Application of Bayesian Neural Network Modeling to Characterize the Interrelationship between Microstructure and Mechanical Property in Alpha+Beta-Titanium Alloys
| Main Author: | |
|---|---|
| Language: | English |
| Published: |
The Ohio State University / OhioLINK
2010
|
| Subjects: | |
| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=osu1275402649 |
| id |
ndltd-OhioLink-oai-etd.ohiolink.edu-osu1275402649 |
|---|---|
| record_format |
oai_dc |
| spelling |
ndltd-OhioLink-oai-etd.ohiolink.edu-osu12754026492021-08-03T05:59:47Z Application of Bayesian Neural Network Modeling to Characterize the Interrelationship between Microstructure and Mechanical Property in Alpha+Beta-Titanium Alloys Koduri, Santhosh K. Materials Science Metallurgy Titanium Alloys Neural Networks Phase Trasformations Fracture Toughness <p>Titanium alloys, especially α+β titanium alloys are used extensively in the aerospace industry because of their attractive balance of properties. The mechanical properties of these materials are very much sensitive to their microstructure. Microstructure in these alloys can be controlled essentially through alloy composition and various thermomechanical processing routes. Microstructures in these alloys are characterized in terms of size, distribution and volume fraction of both α (HCP crystal structure) and β (BCC crystal structure) phases. The above-mentioned features can coexist and span different length scales. The interrelationships between the microstructure and mechanical properties are characterized qualitatively in the literature. Physics based models are difficult to implement due to the presence of a wide variety of microstructural features with different length scales and mutual interaction of these features. The modeling of such properties is much more complex when composition is added as an additional degree of freedom. </p><p>In this work neural network models with a Bayesian framework have been employed to characterize the microstructure and mechanical property interrelationships in α+β Ti alloys based on Ti-xAl-yV (x=4.76 to 6.55; y=3.30 to 4.45) with controlled variations in interstitial oxygen (O) and Fe (O=0.07 wt% to 0.20; Fe =0.11wt% to 0.41). These alloys are subjected to various heat treatments and thermomechanical processing conditions such as β annealing and α+β processing to obtain a range of microstructure and mechanical properties. The important microstructural features in α+β processed α+β titanium alloys are equiaxed alpha grain size, volume fraction of equiaxed alpha grains, width of the α lamellae in transformed β matrix and important features in β heat treated α+β titanium alloys are size of α colony, width of the α lamellae, prior β grain size, volume fraction of colony and grain boundary α thickness. A database is populated with the above-mentioned quantified microstructural information, composition and mechanical properties. The mechanical properties predicted in this study are tensile properties and fracture toughness. Based on the controlled virtual experiments conducted using neural networks on α+β processed alloys suggested important microstructural features that will affect tensile properties are size of the equiaxed alpha grain and volume fraction of equiaxed alpha. The controlled virtual experiments on β heat-treated alloys suggested important microstructural features such as width of the α lamellae, α colony size and prior β grain size have negative influence on tensile properties.</p><p>The virtual experiments conducted on alloys which are processed in the α+β phase field suggested that the size of the equiaxed alpha is an important variable which increases the fracture toughness. In β-processed alloys, important microstructural features such as size of the α colony decrease the fracture toughness while width of the α lamellae and prior β grain size increase the fracture toughness. The alloying elements such Al, O and Fe improve the yield strength of both α+β processed and β processed α+β titanium alloys. The O and Al have negative influence on fracture toughness while Fe has positive influence on fracture toughness.</p> 2010-09-03 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1275402649 http://rave.ohiolink.edu/etdc/view?acc_num=osu1275402649 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
| collection |
NDLTD |
| language |
English |
| sources |
NDLTD |
| topic |
Materials Science Metallurgy Titanium Alloys Neural Networks Phase Trasformations Fracture Toughness |
| spellingShingle |
Materials Science Metallurgy Titanium Alloys Neural Networks Phase Trasformations Fracture Toughness Koduri, Santhosh K. Application of Bayesian Neural Network Modeling to Characterize the Interrelationship between Microstructure and Mechanical Property in Alpha+Beta-Titanium Alloys |
| author |
Koduri, Santhosh K. |
| author_facet |
Koduri, Santhosh K. |
| author_sort |
Koduri, Santhosh K. |
| title |
Application of Bayesian Neural Network Modeling to Characterize the Interrelationship between Microstructure and Mechanical Property in Alpha+Beta-Titanium Alloys |
| title_short |
Application of Bayesian Neural Network Modeling to Characterize the Interrelationship between Microstructure and Mechanical Property in Alpha+Beta-Titanium Alloys |
| title_full |
Application of Bayesian Neural Network Modeling to Characterize the Interrelationship between Microstructure and Mechanical Property in Alpha+Beta-Titanium Alloys |
| title_fullStr |
Application of Bayesian Neural Network Modeling to Characterize the Interrelationship between Microstructure and Mechanical Property in Alpha+Beta-Titanium Alloys |
| title_full_unstemmed |
Application of Bayesian Neural Network Modeling to Characterize the Interrelationship between Microstructure and Mechanical Property in Alpha+Beta-Titanium Alloys |
| title_sort |
application of bayesian neural network modeling to characterize the interrelationship between microstructure and mechanical property in alpha+beta-titanium alloys |
| publisher |
The Ohio State University / OhioLINK |
| publishDate |
2010 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=osu1275402649 |
| work_keys_str_mv |
AT kodurisanthoshk applicationofbayesianneuralnetworkmodelingtocharacterizetheinterrelationshipbetweenmicrostructureandmechanicalpropertyinalphabetatitaniumalloys |
| _version_ |
1719429091061399552 |