I want to simulate a welding with Abaqus. so I need a high range of temperature date of material. thank you.
A correlation to estimate the thermal conductivity of alloys of a given composition, based at considering additive contributions from the elements that constitute the alloy was proposed elsewhere at this forum (*), in the form: k3/ρ ≈ f1·w1·k13/ρ1 +... + fi·wi·ki3ρi +... + fn·wn·kn3/ρn, where wi stands for the mass fraction of a generic element, and fi are empirical fitting coefficients. Let us further accept that the following correlation reasonably predicts (estimates) the density of the alloy (ρ): 1/ρ ≈ g1·w1/ρ1 +... + gi·wi/ρi +... + gn·wn·/ρn, where gi are empirical fitting coefficients. The dimensionless fitting coefficients (fi or gi) can be fitted from the available experimental data, or may be taken as equal to each other and/or equal to the unit, in case of absence of enough experimental data. Taking all these coefficients as the unit, we would be dealing with ‘mixture rules’ to estimate either the thermal conductivity or the density (**) of alloys, rather than with (empirical) correlations. To estimate the thermal conductivity of the considered alloy as a function of the temperature, and not just from composition, we may further accept that ki = kiº·(1 + αi·ΔT) and ρi = ρiº·(1 + βi·ΔT). Here ΔT = T-Tº, being Tº a conveniently selected reference temperature for which the thermal conductivity and the density of the elements are known (kiº and ρiº), while αi and βi are the temperature coefficients for the thermal conductivity and density, respectively, having the unit of reciprocal temperature (1/K for the SI system). These coefficients are to be defined as αi = (dki/dT)/ki and βi = (dρi/dT)/ρi.
(*) Cf. https://www.researchgate.net/post/Is_there_a_model_for_the_compositional_dependence_of_thermal_conductivity_in_nickel_superalloys
(**) An analogous mixture rule was discussed at: https://www.researchgate.net/post/How_do_I_find_the_density_of_natural_fibers
Yes, you can find temperature dependent material properties on web as suggested above. But, it is advisable to generate them for your alloys. Kindly note that these properties are influenced by alloy elements and your alloy may not exactly match with one available in literature.
However, you can use material data to start with.
A correlation to estimate the thermal conductivity of alloys of a given composition, based at considering additive contributions from the elements that constitute the alloy was proposed elsewhere at this forum (*), in the form: k3/ρ ≈ f1·w1·k13/ρ1 +... + fi·wi·ki3ρi +... + fn·wn·kn3/ρn, where wi stands for the mass fraction of a generic element, and fi are empirical fitting coefficients. Let us further accept that the following correlation reasonably predicts (estimates) the density of the alloy (ρ): 1/ρ ≈ g1·w1/ρ1 +... + gi·wi/ρi +... + gn·wn·/ρn, where gi are empirical fitting coefficients. The dimensionless fitting coefficients (fi or gi) can be fitted from the available experimental data, or may be taken as equal to each other and/or equal to the unit, in case of absence of enough experimental data. Taking all these coefficients as the unit, we would be dealing with ‘mixture rules’ to estimate either the thermal conductivity or the density (**) of alloys, rather than with (empirical) correlations. To estimate the thermal conductivity of the considered alloy as a function of the temperature, and not just from composition, we may further accept that ki = kiº·(1 + αi·ΔT) and ρi = ρiº·(1 + βi·ΔT). Here ΔT = T-Tº, being Tº a conveniently selected reference temperature for which the thermal conductivity and the density of the elements are known (kiº and ρiº), while αi and βi are the temperature coefficients for the thermal conductivity and density, respectively, having the unit of reciprocal temperature (1/K for the SI system). These coefficients are to be defined as αi = (dki/dT)/ki and βi = (dρi/dT)/ρi.
