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Volume 8, Issue 1

March 2022

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ISSN 2332-9017

EISSN 2332-9025

In this Issue

Editorial
Guest Editorial
Review Article
Special Papers

Editorial

Reviewer's Recognition

ASME J. Risk Uncertainty Part B. March 2022, 8(1): 010201. doi: https://doi.org/10.1115/1.4053395

Extract

View articletitled, Reviewer's Recognition

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Topics: Risk

Guest Editorial

Special Issue on Uncertainty Quantification and Management in Additive Manufacturing

Zhen Hu, Saideep Nannapaneni, Sankaran Mahadevan

ASME J. Risk Uncertainty Part B. March 2022, 8(1): 010301. doi: https://doi.org/10.1115/1.4053183

Extract

View articletitled, Special Issue on Uncertainty Quantification and Management in Additive Manufacturing

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Topics: Additive manufacturing , Uncertainty quantification , Uncertainty

Review Article

Uncertainty Quantification for Additive Manufacturing Process Improvement: Recent Advances

Sankaran Mahadevan, Paromita Nath, Zhen Hu

ASME J. Risk Uncertainty Part B. March 2022, 8(1): 010801. doi: https://doi.org/10.1115/1.4053184

Abstract

View articletitled, Uncertainty Quantification for Additive Manufacturing Process Improvement: Recent Advances

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Topics: Additive manufacturing , Calibration , Optimization , Physics , Uncertainty , Uncertainty quantification , Errors , Lasers , Manufacturing

Special Papers

Conservative Confidence Interval Prediction in Fused Deposition Modeling Process With Linear Optimization Approach

Arup Dey, Nita Yodo

ASME J. Risk Uncertainty Part B. March 2022, 8(1): 011101. doi: https://doi.org/10.1115/1.4051750

Abstract

View articletitled, Conservative Confidence Interval Prediction in Fused Deposition Modeling Process With Linear Optimization Approach

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Topics: Linear programming , Modeling , Regression models , Uncertainty , Compressive strength , Data collection , Optimization , Errors

Gaussian Process-Based Model to Optimize Additively Manufactured Powder Microstructures From Phase Field Modeling

Arunabha Batabyal, Sugrim Sagar, Jian Zhang, Tejesh Dube, Xuehui Yang, Jing Zhang

ASME J. Risk Uncertainty Part B. March 2022, 8(1): 011102. doi: https://doi.org/10.1115/1.4051745

Abstract

View articletitled, Gaussian Process-Based Model to Optimize Additively Manufactured Powder Microstructures From Phase Field Modeling

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Topics: Optimization , Particulate matter , Machine learning , Additive manufacturing , Sintering

Model Predictive Control of Melt Pool Size for the Laser Powder Bed Fusion Process Under Process Uncertainty

Zhimin Xi

ASME J. Risk Uncertainty Part B. March 2022, 8(1): 011103. doi: https://doi.org/10.1115/1.4051746

Abstract

View articletitled, Model Predictive Control of Melt Pool Size for the Laser Powder Bed Fusion Process Under Process Uncertainty

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Topics: Lasers , Machine learning , Predictive control , Uncertainty

A Transfer Learning-Based Multi-Fidelity Point-Cloud Neural Network Approach for Melt Pool Modeling in Additive Manufacturing

Xufeng Huang, Tingli Xie, Zhuo Wang, Lei Chen, Qi Zhou, Zhen Hu

ASME J. Risk Uncertainty Part B. March 2022, 8(1): 011104. doi: https://doi.org/10.1115/1.4051749

Abstract

View articletitled, A Transfer Learning-Based Multi-Fidelity Point-Cloud Neural Network Approach for Melt Pool Modeling in Additive Manufacturing

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Topics: Additive manufacturing , Artificial neural networks , Modeling , Uncertainty , Cathode ray oscilloscopes , Electron beams , Temperature

Uncertainty Quantification Analysis on Silicon Electrodeposition Process Via Numerical Simulation Methods

Zhuoyuan Zheng, Pingfeng Wang

ASME J. Risk Uncertainty Part B. March 2022, 8(1): 011105. doi: https://doi.org/10.1115/1.4051700

Abstract

View articletitled, Uncertainty Quantification Analysis on Silicon Electrodeposition Process Via Numerical Simulation Methods

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Topics: Electrodeposition , Electrodes , Electrolytes , Finite element model , Silicon , Uncertainty quantification , Temperature , Anodes , Design , Simulation results

Application of Deep Transfer Learning and Uncertainty Quantification for Process Identification in Powder Bed Fusion

Piyush Pandita, Sayan Ghosh, Vipul K. Gupta, Andrey Meshkov, Liping Wang

ASME J. Risk Uncertainty Part B. March 2022, 8(1): 011106. doi: https://doi.org/10.1115/1.4051748

Abstract

View articletitled, Application of Deep Transfer Learning and Uncertainty Quantification for Process Identification in Powder Bed Fusion

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Topics: Additive manufacturing , Design , Modeling , Simulation , Uncertainty quantification , Lasers , Computers , Physics

Fabrication, Mechanics, and Reliability Analysis for Three-Dimensional Printed Lattice Designs

Nitin Nagesh Kulkarni, Stephen Ekwaro-Osire, Paul F. Egan

ASME J. Risk Uncertainty Part B. March 2022, 8(1): 011107. doi: https://doi.org/10.1115/1.4051747

