TY - JOUR
T1 - Integrated approach for AlSi10Mg rapid part qualification
T2 - FEM, machine learning, and experimental verification in LPBF-based additive manufacturing process
AU - Mahmood, Muhammad Arif
AU - Ishfaq, Kashif
AU - Oane, Mihai
AU - Khraisheh, Marwan
AU - Liou, Frank
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
PY - 2025/1
Y1 - 2025/1
N2 - An integrated approach combining finite element modelling, machine learning, and experimental verification was proposed for developing process maps to optimize the LPBF process for AlSi10Mg alloy. A transient thermal simulation model was validated to predict single-layer melt pool size by modifying laser beam power, scan rate, feedstock bed depth, and preheating of feedstock. Using the verified model, a pool of data was generated to develop a backpropagation neural network to predict melt pool dimensional ratios indicating printing defects. It was found that beyond process parameters, powder bed thickness and preheating temperature impacted defect formation. Excessively high preheating temperatures increased the lack of fusion defects by transforming melt pool dynamics from conduction to keyhole mode. Optimal combinations were identified as 30.0 μm thickness with 90.0 and 120.0 °C preheating and 50.0 μm thickness with 120.0 °C preheating. By reducing iterative physical testing, the integrated process mapping approach enables accelerated qualification of LPBF parameters for AlSi10Mg alloy.
AB - An integrated approach combining finite element modelling, machine learning, and experimental verification was proposed for developing process maps to optimize the LPBF process for AlSi10Mg alloy. A transient thermal simulation model was validated to predict single-layer melt pool size by modifying laser beam power, scan rate, feedstock bed depth, and preheating of feedstock. Using the verified model, a pool of data was generated to develop a backpropagation neural network to predict melt pool dimensional ratios indicating printing defects. It was found that beyond process parameters, powder bed thickness and preheating temperature impacted defect formation. Excessively high preheating temperatures increased the lack of fusion defects by transforming melt pool dynamics from conduction to keyhole mode. Optimal combinations were identified as 30.0 μm thickness with 90.0 and 120.0 °C preheating and 50.0 μm thickness with 120.0 °C preheating. By reducing iterative physical testing, the integrated process mapping approach enables accelerated qualification of LPBF parameters for AlSi10Mg alloy.
KW - Additive manufacturing
KW - AlSi10Mg
KW - LPBF
KW - Part quantification and qualification
KW - Printing defects by process maps
UR - http://www.scopus.com/inward/record.url?scp=85195576593&partnerID=8YFLogxK
U2 - 10.1007/s40964-024-00683-0
DO - 10.1007/s40964-024-00683-0
M3 - Article
AN - SCOPUS:85195576593
SN - 2363-9512
VL - 10
SP - 861
EP - 874
JO - Progress in Additive Manufacturing
JF - Progress in Additive Manufacturing
IS - 1
M1 - 163735
ER -