[HTML][HTML] Towards a generic physics-based machine learning model for geometry invariant thermal history prediction in additive manufacturing
Additive manufacturing (AM) is an emerging manufacturing technology that constructs
complex parts through layer-by-layer deposition. The prediction and control of thermal fields …
complex parts through layer-by-layer deposition. The prediction and control of thermal fields …
Knowledge-based bidirectional thermal variable modelling for directed energy deposition additive manufacturing
Directed energy deposition additive manufacturing (DED-AM) has gained significant interest
in producing large-scale metallic structural components. In this paper, a knowledge-based …
in producing large-scale metallic structural components. In this paper, a knowledge-based …
GPyro: uncertainty-aware temperature predictions for additive manufacturing
In additive manufacturing, process-induced temperature profiles are directly linked to part
properties, and their prediction is crucial for achieving high-quality products. Temperature …
properties, and their prediction is crucial for achieving high-quality products. Temperature …
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 …
Online thermal field prediction for metal additive manufacturing of thin walls
Various data-driven modeling methods have been developed to predict the thermal field in
metal additive manufacturing (AM). The generalization capability of these models has been …
metal additive manufacturing (AM). The generalization capability of these models has been …
Rapid and accurate prediction of temperature evolution in wire plus arc additive manufacturing using feedforward neural network
This article proposes an approach based on a feedforward neural network (FFNN-SM) and
computational simulations to rapidly predict thermal cycles in multi-layer single-bead walls …
computational simulations to rapidly predict thermal cycles in multi-layer single-bead walls …
A framework for the robust optimization under uncertainty in additive manufacturing
This paper introduces a conceptual framework for the robust optimization under uncertainty
in the directed energy deposition (DED) of M4 High-Speed Steel. The goal is to identify …
in the directed energy deposition (DED) of M4 High-Speed Steel. The goal is to identify …
[HTML][HTML] Thermal field prediction in DED manufacturing process using Artificial Neural Network
In the last decade, machine learning is increasingly attracting researchers in several
scientific areas and, in particular, in the additive manufacturing field. Meanwhile, this …
scientific areas and, in particular, in the additive manufacturing field. Meanwhile, this …
[PDF][PDF] Efficient prediction of thermal history in wire and arc-directed energy deposition combining machine learning and numerical simulation
Among metallic additive manufacturing technologies, wire and arc-directed energy
deposition (WADED) is recently adopted to manufacture large industrial components. In this …
deposition (WADED) is recently adopted to manufacture large industrial components. In this …
[PDF][PDF] Thermal field pr Thermal field prediction in DED manuf ediction in DED manufacturing pr acturing process using Artificial Neur ocess using Artificial Neural …
S Fetni, QDT Pham, LDVX Tran, HS Tran… - Progress in …, 2022 - researchgate.net
In the last decade, machine learning is increasingly attracting researchers in several
scientific areas and, in particular, in the additive manufacturing field. Meanwhile, this …
scientific areas and, in particular, in the additive manufacturing field. Meanwhile, this …