metadata
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:892
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l
widget:
- source_sentence: >-
content='1. What is the relationship between casting cooling rate and the
formation of the Mg17A12 phase in the context of dynamic recrystallization
(DRX)?\n2. How does the ANN model contribute to predicting hardness
variations in AZ80 cast-forged I-beams under different casting and forging
conditions?' additional_kwargs={'refusal': None}
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- >-
denoises the data [82, 83, 84, 85]. During the forward process, the
noise is added incrementally through numerous steps in a Markovian
process:
𝑞(𝑥𝑡|𝑥𝑡−1) ≔ 𝒩(𝑥𝑡; √1 − 𝛽𝑡𝑥𝑡−1,
𝛽𝑡𝑰)
(3)
Where 𝑥0 is a data sampled from the real data distribution 𝑞(𝑥)
(i.e., 𝑥0 ∼ 𝑞(𝑥)). 𝛽t ∈ (0,1) is the variance schedule (𝛽0 is
small, and 𝛽T is large), and 𝑰 is the identity matrix. By using the
notation 𝛼t ∶= 1 − 𝛽t and 𝛼t ∶= ∏st=0𝛼s, the noised data at any
arbitrary step can be found:
𝑞
(𝑥t|𝑥t−1) ≔ 𝒩(𝑥t; √̅𝛼t𝑥0, (1 −
𝛼t)𝑰)
̅ (4)
With reparameterization, 𝑥 can be written as:
𝑥t = √̅𝛼t𝑥0 + √1 −
𝛼t𝝐
̅
(5)
Where 𝜖 ~ 𝒩(𝜖|0, 𝑰).
During the backward process, the goal is to denoise images iteratively
to get back to a less noisy version:
𝑝
(𝑥t−1|𝑥t) ≔ 𝒩(𝑥t−1; 𝜇t(𝑥t, 𝑡),
𝜎t𝑰)
̃ (6)
The reverse conditional shown in equation 6 does not have a closed form,
but the following approximation is derived for that [82]:
̃𝜇t(𝑥t, 𝑡) ≈
1
𝛼t(𝑥t −
√1−𝛼t
1−𝛼t𝜖t) (7)
Where 𝜖t is the noise introduced in the step 𝑡. The main idea is to
learn this distribution, so we can denoise images by the reverse
conditional. In order to do this, we train a neural network 𝜖𝜃(𝑥t,
𝑡) to approximate it:
𝑀𝑖𝑛𝑖𝑚𝑖𝑧𝑒 𝐿(𝜃) = ‖𝜖t− 𝜖𝜃(𝑥t,
𝑡)‖2
2
(8)
In this work, a U-net architecture, which is first introduced by
Ronneberger et al. [86] for image segmentation. Due to its symmetric
down-sampling and up-sampling, this architecture is suitable for going
from a noisy image to its equivalent denoised version. The encoding part
consists of five blocks of max-pooling followed by several convolutional
layers, group normalization, and GELU activation function. The
self-attention mechanism with four heads is applied after each
down-sampling layer. The decoding part is a somewhat mirrored structure
of the encoding. It consists of five blocks, each one of them has
bi-linear up-sampling followed by several convolutional layers, group
normalization, and GELU activation function. The only difference is that
after each up-sampling layer the data is concatenated with skip
connections corresponding to channels with the same resolution from the
encoding part. The model is conditioned on the time step by the
Transformer positional embedding.
38
- >-
# Figure 39
SEM images of the different morphologies of the intermetallic compounds
present in the cast-forged I-beams produced in the range of casting and
forging conditions investigated in this study. a) Eutectic Mg17Al12, b)
Lamellar Mg17Al12, c) Globular and Continuous Mg17Al12, and d)
Intergranular Mg17Al12 and AlMn5. The casting cooling condition, forging
temperature, and the location of each sample is depicted at the top
right corner of each image.
The microstructures of the I-beams exhibit significant variation due to
different casting and forging conditions, as well as the evolution of
Mg17Al12 phase. Figure 40 and Figure 41 show the microstructures of the
I-beams obtained from the web region and short flange, respectively,
under different casting cooling rates and forging temperatures. Notably,
the characteristics of DRX, including the extent of DRX, its
distribution, and DRX grain size vary among the investigated samples.
For the samples forged at 350°C, both the web region and short flange
display a completely coverage of DRXed grains, indicating extensive
recrystallization (see Figure 40a-b and Figure 41a-b). Conversely, a
bimodal microstructure is observed in the remaining samples. At the web
region of the samples forged at 250 and 300°C, the deformation and
subsequent DRX has resulted in a distinctive “necklace” structure,
characterized by initial elongated grains surrounded by DRXed grains
(see Figure 40c-f). At the short flange of the samples forged at 250 and
300°C, DRX is limited to the vicinity of the eutectic Mg17Al12 and the
globular particles formed around them (see Figure 41c-f).
- >-
# Cooling Rate
# Forging
|Temperature|Measured|Predicted|Measured|Predicted|Measured|Predicted| |
|
|---|---|---|---|---|---|---|---|---|
|250|47.58 + 2.70|46.31 + 1.69|47.50 + 2.59|47.56 + 2.34|43.73 +
3.29|44.01 + 1.90|43.32 + 2.73|42.77 + 2.23|
|NRMSE:|0.0501|0.0416| |0.0503| |0.0555| | |
|300|40.63 + 2.25|39.93 + 1.42|40.48 + 2.19|41.82 + 1.90|38.80 +
2.63|39.27 + 1.57|37.89 + 2.82|38.24 + 1.76|
|NRMSE:|0.0468|0.0571| |0.0597| |0.0688| | |
|350|35.46 + 3.12|33.89 + 1.49|34.75 + 2.69|35.49 + 1.78|34.16 +
2.16|32.94 + 1.05|32.57 + 2.24|33.42 + 1.20|
|NRMSE:|0.0933|0.0891| |0.0734| |0.0826| | |
Figure 55: The predicted and actual contour maps and average HR30T
hardness values with standard deviation for different combinations of
the casting cooling rate and the forging temperature in the
one-condition-out-scenario. NRMSE values for each prediction are also
provided.
