TY - JOUR
T1 - Process mapping and anomaly detection in laser wire directed energy deposition additive manufacturing using in-situ imaging and process-aware machine learning
AU - Assad, Anis
AU - Bevans, Benjamin D.
AU - Potter, Willem
AU - Rao, Prahalada
AU - Cormier, Denis
AU - Deschamps, Fernando
AU - Hamilton, Jakob
AU - Rivero, Iris V.
PY - 2024/9
Y1 - 2024/9
N2 - This work concerns the laser wire directed energy deposition (LW-DED) additive manufacturing process. The objectives were two-fold: (1) process mapping – demarcating the process states as a function of the processing parameters; and (2) process monitoring – detecting process anomalies (instabilities) using data acquired from an in-situ meltpool imaging sensor. The LW-DED process enables high-throughput, near-net shape manufacturing. Without rigorous parameter control, however, LW-DED often introduces defects due to stochastic process drifts. To enhance scalability and reliability, it is essential to understand how LW-DED parameters affect processing regimes, and detect deleterious process drifts. In this work, single-track experiments were conducted over 128 combinations of laser power, scanning velocity, and linear mass density. Four process states were observed via high-speed imaging and delineated as stable, dripping, stubbing, and incomplete melting regimes. Physically intuitive meltpool features were used to train simple machine learning models for classifying the process state into one of the four regimes. The approach was benchmarked against computationally intense, black-box deep machine learning models that directly use as-received meltpool images. Using only six intuitive meltpool morphology and intensity signatures, the approach classified the LW-DED process state with statistical fidelity approaching 90 % (F1-score) compared to F1-score 87 % for deep learning models.
AB - This work concerns the laser wire directed energy deposition (LW-DED) additive manufacturing process. The objectives were two-fold: (1) process mapping – demarcating the process states as a function of the processing parameters; and (2) process monitoring – detecting process anomalies (instabilities) using data acquired from an in-situ meltpool imaging sensor. The LW-DED process enables high-throughput, near-net shape manufacturing. Without rigorous parameter control, however, LW-DED often introduces defects due to stochastic process drifts. To enhance scalability and reliability, it is essential to understand how LW-DED parameters affect processing regimes, and detect deleterious process drifts. In this work, single-track experiments were conducted over 128 combinations of laser power, scanning velocity, and linear mass density. Four process states were observed via high-speed imaging and delineated as stable, dripping, stubbing, and incomplete melting regimes. Physically intuitive meltpool features were used to train simple machine learning models for classifying the process state into one of the four regimes. The approach was benchmarked against computationally intense, black-box deep machine learning models that directly use as-received meltpool images. Using only six intuitive meltpool morphology and intensity signatures, the approach classified the LW-DED process state with statistical fidelity approaching 90 % (F1-score) compared to F1-score 87 % for deep learning models.
KW - Deep learning
KW - LW-DED process mapping
KW - Meltpool imaging
KW - Process stability
KW - Process-aware machine learning
U2 - 10.1016/j.matdes.2024.113281
DO - 10.1016/j.matdes.2024.113281
M3 - Journal article
SN - 0264-1275
VL - 245
JO - Materials & Design
JF - Materials & Design
M1 - 113281
ER -