the received geometrical features, further including critical statistical distribution parameters, and generates a synthetic NDT dataset based on training the AI model.
• The method comprises a few steps depicted in the figures. A smart chart shows herein below:

1. Receiving real time experimental NDT datasets by the processor; & Performing numerical analysis on said dataset via numerical solution model.
2. Training a deep convolutional generative adversarial network (DCGAN) by using the generated NDT datasets with flaw geometrical features.
3. Receiving random number input vectors iteratively at the trained DCGAN; & Generating a synthetic NDT datasets for each of the received said input vector by the trained DCGAN.