The effect of the casting cooling rate is also related to the occurrence
of DRX. Higher cooling rates result in finer α-Mg dendritic structure
and prevent the formation of the lamellar Mg17A12 phase [127]. This can
result in a more uniform distribution of the Mg17A12 intermetallic
particles, which promote dynamic recrystallization by particle
stimulated nucleation (PSN) mechanism [36]. Consequently, the samples
forged from the casts with higher cooling rates can have more DRXed
regions, resulting in slightly higher hardness.
The ANN model accurately captures these hardness variations which are
controlled by the evolution of several different microstructural
features, even in prediction of completely unseen conditions. This model
can be used in the range of studied casting and forging conditions to
produce reliable hardness distribution contour maps and predict the
average hardness of the AZ80 cast-forged I-beam.
This model can be extended by including the effective local strain at
each measurement point instead of the x and y coordinates of the
measurement point, so that the model can be generalized for any
arbitrary forged geometry. In addition, the casting cooling rate
parameter can be replaced by a microstructure feature, such as secondary
dendrite arm spacing (SDAS), to generalize the model for varied casting
methods. On the other hand, the benefit of the current model is that it
needs no further measurement or observation and produces hardness
distribution contour maps from known processing parameters.
- source_sentence: >-
content='1. What does a lower relative CRSS ratio indicate in terms of
activation with reference to the basal slip?\n2. How does the initial
texture affect material flow during deformation in AZ31 alloy?'
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sentences:
- >-
# 2.3.2 CRSS in the present case
(b) relative CRSS ratio (lower values indicate an ease in activation
with reference to the basal slip) [63]
# Figures
# Figure 2-12
Effect of starting grain size on DRX fraction and DRX grain size in an
extruded AZ31 alloy deformed in uniaxial compression at 300 °C, 0.01 s-1
to a strain of 0.5. Adapted from [66]
# Figure 2-13
Effect of initial texture on material flow (a) symmetric distribution of
the starting texture results in a symmetric material flow during
deformation, (b) asymmetric distribution of starting texture with
respect to compression direction (CD) results in a preferred material
flow along ND. Deformation was done using symmetrical cylindrical
samples (with cylindrical axis along the CD). CD: Compression direction,
ND: Normal direction, TD: Transverse direction, ED: Extrusion direction.
Adapted from [42]
# Figure 2-14
Effect of starting texture on (a) DRX fraction, (b) DRX grain size. AZ31
alloy deformed in plane strain compression, in temperature range of 300
°C– 400 °C, and strain rate range of 0.001 s-1 – 1 s-1. Adapted from
[77]
# Figure 2-15
Effect of Al alloying in polycrystalline Mg on the CRSS for various
deformation modes at: (a) Room temperature, (b) 200 °C. Twinning refer
to {1012} tensile twinning. X-axis shows concentration of Al in square
root of wt.% [82]
# Figure 2-16
Equilibrium phase fraction map of AZ80 alloy [93]
# Figure 2-17
Different Mg17Al12 precipitate morphologies in AZ alloys: (a) DP in
lamellar form, (b) DP in ellipsoidal particle (globular) form, (c) CP in
form of laths, and (d) CP in form of irregular plates. (a) and (b) are
for AZ80 [92], while (c) and (d) are for AZ91 [91].
# Figure 2-18
(a-c) hot deformation of a cast-homogenized and pre-aged AZ91 sample at
300 °C, 0.2 s-1, showing the occurrence of DRX only in regions
previously occupied by DP [24]; (d-f) hot deformation of a
cast-homogenized AZ91 sample at 400 °C, 0.2 s-1. At 400 °C, CP got
precipitated in the homogenized material during heating to and holding
at the test temperature. (e-f) shows extensive DRX occurred in the
material in this case as well [23]. (a) and (d) correspond to
non-deformed samples, while (b, c, e, and f) correspond to deformed
samples. C and D in (b) and (c) refer to continuous precipitates and
discontinuous precipitates, respectively.
# Figure 2-19
Variation in (a) DRX ratio and (b) DRX grain size, with the Z parameter.
Cast and extruded AZ31 alloys deformed in uniaxial compression for
temperatures in the range of 300 - 450 °C, and strain rates in the range
of 0.01 – 1 s-1. For the data presented in the graphs, DRXed grains were
distinguished from the parent grains based on their size and shape [67].
# Figure 2-20
Change of DRX mechanism with deformation conditions for deformation of
pure Mg. In area marked as I, DRX takes place via the twinning mechanism
[35]
- >-
# 9.3.2. Microstructure to mechanical properties prediction model
.......................................... 132
# 9.3.3. Evaluation
.........................................................................................................................
133
# 9.4. Results and discussion
.............................................................................................................
134
# 9.4.1. Microstructure and quasi-static tensile test of the cast-forged
AZ80 magnesium alloy
…134
# 9.4.2. Comparison of data balancing strategies
........................................................................
135
# 9.4.3. Mechanical properties prediction
....................................................................................
136
# 9.5. Conclusion
...............................................................................................................................
139
# 10. Summary, Conclusions, limitations, and Future Work
.......................................................... 141
# 10.1. Summary
................................................................................................................................
141
# 10.2. Conclusions
............................................................................................................................
141
# 10.3. Limitations
.............................................................................................................................
145
# References
...........................................................................................................................................
148
- >-
# Characterization of forged magnesium alloys
# 3.4 Case study
In industrially forged parts, control of forging parameters, such as
temperature and cooling rate is often challenging compared to
laboratory-scale studies involving uniaxial compression. As a result,
these variations can significantly impact the microstructure and texture
development. Furthermore, industrial forgings typically exhibit larger
dimensions and involve more complex material flow in closed dies further
influencing the microstructure and texture development during forging.
