activity
20202023
most citedA Physics-Informed Machine Learning Model for Porosity Analysis in Laser Powder Bed Fusion Additive Manufacturing

4 citations · 10 across the 6 of their papers we have counts for

collaborators

6 papers

physics.data-an2023★ 2 cited

Unsupervised Learning of Part Similarity for Goal-Guided Accelerated Experiment Design in Metal Additive Manufacturing

Rui Liu, Sen Liu, Xiaoli Zhang

Metal additive manufacturing is gaining broad interest and increased use in the industrial and academic fields. However, the quantification and commercialization of standard parts…

cond-mat.mtrl-sci2021★ 3 cited

Comprehensive process-molten pool relations modeling using CNN for wire-feed laser additive manufacturing

Noopur Jamnikar, Sen Liu, Craig Brice +1

Wire-feed laser additive manufacturing (WLAM) is gaining wide interest due to its high level of automation, high deposition rates, and good quality of printed parts. In-process mon…

cond-mat.mtrl-sci2021★ 1 cited

Machine learning based in situ quality estimation by molten pool condition-quality relations modeling using experimental data

Noopur Jamnikar, Sen Liu, Craig Brice +1

The advancement of machine learning promises the ability to accelerate the adoption of new processes and property designs for metal additive manufacturing. The molten pool geometry…

cond-mat.mtrl-sci2021

Comprehensive Quality Investigations of Wire-feed Additive Manufacturing by Learning of Experimental Data

Sen Liu, Craig Brice, Xiaoli Zhang

Wire-feed laser additive manufacturing is an emerging fabrication technique capable of highly automated large-scale volume production that can reduce both material waste and overal…

cs.LG2021★ 4 cited

A Physics-Informed Machine Learning Model for Porosity Analysis in Laser Powder Bed Fusion Additive Manufacturing

Rui Liu, Sen Liu, Xiaoli Zhang

To control part quality, it is critical to analyze pore generation mechanisms, laying theoretical foundation for future porosity control. Current porosity analysis models use machi…

cond-mat.mtrl-sci2020

Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstration

Sen Liu, Branden B. Kappes, Behnam Amin-ahmadi +3

Machine learning (ML) is shown to predict new alloys and their performances in a high dimensional, multiple-target-property design space that considers chemistry, multi-step proces…