Events Calendar

07 Apr
Swanson School of Engineering
Event Type

Defenses

Topic

Research

Target Audience

Faculty, Graduate Students, Postdocs

University Unit
Department of Mechanical Engineering and Materials Science
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Integrated Computational Materials Design for Alloy Additive Manufacturing: Introducing Data-Driven Approach to Physical Metallurgy, Xin Wang

This is a past event.

Xin Wang

Integrated Computational Materials Design for Alloy Additive Manufacturing: Introducing Data-Driven Approach to Physical Metallurgy

 

ABSTRACT:

 

Additive manufacturing (AM) attracts broad interest from academic and industrial communities due to its ability to produce complex geometries, fast prototyping, and in-situ repair. However, the AM involves many parameters and uncertainties, which leads to property variation in products. For instance, the influence of chemical composition variation on the AM component performance is an important parameter that has not been thoroughly studied. Moreover, the micro-segregation in AM caused by the high-cooling rate also makes the as-built structure and properties vary locally within the prints. The alloy bulk properties may change with different AM processing parameters. Such variations must be studied while the experimental study is time- and cost-consuming.

This thesis introduced the data-driven approach, such as statistical analysis, machine learning, and Bayesian inference combined with integrated computational materials engineering (ICME), to address the AM property variation challenges. Firstly, the process-structure-property-performance (PSPP) relationships for AM high-strength low-alloy (HSLA) 115 steel with post-treatment were established to study feedstock composition impact on the AM print performance. The high-throughput calculations of the developed ICME framework quantified uncertainties in critical properties, such as yield strength, printability, and low-temperature ductility, with the feedstock composition variation. Moreover, the machine learning approach was implemented to surrogate the ICME model framework for an accelerated simulation for a more comprehensive study and robust feedstock composition optimization. Finally, the optimized HSLA 115 steel was printed. It showed excellent properties even though the printed composition differs from the designed composition, which proves the successfulness of the design with uncertainty in composition. Moreover, this thesis also studied the impact of AM 316L stainless steel segregation on the deformation mechanism and mechanical properties. A machine learning-based stacking fault energy (SFE) predictor, which surpassed the conventional thermodynamic and empirical models, was developed to predict the SFE change with segregation in AM. This data-driven model successfully explained the twinning behavior in as-built AM 316L. Finally, a Bayesian-based model calibration was applied to understand the considerable variation in the mechanical properties of CoCrFeMnNi HEAs with different AM techniques and processing parameters. It reveals the importance of dislocation density and grain size in strengthening the AM products, and correlation analysis was conducted to find the relationship between the strengthening mechanism and processing parameters.

Friday, April 7 at 10:00 a.m.

Benedum Hall, 611
3700 O'Hara Street, Pittsburgh, PA 15261

Integrated Computational Materials Design for Alloy Additive Manufacturing: Introducing Data-Driven Approach to Physical Metallurgy, Xin Wang

Xin Wang

Integrated Computational Materials Design for Alloy Additive Manufacturing: Introducing Data-Driven Approach to Physical Metallurgy

 

ABSTRACT:

 

Additive manufacturing (AM) attracts broad interest from academic and industrial communities due to its ability to produce complex geometries, fast prototyping, and in-situ repair. However, the AM involves many parameters and uncertainties, which leads to property variation in products. For instance, the influence of chemical composition variation on the AM component performance is an important parameter that has not been thoroughly studied. Moreover, the micro-segregation in AM caused by the high-cooling rate also makes the as-built structure and properties vary locally within the prints. The alloy bulk properties may change with different AM processing parameters. Such variations must be studied while the experimental study is time- and cost-consuming.

This thesis introduced the data-driven approach, such as statistical analysis, machine learning, and Bayesian inference combined with integrated computational materials engineering (ICME), to address the AM property variation challenges. Firstly, the process-structure-property-performance (PSPP) relationships for AM high-strength low-alloy (HSLA) 115 steel with post-treatment were established to study feedstock composition impact on the AM print performance. The high-throughput calculations of the developed ICME framework quantified uncertainties in critical properties, such as yield strength, printability, and low-temperature ductility, with the feedstock composition variation. Moreover, the machine learning approach was implemented to surrogate the ICME model framework for an accelerated simulation for a more comprehensive study and robust feedstock composition optimization. Finally, the optimized HSLA 115 steel was printed. It showed excellent properties even though the printed composition differs from the designed composition, which proves the successfulness of the design with uncertainty in composition. Moreover, this thesis also studied the impact of AM 316L stainless steel segregation on the deformation mechanism and mechanical properties. A machine learning-based stacking fault energy (SFE) predictor, which surpassed the conventional thermodynamic and empirical models, was developed to predict the SFE change with segregation in AM. This data-driven model successfully explained the twinning behavior in as-built AM 316L. Finally, a Bayesian-based model calibration was applied to understand the considerable variation in the mechanical properties of CoCrFeMnNi HEAs with different AM techniques and processing parameters. It reveals the importance of dislocation density and grain size in strengthening the AM products, and correlation analysis was conducted to find the relationship between the strengthening mechanism and processing parameters.

Friday, April 7 at 10:00 a.m.

Benedum Hall, 611
3700 O'Hara Street, Pittsburgh, PA 15261

Event Type

Defenses

Topic

Research

Target Audience

Faculty, Graduate Students, Postdocs

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