Uncertainty quantification for additive manufacturing process improvement: recent advances
This paper reviews the state of the art in applying uncertainty quantification (UQ) methods to
additive manufacturing (AM). Physics-based as well as data-driven models are increasingly …
additive manufacturing (AM). Physics-based as well as data-driven models are increasingly …
Physics-informed and hybrid machine learning in additive manufacturing: application to fused filament fabrication
This article investigates several physics-informed and hybrid machine learning strategies
that incorporate physics knowledge in experimental data-driven deep-learning models for …
that incorporate physics knowledge in experimental data-driven deep-learning models for …
Optimization of fused filament fabrication process parameters under uncertainty to maximize part geometry accuracy
This work presents a novel process design optimization framework for additive
manufacturing (AM) by integrating physics-informed computational simulation models with …
manufacturing (AM) by integrating physics-informed computational simulation models with …
Active learning for adaptive surrogate model improvement in high-dimensional problems
This paper investigates a novel approach to efficiently construct and improve surrogate
models in problems with high-dimensional input and output. In this approach, the principal …
models in problems with high-dimensional input and output. In this approach, the principal …
Additive manufacturing melt pool prediction and classification via multifidelity Gaussian process surrogates
It is well known that the process parameters chosen in metal additive manufacturing (AM)
are directly related to the melt pool dimensions, which can be related to microstructure …
are directly related to the melt pool dimensions, which can be related to microstructure …
An active learning high-throughput microstructure calibration framework for solving inverse structure–process problems in materials informatics
Determining a process–structure–property relationship is the holy grail of materials science,
where both computational prediction in the forward direction and materials design in the …
where both computational prediction in the forward direction and materials design in the …
Probabilistic digital twin for additive manufacturing process design and control
This paper proposes a detailed methodology for constructing an additive manufacturing
(AM) digital twin for the laser powder bed fusion (LPBF) process. An important aspect of the …
(AM) digital twin for the laser powder bed fusion (LPBF) process. An important aspect of the …
[HTML][HTML] Design of experiments informed deep learning for modeling of directed energy deposition process with a small-size experimental dataset
In order to address the high throughput data generation challenges in the directed energy
deposition (DED) process development, a design of experiments (DOE) informed deep …
deposition (DED) process development, a design of experiments (DOE) informed deep …
Probabilistic predictive control of porosity in laser powder bed fusion
This work presents a Bayesian methodology for layer-by-layer predictive quality control of an
additively manufactured part by integrating physics-based simulation with online monitoring …
additively manufactured part by integrating physics-based simulation with online monitoring …
Characterization, propagation, and sensitivity analysis of uncertainties in the directed energy deposition process using a deep learning-based surrogate model
Uncertainties raised from process parameters, material properties, and environmental
conditions significantly impact the quality of the printed parts in the directed energy …
conditions significantly impact the quality of the printed parts in the directed energy …