PhD Thesis

Acoustic Based Geometric defects identification and process parameter map generation using machine learning Models for Wire Arc Additive Manufacturing(WAAM).

WAAM Workflow and Focused research:

WAAM different steps and My focused area

Task:

  1. Collect and process the Acoustic signals during the metal 3D printing process.
  2. Collect and analyze the 3D point cloud from the printed object for experimental purpose.
  3. Develop a novel dataset labeling algorithm to classify good and defective signals.
  4. Construct different machine learning (ML) models with different acoustic features to identify Geometric defects for the metal additive manufacturing process.
  5. Build process parameter maps based on the probability for different materials so that good quality printing process parameters can be achieved.

```