Benjamin Bevans

Research Assistant Professor

University of Oklahoma

United States

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Benjamin Bevans is a Research Assistant Professor at the University of Oklahoma, USA. His research focuses on additive manufacturing, particularly in laser powder bed fusion methods and in-situ monitoring techniques. Recent articles include studies on modeling and simulation, process qualification, and flaw detection in additive manufacturing processes.

Articles (10)

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2025

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2024

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2024

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2023

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2023

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2023

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2022

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2022

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2022

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2022

Collaborators (5)

André Ramalho

Universidade Nova de Lisboa

PORTUGAL

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Yingtao Liu

Associate Professor

University of Oklahoma

UNITED STATES

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Prahalada Rao

Associate Professor

Virginia Tech

UNITED STATES

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Zahed Siddique

University of Oklahoma

UNITED STATES

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Harold Halliday

Coordinator

Navajo Technical University

UNITED STATES

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Benjamin Bevans

Research Interests

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Articles (10)

A Review of Modeling, Simulation, and Process Qualification of Additively Manufactured Metal Components via the Laser Powder Bed Fusion Method

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

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

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

Prediction of recoater crash in laser powder bed fusion additive manufacturing using graph theory thermomechanical modeling

Monitoring and flaw detection during wire-based directed energy deposition using in-situ acoustic sensing and wavelet graph signal analysis

Closed-loop Control of Meltpool Temperature in Directed Energy Deposition

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

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

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

Collaborators (5)

André Ramalho

Yingtao Liu

Prahalada Rao

Zahed Siddique

Harold Halliday

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