To exemplify these points, we refer to the work of Jahed et al. who
studied the forging of an I-beam at the University of Waterloo by using
a cast AZ31B alloy billet. The investigation served as a precursor study
before the forging of a more geometrically complex automotive suspension
component (Gryguc et al., 2021a, 2021b, 2021c; Toscano et al., 2018a,
2018b; Williams et al., 2021). The geometry was forged using a starting
cylindrical billet with a diameter of 63.5 mm and length of 65 mm
machined from an original cast AZ31B billet of 300 mm diameter (Toscano
et al., 2018a, 2018b).
Forging was carried out using a 500-ton hydraulic press at 250 °C at a
ram displacement rate of 20 mm/s, equivalent to an initial strain rate
of about 0.3 s−1. The dies were pre-heated to the same forging
temperature. Graphite was used as a lubricant. Forging was carried out
in a single step. After forging, the forged part was allowed to cool by
itself in the air. Subsequently, the part was longitudinally sectioned
along mid-plane (refer to Fig. 36c) and prepared for metallographic
examination using the standard practice. Microstructure and texture were
observed at three different locations, as marked in Fig. 36c.
The texture was measured using a Brucker D8 Discover diffractometer
using the process outlined in Pahlevanpour et al. (2018) and presented
here based on the (0002) basal pole figures. Surface hardness
measurements on the entire prepared cross-section were carried out using
a United True Blue II Rockwell Hardness Tester using Rockwell 15T scale
and a 1/16 ball indenter.
The results presented in Fig. 36 show significantly different
microstructures and textures at the three locations marked in Fig. 36c.
This difference is attributed to local variations in strain and strain
rate gradients, which are significantly impacted by the die geometry as
it constrains the material flow. Additionally, the DRX grain size from
the microstructures presented here is coarser than what is expected from
a specimen-size hot compression test, which can be attributed to a slow
cooling rate post-forging (Beer and Barnett, 2008).
The differences in microstructure and texture are also reflected in the
variation in the hardness map presented in Fig. 36c. Gryguc et al., have
shown that such spatial variations in microstructure and texture in a
forged part also result in variations in strength and ductility (Gryguc
et al., 2018). Other authors have also reported similar spatial
variations in microstructure, texture, and mechanical properties in
industrially relevant magnesium forged parts. Specifically, such
variations were reported in aircraft brackets forged using AZ31 alloy
and automotive wheel hubs forged using AZ80 alloy (Dziubińska et al.,
2015; Liao et al., 2023).
Due to these variations in mechanical properties, it is crucial to
assess them individually in all critical load areas when forging a new
part, rather than relying on single measurements assuming it represents
the overall behavior of the bulk material.
- source_sentence: >-
content='1. What was the purpose of using graphite lubricate on the die
and billet during the forging process? \n2. How were the tensile
monotonic and fatigue test samples extracted from the closed die forged
components? ' additional_kwargs={'refusal': None}
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sentences:
- >-
microstructure at the end of the forging stage. Because of the I-beam
geometry, the effective strain distribution is nonuniform across the
forged sample. The middle part of the I-beam component, referred to as
the web region, undergoes a higher extent of deformation [63, 39]. The
grains are therefore severely elongated in the web region. The higher
effective strain results in an increase in the hardness due to work
hardening. Moreover, higher deformation promotes DRX, which refines
grain size and further increases the material hardness.
# Table 5: Chemical composition of the cast AZ80.
|Element|Mg|Al|Zn|Cu|Fe|Mn|Ni|
|---|---|---|---|---|---|---|---|
|Wt.%|Bal.|8.64|0.52|0.0017|0.0044|0.24|0.0012|
Figure 49: SEM microstructure images of the AZ80 magnesium alloy cast at
a cooling rate of: a) 1.5 °C/s and b) 6 °C/s.
Typical representative alterations in the forged microstructure due to
casting and forging process parameters are shown in Figure 50. Depending
on the forging temperature, some dissolution of the eutectic and
precipitated Mg17Al12 phase might occur during forging. Figure 50a and
Figure 50b compare the effect of forging temperature on the extent of
dissolution of β-Mg17Al12 particles during forging for samples cast at
1.5 °C/s cooling rate and forged at 300 °C and 350 °C, respectively.
Moreover, the Mg17Al12 intermetallic can precipitate in discontinuous
and/or continuous morphologies [153]. Figure 50c and Figure 50d show two
different morphologies of precipitated β-Mg17Al12 formed in samples cast
at 6 °C/s cooling rate and forged at 250 °C and 300 °C, respectively. In
addition to the direct effect of intermetallic compounds on the material
hardness [146], the β-Mg17Al12 morphologies are shown to control
microstructure evolution and hardness via affecting DRX [44].
DRX occurs for all the studied cast-forge conditions; however, the
extent and the size of DRXed grains vary for different samples. Both
eutectic and precipitated Mg17Al12, as well as the higher amount of
deformation promote DRX [44]. The size of DRXed grains is also
controlled by deformation temperature [36] and the presence of fine
intermetallic particles. Figure 50e and Figure 50f illustrate the effect
of deformation temperature on the grain size of DRXed grains. Forging
the sample cast at 1.5
- >-
Metals 2021, 11, 1290 lower control arm (step 4). Graphite lubricate was
used on the die and billet to reduce friction, sticking and promote
material flow. The orientation of the billet to the press was such that
the radial direction was along the direction of the press stroke (i.e.,
the direction of forging was parallel to the radial direction of the
billet). Forging was carried out in a single step at a displacement rate
of 2.1–4.2 mm/s, and they were allowed to air cool with no additional
post-heat treatments.
# Figure 1. Plan view of extruded billet during different stages of
forging operation to achieve final component (prior to machining of the
flash).
Tensile monotonic and fatigue test samples were utilized with geometries
according to Roostaei et al. [25] and a 4 mm thickness, these were
machined from various locations throughout closed die forged components.
