TY - JOUR
T1 - Comparison of early stopping neural network and random forest for in-situ quality prediction in laser based additive manufacturing
AU - Behnke, Matthew
AU - Guo, Shenghan
AU - Guo, Weihong
N1 - Funding Information:
The authors would like to thank the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS) at Rutgers University for providing research experience under the National Science Foundation (NSF)-sponsored DIMACS Research Experiences for Undergraduates (REU) program (grant CCF-1852215). The authors would also like to thank Prof. Linkan Bian’s team for providing the case study data and domain knowledge.
Publisher Copyright:
© 2021 The Authors. Published by Elsevier B.V.
PY - 2021
Y1 - 2021
N2 - 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 well-known characteristic of the LBAM process. Porosity defects, which have hampered the expansive adoption of LBAM, is correlated with the melt pool characteristic that occurs throughout the LBAM process. 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.
AB - 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 well-known characteristic of the LBAM process. Porosity defects, which have hampered the expansive adoption of LBAM, is correlated with the melt pool characteristic that occurs throughout the LBAM process. 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.
KW - Deep Learning
KW - Laser Based Additive Manufacturing
KW - Machine Learning
KW - Random Forest
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U2 - 10.1016/j.promfg.2021.06.065
DO - 10.1016/j.promfg.2021.06.065
M3 - Conference article
AN - SCOPUS:85117906403
SN - 2351-9789
VL - 53
SP - 656
EP - 663
JO - Procedia Manufacturing
JF - Procedia Manufacturing
T2 - 49th SME North American Manufacturing Research Conference, NAMRC 2021
Y2 - 21 June 2021 through 25 June 2021
ER -