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Development of a high temperature flow stress model for AerMet 100 covering several orders of magnitude of strain rate

M. Shakib, Karen Perkins Orcid Logo, S.E. Bray, C.R. Siviour

Materials Science and Engineering: A, Volume: 657, Pages: 26 - 32

Swansea University Author: Karen Perkins Orcid Logo

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DOI (Published version): 10.1016/j.msea.2016.01.046

Abstract

Constant strain rate, constant velocity and Hopkinson Pressure Bar compression tests were carried out on AerMet 100 martensitic steel between 1130 °C and 1250 °C spanning strain rates from 0.01 s−1 to 4000 s−1. The results were used to generate a predictive flow stress model over the entire range of...

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Published in: Materials Science and Engineering: A
ISSN: 0921-5093
Published: 2016
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URI: https://cronfa.swan.ac.uk/Record/cronfa28322
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first_indexed 2016-05-26T12:30:14Z
last_indexed 2020-12-19T03:44:13Z
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spelling 2020-12-18T15:13:49.9035125 v2 28322 2016-05-26 Development of a high temperature flow stress model for AerMet 100 covering several orders of magnitude of strain rate f866eaa2d8f163d2b4e99259966427c8 0000-0001-5826-9705 Karen Perkins Karen Perkins true false 2016-05-26 EEN Constant strain rate, constant velocity and Hopkinson Pressure Bar compression tests were carried out on AerMet 100 martensitic steel between 1130 °C and 1250 °C spanning strain rates from 0.01 s−1 to 4000 s−1. The results were used to generate a predictive flow stress model over the entire range of test conditions. The effect of initial austenite grain size on flow stress was found to follow the Hall–Petch relationship. This dependency was then removed through innovative heat treatments. The morphology of the flow stress curves were also dependent on mechanisms of microstructural evolution which were controlled by the test method, strain rate and temperature. Friction and adiabatic heating also had a major contribution. A novel method was proposed in order to define the flow stress, which was then used to determine the work hardening exponent of the Zener–Hollomon equation. It was found that a deviation from the linear trend was observed in Hopkinson Pressure Bar tests and reasons were given. An artificial neural network approach was used to determine a more accurate predictive flow stress model which included the effects of test method, temperature, stain rate and initial austenite grain size. The method showed that it was possible to predict the flow stress between 50 and 2000 s−1 where mechanical testing’s results were absent. Journal Article Materials Science and Engineering: A 657 26 32 0921-5093 Flow stress; AerMet 100; Hopkinson Pressure Bar; Servohydraulic; Artificial neural networks; Prior austenite grain size 7 3 2016 2016-03-07 10.1016/j.msea.2016.01.046 COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University 2020-12-18T15:13:49.9035125 2016-05-26T09:10:57.3130538 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised M. Shakib 1 Karen Perkins 0000-0001-5826-9705 2 S.E. Bray 3 C.R. Siviour 4
title Development of a high temperature flow stress model for AerMet 100 covering several orders of magnitude of strain rate
spellingShingle Development of a high temperature flow stress model for AerMet 100 covering several orders of magnitude of strain rate
Karen Perkins
title_short Development of a high temperature flow stress model for AerMet 100 covering several orders of magnitude of strain rate
title_full Development of a high temperature flow stress model for AerMet 100 covering several orders of magnitude of strain rate
title_fullStr Development of a high temperature flow stress model for AerMet 100 covering several orders of magnitude of strain rate
title_full_unstemmed Development of a high temperature flow stress model for AerMet 100 covering several orders of magnitude of strain rate
title_sort Development of a high temperature flow stress model for AerMet 100 covering several orders of magnitude of strain rate
author_id_str_mv f866eaa2d8f163d2b4e99259966427c8
author_id_fullname_str_mv f866eaa2d8f163d2b4e99259966427c8_***_Karen Perkins
author Karen Perkins
author2 M. Shakib
Karen Perkins
S.E. Bray
C.R. Siviour
format Journal article
container_title Materials Science and Engineering: A
container_volume 657
container_start_page 26
publishDate 2016
institution Swansea University
issn 0921-5093
doi_str_mv 10.1016/j.msea.2016.01.046
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 0
active_str 0
description Constant strain rate, constant velocity and Hopkinson Pressure Bar compression tests were carried out on AerMet 100 martensitic steel between 1130 °C and 1250 °C spanning strain rates from 0.01 s−1 to 4000 s−1. The results were used to generate a predictive flow stress model over the entire range of test conditions. The effect of initial austenite grain size on flow stress was found to follow the Hall–Petch relationship. This dependency was then removed through innovative heat treatments. The morphology of the flow stress curves were also dependent on mechanisms of microstructural evolution which were controlled by the test method, strain rate and temperature. Friction and adiabatic heating also had a major contribution. A novel method was proposed in order to define the flow stress, which was then used to determine the work hardening exponent of the Zener–Hollomon equation. It was found that a deviation from the linear trend was observed in Hopkinson Pressure Bar tests and reasons were given. An artificial neural network approach was used to determine a more accurate predictive flow stress model which included the effects of test method, temperature, stain rate and initial austenite grain size. The method showed that it was possible to predict the flow stress between 50 and 2000 s−1 where mechanical testing’s results were absent.
published_date 2016-03-07T03:34:27Z
_version_ 1763751459940204544
score 11.012678

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