Design and development of advanced Al-Ti-V alloys for beampipe applications in particle accelerators

The present investigation reports the design and development of an advanced material with a high figure of merit (FoM) for beampipe applications in particle accelerators by bringing synergy between computational and experimental approaches. Machine-learning algorithms have been used to predict the phase(s), low density, and high radiation length of the designed Al-Ti-V alloys. Al-Ti-V alloys with various compositions for single-phase and dual-phase mixtures, liquidus temperature, and density values were obtained using the latin hypercube sampling method in TC Python Thermo-Calc software. The obtained dataset was utilized to train the machine-learning algorithms. Classification algorithms such as XGBoost and regression models such as linear regression and random forest regressor have been used to compute the number of phases, radiation length, and density, respectively. The XGBoost algorithm shows an accuracy of 98%, the linear regression model shows an accuracy of 94%, and the random forest regressor model is accurate up to 99%. The developed Al-Ti-V alloys exhibit high radiation length as well as a good combination of high elastic modulus and toughness due to the synergistic effect of the presence of hard <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"><a:msub><a:mi>Al</a:mi><a:mn>3</a:mn></a:msub><a:mi>Ti</a:mi></a:math> phase along with a minor volume fraction of FCC <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"><c:mo stretchy="false">(</c:mo><c:mi>Al</c:mi><c:msub><c:mo stretchy="false">)</c:mo><c:mi>ss</c:mi></c:msub></c:math> solid solution phase mixture. The comparison of our alloys, alloy-1 (<g:math xmlns:g="http://www.w3.org/1998/Math/MathML" display="inline"><g:mrow><g:msub><g:mrow><g:mi>Al</g:mi></g:mrow><g:mrow><g:mn>75.2</g:mn></g:mrow></g:msub><g:msub><g:mrow><g:mi>Ti</g:mi></g:mrow><g:mrow><g:mn>22.8</g:mn></g:mrow></g:msub><g:msub><g:mrow><g:mi mathvariant="normal">V</g:mi></g:mrow><g:mrow><g:mn>2</g:mn></g:mrow></g:msub></g:mrow></g:math>) and alloy-2 (<j:math xmlns:j="http://www.w3.org/1998/Math/MathML" display="inline"><j:mrow><j:msub><j:mrow><j:mi>Al</j:mi></j:mrow><j:mrow><j:mn>89</j:mn></j:mrow></j:msub><j:msub><j:mrow><j:mi>Ti</j:mi></j:mrow><j:mrow><j:mn>10</j:mn></j:mrow></j:msub><j:msub><j:mrow><j:mi mathvariant="normal">V</j:mi></j:mrow><j:mrow><j:mn>1</j:mn></j:mrow></j:msub></j:mrow></j:math>) shows an increase in the radiation length by 7 times and a decrease in the density by 2 to 3 times as compared to stainless steel 304, the preferred material for constructing beampipes in low-energy particle accelerators. Further, we experimentally verify the elastic modulus of the alloy-1 and compute the FoM equal to 0.416, which is better than other existing materials for beampipes in low-energy experiments.

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Journal
Physical Review Accelerators and Beams
Published
2025-04-08
DOI
https://doi.org/10.1103/physrevaccelbeams.28.043101
Citations
1
Primary Topic
Fusion materials and technologies
Type
article
Field-Weighted Citation Impact
0.52

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article

Design and development of advanced Al-Ti-V alloys for beampipe applications in particle accelerators

R. Sahoo, Sumanta Samal, K. Singh, Kangkan Goswami
1 citations
Physical Review Accelerators and Beams
Fusion materials and technologies
0.52
article

Design and development of advanced Al-Ti-V alloys for beampipe applications in particle accelerators

R. Sahoo, Sumanta Samal, K. Singh, Kangkan Goswami
article en
1 citations

Abstract

The present investigation reports the design and development of an advanced material with a high figure of merit (FoM) for beampipe applications in particle accelerators by bringing synergy between computational and experimental approaches. Machine-learning algorithms have been used to predict the phase(s), low density, and high radiation length of the designed Al-Ti-V alloys. Al-Ti-V alloys with various compositions for single-phase and dual-phase mixtures, liquidus temperature, and density values were obtained using the latin hypercube sampling method in TC Python Thermo-Calc software. The obtained dataset was utilized to train the machine-learning algorithms. Classification algorithms such as XGBoost and regression models such as linear regression and random forest regressor have been used to compute the number of phases, radiation length, and density, respectively. The XGBoost algorithm shows an accuracy of 98%, the linear regression model shows an accuracy of 94%, and the random forest regressor model is accurate up to 99%. The developed Al-Ti-V alloys exhibit high radiation length as well as a good combination of high elastic modulus and toughness due to the synergistic effect of the presence of hard <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"><a:msub><a:mi>Al</a:mi><a:mn>3</a:mn></a:msub><a:mi>Ti</a:mi></a:math> phase along with a minor volume fraction of FCC <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"><c:mo stretchy="false">(</c:mo><c:mi>Al</c:mi><c:msub><c:mo stretchy="false">)</c:mo><c:mi>ss</c:mi></c:msub></c:math> solid solution phase mixture. The comparison of our alloys, alloy-1 (<g:math xmlns:g="http://www.w3.org/1998/Math/MathML" display="inline"><g:mrow><g:msub><g:mrow><g:mi>Al</g:mi></g:mrow><g:mrow><g:mn>75.2</g:mn></g:mrow></g:msub><g:msub><g:mrow><g:mi>Ti</g:mi></g:mrow><g:mrow><g:mn>22.8</g:mn></g:mrow></g:msub><g:msub><g:mrow><g:mi mathvariant="normal">V</g:mi></g:mrow><g:mrow><g:mn>2</g:mn></g:mrow></g:msub></g:mrow></g:math>) and alloy-2 (<j:math xmlns:j="http://www.w3.org/1998/Math/MathML" display="inline"><j:mrow><j:msub><j:mrow><j:mi>Al</j:mi></j:mrow><j:mrow><j:mn>89</j:mn></j:mrow></j:msub><j:msub><j:mrow><j:mi>Ti</j:mi></j:mrow><j:mrow><j:mn>10</j:mn></j:mrow></j:msub><j:msub><j:mrow><j:mi mathvariant="normal">V</j:mi></j:mrow><j:mrow><j:mn>1</j:mn></j:mrow></j:msub></j:mrow></j:math>) shows an increase in the radiation length by 7 times and a decrease in the density by 2 to 3 times as compared to stainless steel 304, the preferred material for constructing beampipes in low-energy particle accelerators. Further, we experimentally verify the elastic modulus of the alloy-1 and compute the FoM equal to 0.416, which is better than other existing materials for beampipes in low-energy experiments.

Physical Review Accelerators and BeamsVol. 28(4)
Indian Institute of Technology Indore (IN)
CERN, Department of Science and Technology, Ministry of Science and Technology, India, University Grants Commission, Department of Atomic Energy, Government of India, Indian Institute of Technology Roorkee, Indian Institute of Technology Madras, Indian Institute of Technology Indore
Openalex Percentile: Top 40%
Fusion materials and technologies
0.52
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