There were 18 test samples that were extracted from 18 different
locations throughout each of the closed die forging (see Figure 2), all
with varying thermomechanical histories. One component forged at each
temperature was dedicated to stress-controlled fatigue testing where 10
samples are presented for the 300 °C forging and 8 for the 450 °C
forging. Strain-controlled tests were performed on 14 different samples
extracted from another component forged at 300 °C. The remainder of the
original 18 test samples from each component were used for monotonic
testing to understand the spatial variation in strength and ductility.
The test samples had a nominal surface finish of Ra ≤ 0.2 μm; however,
in practice, the actual roughness was substantially less, around 0.05
μm. These samples were later utilized for quasi-static and cyclic
(stress-controlled) testing.
The quasi-static tensile tests were performed according to ASTM standard
E8/E8M-15a using an MTS 810 Servo-Hydraulic test machine operating in
displacement control mode with a displacement rate of 1 mm/min. Strain
measurement was accomplished using a GOM ARAMIS 3D 5MP DIC system which
passively functioned to measure the average axial strain on the surface
of the gauge section of the sample throughout the duration of the test.
The fatigue tests were performed as per ASTM E466-15 in an ambient
environment using an MTS 810 Servo-Hydraulic test machine operating in
stress control mode at a frequency range of 0.5 Hz to 60 Hz depending on
the stress amplitude to maintain an approximately consistent loading
rate between all tests.
The strain was measured throughout the first 10,000 cycles using an MTS
632.26 extensometer with an 8-mm gauge and travel of ±1.2 mm until
stabilization of the cyclic hysteresis loop was achieved. The tests were
conducted at zero mean stress (i.e., RL = −1, fully reversed stress
cycle) and stress amplitudes of between 140 MPa and 180 MPa. The failure
criteria for the tests were considered to be the final rupture of the
specimen gauge section.
# Figure 2. Sample extraction schematic from AZ80 Mg die-forged
component.
# 3. Results and Discussion
# 3.1. Structure-Property Relationship (Effect of Material Modification)
Some of the favourable aspects of selecting forging as a “near
net-shape” manufacturing process include...
- >-
The effect of initial cooling rate is apparent. Material cast with a
higher cooling rate shows a higher peak compressive stress than material
cast with a lower cooling rate, though this effect is most noticeable at
low temperatures and/or high strain rates. Following the peak stress,
the HCR samples experienced a higher rate of dynamic softening, lowering
the stress to a similar value as the LCR samples. At high strains, after
significant deformation has occurred, there is no effect of initial cast
cooling rate. The peak compressive stress of all deformation conditions
that were tested is shown in Figure 30. While some conditions show a
higher difference in peak stress than others, the effect is consistent
across all temperatures and strain rates. The higher flow stress that is
seen in finer grained cast AZ80 has also been reported for similar
alloys [100, 101].
|a)| |250 C-HCR|b)|I/s-HCR| |
|---|---|---|---|---|---|
|200| |250 C-LCR|200|I/s-LCR| |
|180|300°C-HCR|180| |0.1/s-HCR| |
| |300°C-LCR| | |0.1/s-LCR| |
|160| |350 C-HCR|60|0.01/s-HCR| |
| | |350 C-LCR| |0.01/s-LCR| |
|140| | |9|140| |
|8|120| |3|120| |
|2|80|2|80| | |
|60| |60| | | |
|40| |40| | | |
|20| |20| | | |
|0.0|0.2|0.4|0.6|0.8|1.0|
Figure 29: Measured compressive flow curves for degassed AZ80 cast with
cooling rates of 3.4°C/s (LCR, dashed line) and 24.7°C/s (HCR, solid
line) showing: a) the effect of temperature at a strain rate of 0.1s-1
and b) the effect of strain rate at a temperature of 300°C. Data from
[78].
|250C-HCR|220|250C-LCR|
|---|---|---|
|300 C-HCR|200|300C-LCR|
|350C-HCR|180|350C-LCR|
|3|160| |
|2|120| |
|2|100| |
|80| | |
|0.01| | |
Figure 30: Peak compressive stress at each strain rate for LCR and HCR
degassed AZ80 at deformation temperatures of 250°C, 300°C, and 350°C.
Data from [78].
52
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content='1. What was the conclusion of Grygu´c et al. regarding the
texture modification due to forging in AZ80 Mg materials?\n2. How does the
forging temperature affect the yield strength of AZ80 Mg according to the
studies presented?' additional_kwargs={'refusal': None}
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- >-
Metals 2021, 11, 1290
(T5) treatment, with promising results [30]. In general, a significant
component of the variability observed in the properties of the as-forged
components stems from the air-cooling following forging, and the
resulting variability in local cooling rates throughout the components.
They also mention that the second phase sizes and morphologies which
precipitate are highly dependent on the temperature and aging time, and
in this study since air cooling was employed, both of these parameters
would naturally spatially vary throughout the forged component and would
be different in location #3 (Figure 8a) and location #13 (Figure 8b).
Supporting this, is the observation that there is an apparent difference
in the uniformity of the distribution in these continuous precipitates
between the two locations. In location #3 (Figure 8a) the precipitate
distribution is somewhat striated in the middle of the micrograph, with
a non-uniform volume fraction of the second phase, as compared with
location #13 (Figure 8b) which is much more uniform in nature across the
entire image. Furthermore, from the higher magnification images, it can
be observed that, in general, location #3 (Figure 8c) has comparatively
larger precipitate sizes relative to location #13 (Figure 8d). A more
refined precipitate structure with smaller spacing between particles
leads to a higher propensity to resist dislocation movement when
plastically deformed which is beneficial to the mechanical properties
[34]. Both effects of this increase in uniformity in precipitation
distribution and decrease in size in location #13 (Figure 8b) can
contribute to an increase in strength, and fatigue life (as observed in
the two samples tested at 160 MPa where there is a difference in life of
about 6×) [1,34]. The source of this variation in life has many
contributing effects, the most significant of which are: firstly, the
effect of the thermal process history on the microstructure and
precipitate morphology discussed here; secondly, the equivalent strain
imposed by the mechanical deformation during forging which will be
subsequently discussed; and finally, the well documented stochastic
nature of fatigue.
|T = 450 °C|y = 568.63x-0.13|
|---|---|
|T = 300 °C|y = 429.9x-0.088|
[MPa]
Amplitude cycles
Stress
Number of Cycles to Failure [N]
|5,000|50,000|500,000|
|---|---|---|
|160|170|#13: 282,092|
|140|130|#3: 46,428|
Figure 7. Relationship between the forging temperature and
stress-controlled fatigue behaviour for closed-die forged AZ80 Mg
automotive suspension component. Location #3 & #13 are highlighted for
reference (per Figure 2) as they are subsequently referred to in the SEM
micrographs in Figure 8.