(*) Cf. https://www.researchgate.net/post/Is_there_a_model_for_the_compositional_dependence_of_thermal_conductivity_in_nickel_superalloys
(**) An analogous mixture rule was discussed at: https://www.researchgate.net/post/How_do_I_find_the_density_of_natural_fibers
About predicting (estimating) the effect of compositional and temperature dependencies on either the specific heat capacity or the molar heat capacity of alloys, you may want to check my answers given elsewhere at this forum: https://www.researchgate.net/post/What_is_the_relation_between_Temperature_and_heat_capacity_of_AA6063_alloy_Any_equation_or_any_alternate_way_to_find_heat_capacity_of_AA6063
Thank you too Mina...It seems it is very good. but this state is based on its website! however thank you for your recommendation.
I recommend JMatPro as well. There you can define the composition of the material under study and calculate thermo-physical properties for this material, e.g. temperature-dependent thermal conductivity, heat capacity, density, Young's and shear moduli, enthalpy.
I tried http://www.matweb.com/ as recommended here by Hui Huang but for the alloy I need the database has data only at room temperature.
Olga Zinovieva
Yes it's an option for a simple simulation for welding or any other thermomechanical simulation. But I needed to study a welding process in both numerical and experimental methods. so I thought I'll need the most accurate data for SS304L. However after comparing the results I found those data accurate enough ( It was a single beed on plate welding). thank you all.
Dear Dr. Tata:
If you'd like to dig a little into the commercial data, below there are a set of interesting links to files containing data at some temperatures.
https://www.integritystainless.com/wp-content/uploads/2016/07/NAS-HR-Grade-304-304L-304H.pdf
https://www.notzgroup.com/media/wysiwyg/PDF/NME/17_Outokumpu_Therma_range_Datasheet_May_2015.pdf
http://www.dm-consultancy.com/TR/dosya/1-59/h/aisi-340-info.pdf
https://www.northamericanstainless.com/wp-content/uploads/2010/10/Grade-310S-314.pdf
https://m.lincolnelectric.com/assets/global/Products/Consumable_StainlessNickelandHighAlloy-BlueMax-BlueMaxMIG316LSi/c64000.pdf
https://www.aksteel.com/sites/default/files/2018-01/304304L201706_1.pdf
https://www.spacematdb.com/spacemat/manudatasheets/304_304L_Data_Sheet.pdf
http://www.farnell.com/datasheets/33229.pdf
http://www.crucible.com/PDFs/DataSheets2010/Data%20Sheet%20XM%2019.pdf
https://www5.kau.se/sites/default/files/Dokument/subpage/2010/02/4_39_48_pdf_20591.pdf
http://www.ccsi-inc.com/images/mold/A2%20Tool%20Steel.pdf
https://www.rolledalloys.com/shared-content/technical-resources/databooks/RA330_DB_US_EN.pdf
https://www.uddeholm.com/files/PB_Uddeholm_arne_english.pdf
https://www.atimetals.com/Products/Documents/datasheets/stainless-specialty-steel/martensitic/ati-403_en_tds_v1.pdf
http://www.worldstainless.org/Files/issf/non-image-files/PDF/Atlas_Grade_datasheet_-_all_datasheets_rev_Aug_2013.pdf
https://www.aksteel.com/sites/default/files/2018-01/301201706_0.pdf
http://www.yoshu.co.jp/seihin/253ma/253ma_eng.pdf
https://www.bssa.org.uk/cms/File/StainlessSteels_at_HighTemperatures_EN.pdf
https://www.doerrenberg.de/uploads/tx_c1x1downloads/1.2379_en_01.pdf
http://www.jfe-steel.co.jp/en/products/sheets/catalog/b1e-002.pdf
With best regards
Marcos Nobre
Farzad Tatar
Did you compare the experimental results with JMatPro, am I right? Did you publish it?
You can also fined some experimentally measured properties for SS304 in this book.
https://books.google.se/books/about/Recommended_Values_of_Thermophysical_Pro.html?id=eV0iTILEaxcC&redir_esc=y
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