Abstract

View articletitled, Fabrication, Mechanics, and Reliability Analysis for Three-Dimensional Printed Lattice Designs

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Topics: Density , Design , Event history analysis , Manufacturing , Printing , Reliability , Yield stress , Additive manufacturing , Probability , Stress

Data-Driven Sensitivity Analysis for Static Mechanical Properties of Additively Manufactured Ti–6Al–4V

Antriksh Sharma, Jie Chen, Evan Diewald, Anahita Imanian, Jack Beuth, Yongming Liu

ASME J. Risk Uncertainty Part B. March 2022, 8(1): 011108. doi: https://doi.org/10.1115/1.4051799

Abstract

View articletitled, Data-Driven Sensitivity Analysis for Static Mechanical Properties of Additively Manufactured Ti–6Al–4V

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Topics: Alloys , Elongation , Lasers , Manufacturing , Sensitivity analysis , Temperature , Tensile strength , Melting , Metals , Sintering

Accelerating Additive Design With Probabilistic Machine Learning

Yiming Zhang, Sreekar Karnati, Soumya Nag, Neil Johnson, Genghis Khan, Brandon Ribic

ASME J. Risk Uncertainty Part B. March 2022, 8(1): 011109. doi: https://doi.org/10.1115/1.4051699

Abstract

View articletitled, Accelerating Additive Design With Probabilistic Machine Learning

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Topics: Additive manufacturing , Design , Machine learning , Modeling , Optimization , Sensitivity analysis , Thin wall structures , Uncertainty , Tensile strength

On Characterizing Uncertainty Sources in Laser Powder-Bed Fusion Additive Manufacturing Models

Tesfaye Moges, Kevontrez Jones, Shaw Feng, Paul Witherell, Gaurav Ameta

ASME J. Risk Uncertainty Part B. March 2022, 8(1): 011110. doi: https://doi.org/10.1115/1.4052039

Abstract

View articletitled, On Characterizing Uncertainty Sources in Laser Powder-Bed Fusion Additive Manufacturing Models

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Topics: Lasers , Uncertainty

Bayesian Calibration of Multiple Coupled Simulation Models for Metal Additive Manufacturing: A Bayesian Network Approach

Jiahui Ye, Mohamad Mahmoudi, Kubra Karayagiz, Luke Johnson, Raiyan Seede, Ibrahim Karaman, Raymundo Arroyave, Alaa Elwany

ASME J. Risk Uncertainty Part B. March 2022, 8(1): 011111. doi: https://doi.org/10.1115/1.4052270

Abstract

View articletitled, Bayesian Calibration of Multiple Coupled Simulation Models for Metal Additive Manufacturing: A Bayesian Network Approach

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Topics: Calibration , Simulation models , Alloys , Metals , Additive manufacturing , Uncertainty quantification , Lasers

Multi-Objective Optimization Under Uncertainty of Part Quality in Fused Filament Fabrication

Berkcan Kapusuzoglu, Paromita Nath, Matthew Sato, Sankaran Mahadevan, Paul Witherell

ASME J. Risk Uncertainty Part B. March 2022, 8(1): 011112. doi: https://doi.org/10.1115/1.4053181

Abstract

View articletitled, Multi-Objective Optimization Under Uncertainty of Part Quality in Fused Filament Fabrication

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Topics: Artificial neural networks , Design , Manufacturing , Optimization , Pareto optimization , Uncertainty

All Issues

Select Articles from Part A: Civil Engineering

State-of-the-Art Reviews

Resilience-Based Design of Infrastructure: Review of Models, Methodologies, and Computational Tools

Mahdi Shadabfar, Mojtaba Mahsuli, Yi Zhang, Yadong Xue, Bilal M. Ayyub, Ph.D., Hongwei Huang, Ph.D., Ricardo A. Medina, Ph.D.

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. March 2022, 8(1): 03121004. doi: https://doi.org/10.1061/AJRUA6.0001184

Abstract

Topics: Design , Resilience

Technical Papers

Comparative Study of Data-Driven Models in Motor RUL Estimation

Ahin Banerjee, Sanjay K. Gupta, Chandrasekhar Putcha

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. March 2022, 8(1): 04021067. doi: https://doi.org/10.1061/AJRUA6.0001186

Abstract

Topics: Engines , Motors

Influence of Surface Roughness Uncertainties on Design of Structures with Open and Suburban Exposures

Tarak N. Nandi, Ph.D., Adam L. Pintar, Ph.D., Emil Simiu, Ph.D.

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. March 2022, 8(1): 04021068. doi: https://doi.org/10.1061/AJRUA6.0001191

Abstract

Topics: Design , Surface roughness , Uncertainty

Real-Time Risk Assessment of Tunneling-Induced Building Damage Considering Polymorphic Uncertainty

Ba Trung Cao, Ph.D., Markus Obel, Ph.D., Steffen Freitag, Ph.D., Lukas Heußner, Günther Meschke, Peter Mark

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. March 2022, 8(1): 04021069. doi: https://doi.org/10.1061/AJRUA6.0001192

Abstract

Topics: Damage , Risk assessment , Tunnel construction , Uncertainty

Managing Cost Risks in Oil and Gas Construction Projects: Root Causes of Cost Overruns

Iraj Esmaeili, Hamed Kashani

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. March 2022, 8(1): 04021072. doi: https://doi.org/10.1061/AJRUA6.0001193

Abstract

Topics: Construction

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