Figure 8 illustrates SEM micrographs highlighting the precipitate
morphology of two different locations within the forging that were
tested at the same stress amplitude yet exhibited a significantly
different number of cycles to failure. The precipitates are continuous
in nature and range in both size and morphology, from smaller
lozenge/blunted asymmetric hexagonal structure to larger slab-like
structures, and they can be observed in the brighter contrast regions in
the micrographs with the darker background being the.
- >-
# Chapter 4: Methodology
Due to a relatively coarse grain size of the as-received cast material
(compared to the starting extruded material), a larger area needed to be
scanned for the cast-deformed samples, to be able to capture enough
grains to produce statistically relevant results. Details of the area
scanned for cast and extruded-deformed samples at various strain levels
are provided in Figure 4-16. Note that for the sample deformed to the
strain of 1.0, the scan area was limited to 0.75 x 0.6 mm even for the
cast material, in order to avoid an overlap of the scan area with the
dead metal zone (refer to Figure 4-16 (c) and (d)). As the results in
later text show, hot deformation resulted in a considerable grain
refinement in both cast and extruded materials, and this relatively
small scan area was still found to adequately capture statistically
relevant amount of data.
|(a)|8 = 0.15|(b)|8 = 0.4|(c)|8 = 1.0|
|---|---|---|---|---|---|
|8|3 0.75 mm|!|8 0.75 mm|0.75 mm| |
| |LQ|LO| | | |
| | |(d)|17.5 mm|E| |
|0 0.75 mm 2mm| | | | | |
|Cast Az80 = 4008C, 0.1s-1 8 = 1.0| | | | | |
|EBSD mapping area for cast mt| | | | | |
|EBSD mapping area for extruded mt; Dead metal zone| | | | | |
Figure 4-16 Location and size of EBSD scan for cast and extruded
compressed samples. Sample size and the scan area are proportionally
scaled. The dead metal zone location and extent is a simple schematic
(and not to scale) and just for illustrative purposes. The true extent
of the dead metal zone for one of the samples can be visualized from
(d).
The scanned data was processed and analysed using TSL OIM 8.0 software.
The data was cleaned using standard data cleaning techniques [122]. This
is illustrated through Figure 4-17 and Figure 4-18 for an extruded AZ80
sample deformed at 400 °C, 0.1 s-1 to a strain of 1.0. Initially, based
on Image Quality (IQ) map and distribution, it was observed that areas
with precipitates showed much lower IQ values. Therefore, the data
pertaining to the precipitates could be filtered away from the data
pertaining to the actual grains, using a lower threshold on the IQ
values. For the case presented in Figure 4-17, an IQ value of 550 was
found to be appropriate, though it should be noted.
- >-
dominated by compressive loading) on the resulting texture of the
as-forged material was previously explored by Grygu´c et al. [16, 17] in
medium-scale I-beam style forgings. Those studies concluded that the
favourable texture modification due to forging was the alignment of the
c-axis of the hexagonal-closed-packed (HCP) crystal to be parallel with
the local forging direction (or direction of dominant compressive
loading), which is often normal to the die surface in relatively thin
forgings at areas closer to the surface of the forged component.
Depending on the process temperature, and local thermal history
(accounting for the heat of deformation), the material recrystallization
process may be highly non-uniform and heterogeneous in nature, resulting
in spatially varying microstructure, texture and thus mechanical
properties.
# Figure 6
illustrates the relationship between the primary thermal process
parameter (the target forging temperature) and the resulting yield
strength for a variety of forged AZ80 Mg. Similar AZ80 Mg material
conditions are presented, cast [3], extruded [17], cast-forged [3] and
extruded forged [17], and they are once again grouped into categories of
starting microstructure for clarity in illustrating the process-property
relationship of forged AZ80 Mg. It can be observed that there is a
general trend (albeit it, quite weak) where lower forging temperatures
produce material with higher strength. This relatively poor correlation
will subsequently be discussed in relation to the recrystallization
mechanisms present during the forging process. For wrought forms of base
material (i.e., extruded), forging at higher temperatures can result in
grain growth (resulting in lower strength) and lower temperatures the
potential for shear cracking (from reduced ductility) [30]. Of the
studies presented, all were isothermal in nature (forging dies and
billet are at the same temperature), and the forging temperature window
ranges 250–450 °C. Beneath 250 °C, edge cracking and poor formability
result as only a few deformation mechanisms become active, and large
strain is not achievable [31]. At temperatures higher than 427 °C,
incipient melting of the Mg17Al12 eutectic phase will occur near the
grain boundary [13, 32]. This propensity towards incipient melting at
higher processing temperatures for AZ series Mg alloys limits them from
being extruded quickly, as compared to other structural metals, the
processing window is quite narrow; however, this is not as limiting with
die forging as the local deformation rates are typically smaller [33].
Chaudhury et al. suggest for AZ80 Mg, a non-isothermal forging process
with a temperature window of 290–400 °C for the billet, and 205–290 °C
for the forging dies to avoid these aforementioned types of defects that
can result in the as-forged material [30] which agrees well with the
trend seen in the current study.
|Forging Temperature [°C]|Yield Strength|
|---|---|
|300|280|
|260|240|
|220|200|
|180|160|
|140|120|
|100| |
Figure 6. Relationship between primary thermal process parameter
(forging temperature) and resulting yield strength of AZ80 Mg after
forging (subdivided into various starting material conditions). The
current study denotes properties extracted from various locations of the
extruded then die-forged automotive suspension control arm forged at
various temperatures (300 °C and 450 °C). For reference, extruded-forged
data can found in the following studies [5–13,16], cast-forged from
[3,16].
