Advances in computational modeling for laser powder bed fusion additive manufacturing: A comprehensive review of finite element techniques and strategies

D Sarkar, A Kapil, A Sharma - Additive Manufacturing, 2024 - Elsevier
The laser powder bed fusion process, a prominent additive manufacturing method, involves
intricate thermal dynamics and stress distribution. Despite the mature knowledge base in …

A comprehensive review of recent advances in laser powder bed fusion characteristics modeling: metallurgical and defects

SF Nabavi, H Dalir, A Farshidianfar - The International Journal of …, 2024 - Springer
This comprehensive review explores recent advancements in laser powder bed fusion
(LPBF) modeling, with a particular focus on metallurgical, temperature, and defect aspects …

Towards a digital twin framework in additive manufacturing: Machine learning and bayesian optimization for time series process optimization

V Karkaria, A Goeckner, R Zha, J Chen, J Zhang… - Journal of Manufacturing …, 2024 - Elsevier
Laser directed-energy deposition (DED) offers notable advantages in additive
manufacturing (AM) for producing intricate geometries and facilitating material functional …

Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing

A Gaikwad, RJ Williams, H de Winton, BD Bevans… - Materials & Design, 2022 - Elsevier
Finding actionable trends in laser-based metal additive manufacturing process monitoring
data is challenging owing to the diversity and complexity of the underlying physical …

Monitoring and prediction of porosity in laser powder bed fusion using physics-informed meltpool signatures and machine learning

Z Smoqi, A Gaikwad, B Bevans, MH Kobir… - Journal of Materials …, 2022 - Elsevier
In this work we accomplished the monitoring and prediction of porosity in laser powder bed
fusion (LPBF) additive manufacturing process. This objective was realized by extracting …

Digitally twinned additive manufacturing: Detecting flaws in laser powder bed fusion by combining thermal simulations with in-situ meltpool sensor data

R Yavari, A Riensche, E Tekerek, L Jacquemetton… - Materials & Design, 2021 - Elsevier
The goal of this research is the in-situ detection of flaw formation in metal parts made using
the laser powder bed fusion (LPBF) additive manufacturing process. This is an important …

Feedforward control of thermal history in laser powder bed fusion: Toward physics-based optimization of processing parameters

A Riensche, BD Bevans, Z Smoqi, R Yavari… - Materials & Design, 2022 - Elsevier
We developed and applied a model-driven feedforward control approach to mitigate thermal-
induced flaw formation in laser powder bed fusion (LPBF) additive manufacturing process …

[HTML][HTML] Predicting meltpool depth and primary dendritic arm spacing in laser powder bed fusion additive manufacturing using physics-based machine learning

AR Riensche, BD Bevans, G King, A Krishnan… - Materials & Design, 2024 - Elsevier
The long-term goal of this work is to predict and control the microstructure evolution in metal
additive manufacturing processes. In pursuit of this goal, we developed and applied an …

Heterogeneous sensor data fusion for multiscale, shape agnostic flaw detection in laser powder bed fusion additive manufacturing

B Bevans, C Barrett, T Spears, A Gaikwad… - Virtual and Physical …, 2023 - Taylor & Francis
We developed and applied a novel approach for shape agnostic detection of multiscale
flaws in laser powder bed fusion (LPBF) additive manufacturing using heterogenous in-situ …

Digital twins for rapid in-situ qualification of part quality in laser powder bed fusion additive manufacturing

BD Bevans, A Carrington, A Riensche, A Tenequer… - Additive …, 2024 - Elsevier
This work concerns the laser powder bed fusion (LPBF) additive manufacturing process.
Currently, LPBF parts are inspected post-process using such techniques as X-ray computed …