Method and system for assessing internal defects of a material

A defect assessment system for assessing one or more defects in a material, the defect assessment system comprising:

• a memory;
• a processor, coupled to the memory, wherein the processor is configured to:
• extract one or more assessment parameters from ultrasound waves propagated through a cross-section of the material at each of one or more predefined alignment angles;
• identify presence of one or more defects in the material by analysing the one or more assessment parameters using a first machine learning model;
• determine a position of each of the one or more defects present in the material based on a graphical representation of the one or more defects;
• and
• determine a severity score and size of each of the one or more defects, corresponding to the position determined on the graphical representation of each of the one or more defects, using a second machine learning model.

A non-transitory computer readable medium including instructions stored thereon that when processed by at least one processor, cause a sequence designing system to perform operations comprising:

• extracting one or more assessment parameters from ultrasound waves propagated through a cross-section of the material at each of one or more predefined alignment angles;
• identifying presence of one or more defects in the material by analysing the one or more assessment parameters using a first machine learning model;
• determining a position of each of the one or more defects present in the material based on a graphical representation of the one or more defects;
• and
• determining a severity score and size of each of the one or more defects, corresponding to the position of each of the one or more defects determined on the graphical representation using a second machine learning model.