The stress-controlled fatigue response of the closed-die forged AZ80 Mg
automotive suspension component is illustrated in Figure 7 for the two
target forging temperatures that were investigated in this current
study. It can be easily observed that for virtually all of the tests
performed, for a given stress amplitude, the lower forging temperature
(300 °C) pro-
- source_sentence: >-
content='1. What does MSE stand for in the context provided?\n2. Which
technique is abbreviated as OES?' additional_kwargs={'refusal': None}
response_metadata={'token_usage': {'completion_tokens': 24,
'prompt_tokens': 196, 'total_tokens': 220, 'completion_tokens_details':
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'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_709714d124',
'finish_reason': 'stop', 'logprobs': None}
id='run-798f8f65-4b52-4198-a267-dc6f61a192d0-0'
usage_metadata={'input_tokens': 196, 'output_tokens': 24, 'total_tokens':
220, 'input_token_details': {'audio': 0, 'cache_read': 0},
'output_token_details': {'audio': 0, 'reasoning': 0}}
sentences:
- >-
# References
1. J.-H. Jun, "Dependence of hardness on interlamellar spacing of
discontinuous precipitates in cast AZ91 magnesium alloy," Journal of
Alloys and Compounds, vol. 725, pp. 237-241, 2017.
2. H. Jafari, M.H. Idris, A. Ourdjini, and G. Payganeh, "Effect of
thermomechanical treatment on microstructure and hardness behavior of
AZ63 magnesium alloy," Acta Metallurgica Sinica, vol. 22, pp. 401-407,
2009.
3. Fuan Wei, Zhengang Zhang, Bo Shi, Chen Yang, and Jinhui Wang, "Effect
of rolling deformation on microstructure and mechanical properties of
Mg-6Sn-3Al-1Zn alloy," Materials Research Express, vol. 7, p. 026516,
2020.
4. Ondřej Hilšer, Stanislav Rusz, Pavel Szkandera, Lubomír Čížek, Martin
Kraus, Jan Džugan, and Wojciech Maziarz, "Study of the Microstructure,
Tensile Properties and Hardness of AZ61 Magnesium Alloy Subjected to
Severe Plastic Deformation," Metals, vol. 8, p. 776, 2018.
5. Longqing Tang, Guowei Bo, Fulin Jiang, Shiwei Xu, Jie Teng, Dingfa
Fu, Hui Zhang, "Unravelling the precipitation evolutions of AZ80
magnesium alloy during non-isothermal and isothermal processes," Journal
of Materials Science & Technology, vol. 75, pp. 184-195, 2021.
6. Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen,
"Hierarchical text-conditional image generation with clip latents,"
arXiv preprint arXiv:2204.06125, p.
https://doi.org/10.48550/arXiv.2204.06125, 2022.
7. P. Acar, "Machine Learning Approach for Identification of
Microstructure–Process Linkages," AIAA Journal, vol. 57, no. 8, pp.
3608-3614; https://doi.org/10.2514/1.J058244, 2019.
8. Yanming Liu, Shu Jian Chen, Kwesi Sagoe-Crentsil, Wenhui Duan, "Large
set microstructure reconstruction mimicking quantum computing approach
via deep learning," Acta Materialia, vol. 230, p. 117860;
https://doi.org/10.1016/j.actamat.2022.117860, 2022.
9. Yue Liu, Tianlu Zhao, Wangwei Ju, Siqi Shi, "Materials discovery and
design using machine learning," Journal of Materiomics, vol. 3, no. 3,
pp. 159-177; https://doi.org/10.1016/j.jmat.2017.08.002, 2017.
10. Brian L. DeCost, Toby Francis, Elizabeth A. Holm, "Exploring the
microstructure manifold: Image texture representations applied to
ultrahigh carbon steel microstructures," Acta Materialia, vol. 133, pp.
30-40; https://doi.org/10.1016/j.actamat.2017.05.014, 2017.
11. Daniel A. Cogswell and W. Craig Carter, "Thermodynamic phase-field
model for microstructure with multiple components and phases: The
possibility of metastable phases," Phys. Rev. E, vol. 83, no. 6, p.
061602; https://doi.org/10.1103/PhysRevE.83.061602, 2011.
12. S. Florez, M. Shakoor, T. Toulorge, M. Bernacki, "A new finite
element strategy to simulate microstructural evolutions," Computational
Materials Science, vol. 172, p. 109335;
https://doi.org/10.1016/j.commatsci.2019.109335, 2020.
13. Damien Tourret, Hong Liu, Javier LLorca, "Phase-field modeling of
microstructure evolution: Recent applications, perspectives and
challenges," Progress in Materials Science, vol. 123, p. 100810;
https://doi.org/10.1016/j.pmatsci.2021.100810, 2022.
- |-
# MSE – Mean Squared Error
# NRMSE – Normalized Root Mean Squared Error
# OES – Optical Emission Spectrometry
# PSN – Particle Stimulated Nucleation
# ReLU – Rectified linear unit
# RT – Room Temperature
# SDAS – Secondary Dendrite Arm Spacing
# SEM – Scanning Electron Microscopy
# SGD – Stochastic Gradient Descent
# SVM – Support Vector Machine
# TD – Transverse Direction
# UTS – Ultimate Tensile Strength
# VAE – Variational Autoencoder
# XCT – X-ray Computed Tomography
# YS – Yield Strength
# xxi
- >-
analytical or even empirical model that could relate a specific process
parameter to a selected property. Also, compared to steel or aluminum
alloys, there are still not enough experimental results that guide
researchers and manufacturers in selecting proper processing routes and
finding the optimum combination of process parameters.
