- Random Forest and Early Stopping Neural Network Methods for In-Situ Quality Prediction in Laser-Based Additive Manufacturing
- Project Year:
2020
- REU Student (s):
Matthew Behnke | Colorado Mesa University CO
- Student 1 Institution:
Colorado Mesa University
- Project Mentor:
Weihong 'Grace' Guo
- Project Mentor Area:
Industrial and Systems Engineering
- Project Abstract:
Laser-Based Additive Manufacturing (LBAM) is a promising process in manufacturing that allows for capabilities in producing complex parts with multiple functionalities for a large array of engineering applications. Melt pool is a defining characteristic of the LBAM process and known defects of porosity in the melt pool and LBAM process has prevented the expansive adoption of LBAM. High-speed monitors that can capture the LBAM process have created the possibility for in-situ monitoring for defects and abnormalities. This paper focuses on augmenting knowledge of the relation between the LBAM process and porosity and providing models that could efficiently, accurately, and consistently predict defects and anomalies in-situ for the LBAM process. Two models are presented in this paper, Random Forest Classifier and Early Stopping Neural Network, which are used to classify pyrometer images and categorize if those images will result in defects. Both methods can achieve over 99% accuracy in an efficient manner, which would create an in-situ method for quality prediction in the LBAM process.