Machine learning techniques, as computational tools that use data
gathered from experience to improve performance, have shown to be
successful in establishing such relationships between interacting
variables. Machine learning methods can be used to connect sparse
findings on production of magnesium alloys to provide some insight into
missing pieces of information. Such a link between different parameters
not only help researchers to predict desired properties or select
optimum parameters, but also can be used in theoretical studies of
controlling mechanisms, which can finally give rise to more robust
analytical or empirical models.
In the current study, the feasibility of using machine learning
approaches in establishing process-microstructure-property relationship
would be put to the test. For this purpose, cast-forging process of AZ80
magnesium alloy, as a cost-effective hybrid manufacturing method, is
going to be studied through advanced characterization methods. The
effect of different process parameters of casting, forging, and
intermediate thermo-mechanical processes and microstructural and
mechanical properties of material in various stages of manufacturing are
parameters of interest which will also be linked through machine
learning models.
# 1.2 Challenges and Opportunities
To establish a link between process, microstructure, and property, a
complete study of all the controlling parameters is required. Previous
studies on the viability of production of magnesium-based structural
components cover a variety of parameters and properties [3,4,7,8], but
since these studies have focused on different aspects of production, a
complete experimental study on the effect of parameters of casting and
forging processes for AZ80 magnesium alloy is selected as a basis for
the establishment of this relationship. It also contributes to
broadening the knowledge of cast-forging manufacturing of magnesium
alloys, where thorough experimental work is unavailable.
Such an experimental study allows us to measure all properties in a
controlled, consistent approach, while it also allows us to incorporate
different processing parameters and, if necessary, append new variables
based on the performance of machine learning models.
As the experimental part progresses through different processing steps
like wedge casting, cylinder casting, homogenization, I-beam forging,
optimized preform casting, and forging the final structural components,
data-driven models are developed to link process parameters to
microstructural features.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.9948979591836735
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 1
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 1
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9948979591836735
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.33333333333333337
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19999999999999998
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09999999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.9948979591836735
name: Cosine Recall@1
- type: cosine_recall@3
value: 1
name: Cosine Recall@3
- type: cosine_recall@5
value: 1
name: Cosine Recall@5
- type: cosine_recall@10
value: 1
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9981169885386298
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9974489795918368
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9974489795918368
name: Cosine Map@100
SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-l. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
Model Details
Model Description
- Model Type: Sentence Transformer
- Base model: Snowflake/snowflake-arctic-embed-l
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 1024 dimensions
- Similarity Function: Cosine Similarity
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
Usage
Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("tabesink92/mg_alloy-snowflake-arctic-embed-l-ft-v2")
# Run inference
sentences = [
"content='1. What does MSE stand for in the context provided?\\n2. Which technique is abbreviated as OES?' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 24, 'prompt_tokens': 196, 'total_tokens': 220, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_709714d124', 'finish_reason': 'stop', 'logprobs': None} id='run-798f8f65-4b52-4198-a267-dc6f61a192d0-0' usage_metadata={'input_tokens': 196, 'output_tokens': 24, 'total_tokens': 220, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}",
'# MSE – Mean Squared Error\n\n# NRMSE – Normalized Root Mean Squared Error\n\n# OES – Optical Emission Spectrometry\n\n# PSN – Particle Stimulated Nucleation\n\n# ReLU – Rectified linear unit\n\n# RT – Room Temperature\n\n# SDAS – Secondary Dendrite Arm Spacing\n\n# SEM – Scanning Electron Microscopy\n\n# SGD – Stochastic Gradient Descent\n\n# SVM – Support Vector Machine\n\n# TD – Transverse Direction\n\n# UTS – Ultimate Tensile Strength\n\n# VAE – Variational Autoencoder\n\n# XCT – X-ray Computed Tomography\n\n# YS – Yield Strength\n\n# xxi',
'analytical or even empirical model that could relate a specific process parameter to a selected property. Also, compared to steel or aluminum alloys, there are still not enough experimental results that guide researchers and manufacturers in selecting proper processing routes and finding the optimum combination of process parameters.\n\nMachine learning techniques, as computational tools that use data gathered from experience to improve performance, have shown to be successful in establishing such relationships between interacting variables. Machine learning methods can be used to connect sparse findings on production of magnesium alloys to provide some insight into missing pieces of information. Such a link between different parameters not only help researchers to predict desired properties or select optimum parameters, but also can be used in theoretical studies of controlling mechanisms, which can finally give rise to more robust analytical or empirical models.\n\nIn the current study, the feasibility of using machine learning approaches in establishing process-microstructure-property relationship would be put to the test. For this purpose, cast-forging process of AZ80 magnesium alloy, as a cost-effective hybrid manufacturing method, is going to be studied through advanced characterization methods. The effect of different process parameters of casting, forging, and intermediate thermo-mechanical processes and microstructural and mechanical properties of material in various stages of manufacturing are parameters of interest which will also be linked through machine learning models.\n\n# 1.2 Challenges and Opportunities\n\nTo establish a link between process, microstructure, and property, a complete study of all the controlling parameters is required. Previous studies on the viability of production of magnesium-based structural components cover a variety of parameters and properties [3,4,7,8], but since these studies have focused on different aspects of production, a complete experimental study on the effect of parameters of casting and forging processes for AZ80 magnesium alloy is selected as a basis for the establishment of this relationship. It also contributes to broadening the knowledge of cast-forging manufacturing of magnesium alloys, where thorough experimental work is unavailable.\n\nSuch an experimental study allows us to measure all properties in a controlled, consistent approach, while it also allows us to incorporate different processing parameters and, if necessary, append new variables based on the performance of machine learning models.\n\nAs the experimental part progresses through different processing steps like wedge casting, cylinder casting, homogenization, I-beam forging, optimized preform casting, and forging the final structural components, data-driven models are developed to link process parameters to microstructural features.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Information Retrieval
- Evaluated with
InformationRetrievalEvaluator
| Metric | Value |
|---|---|
| cosine_accuracy@1 | 0.9949 |
| cosine_accuracy@3 | 1.0 |
| cosine_accuracy@5 | 1.0 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.9949 |
| cosine_precision@3 | 0.3333 |
| cosine_precision@5 | 0.2 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.9949 |
| cosine_recall@3 | 1.0 |
| cosine_recall@5 | 1.0 |
| cosine_recall@10 | 1.0 |
| cosine_ndcg@10 | 0.9981 |
| cosine_mrr@10 | 0.9974 |
| cosine_map@100 | 0.9974 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 892 training samples
- Columns:
sentence_0andsentence_1 - Approximate statistics based on the first 892 samples:
sentence_0 sentence_1 type string string details - min: 340 tokens
- mean: 371.01 tokens
- max: 410 tokens
- min: 29 tokens
- mean: 439.26 tokens
- max: 512 tokens
- Samples:
sentence_0 sentence_1 content='1. What experimental method was used to investigate the fatigue properties of extruded AZ80 magnesium alloys in the study? \n2. How does the crack initiation mechanism change with varying stress amplitudes in the AZ80 magnesium alloys?' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 46, 'prompt_tokens': 731, 'total_tokens': 777, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_7fcd609668', 'finish_reason': 'stop', 'logprobs': None} id='run-d649e508-ccc4-420e-b812-67719c92f2cb-0' usage_metadata={'input_tokens': 731, 'output_tokens': 46, 'total_tokens': 777, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}# Fatigue behaviour and fractography of extruded AZ80 magnesium alloys in very high cycle regime
# Kazuaki Shiozawaa *, Tomoki Kashiwagi b, Tutomu Murai c, Tooru Takahashi c
b Tohoku Electric Power Co.Inc., Sendai980-8550, Japan
c Sankyo-Tateyama Aluminum Industry Co. Ltd., Imizu934-8577, Japan
Received 26 February 2010; revised 11 March 2010; accepted 15 March 2010
# Abstract
In order to investigate the fatigue properties of extruded magnesium alloy in very high-cycle regime, rotary bending fatigue test was performed in ambient atmosphere at room temperature using the hourglass shaped specimens of AZ80 alloys extruded (F-specimen) and treated by an artificial aging after extrusion (T5-specimen). From the experimental results, both specimens show a clear step-wise S-N curve on which two knees exists. Specific stress amplitude formed the knee corresponded to the 0.2% offset proof stress of 160MPa in compression. From the detailed observation of fracture surface, small facet-like s...content='1. What experimental method was used to investigate the fatigue properties of extruded AZ80 magnesium alloys in the study? \n2. How does the crack initiation mechanism change with varying stress amplitudes in the AZ80 magnesium alloys?' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 46, 'prompt_tokens': 731, 'total_tokens': 777, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_7fcd609668', 'finish_reason': 'stop', 'logprobs': None} id='run-5e3c339c-30e6-4dc7-bacb-24b9143312ad-0' usage_metadata={'input_tokens': 731, 'output_tokens': 46, 'total_tokens': 777, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}# Fatigue behaviour and fractography of extruded AZ80 magnesium alloys in very high cycle regime
# Kazuaki Shiozawaa *, Tomoki Kashiwagi b, Tutomu Murai c, Tooru Takahashi c
b Tohoku Electric Power Co.Inc., Sendai980-8550, Japan
c Sankyo-Tateyama Aluminum Industry Co. Ltd., Imizu934-8577, Japan
Received 26 February 2010; revised 11 March 2010; accepted 15 March 2010
# Abstract
In order to investigate the fatigue properties of extruded magnesium alloy in very high-cycle regime, rotary bending fatigue test was performed in ambient atmosphere at room temperature using the hourglass shaped specimens of AZ80 alloys extruded (F-specimen) and treated by an artificial aging after extrusion (T5-specimen). From the experimental results, both specimens show a clear step-wise S-N curve on which two knees exists. Specific stress amplitude formed the knee corresponded to the 0.2% offset proof stress of 160MPa in compression. From the detailed observation of fracture surface, small facet-like s...content='1. What is the chemical composition of the AZ80 magnesium alloy used in the study?\n2. How was the T5 treatment applied to the specimens, and what was its purpose in the experiment?' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 41, 'prompt_tokens': 617, 'total_tokens': 658, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_7fcd609668', 'finish_reason': 'stop', 'logprobs': None} id='run-1ea8b034-255f-4994-b459-394267605838-0' usage_metadata={'input_tokens': 617, 'output_tokens': 41, 'total_tokens': 658, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}# 2. Experimental Procedures
# 2.1. Testing materials and specimen
The material used in this study was commercial Mg-Al-Zn magnesium alloy, AZ80. The chemical composition of these materials (in mass percentage) is 8.24Al, 0.67Zn, 0.20Mn, 0.005Fe, 0.012Si, 0.0008Cu, 0.0007Ni and balanced Mg. The bar with 16mm in diameter was extruded from a billet of 160mm diameter (an extrusion ratio of 99.4:1) under the extrusion ram speed of 1.5m/min at temperature of 623K. Hour-glass shaped specimens with a grip diameter of 10 mm and minimum diameter of 5 mm (Fig. 1) was machined from the extruded bar with the loading axis parallel to their extrusion directions. The elastic stress concentration factor, Kt, of these specimens was 1.065. On the other hand, to investigate the effect of an aging treatment on fatigue behavior, specimens were prepared from the extruded bar which was heated at 473K for 32h and then air-cooled (T5 treatment). From now on, the specimen extruded and no heat-treated is refer... - Loss:
MatryoshkaLosswith these parameters:{ "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy: stepsper_device_train_batch_size: 16per_device_eval_batch_size: 16num_train_epochs: 25multi_dataset_batch_sampler: round_robin
All Hyperparameters
Click to expand
overwrite_output_dir: Falsedo_predict: Falseeval_strategy: stepsprediction_loss_only: Trueper_device_train_batch_size: 16per_device_eval_batch_size: 16per_gpu_train_batch_size: Noneper_gpu_eval_batch_size: Nonegradient_accumulation_steps: 1eval_accumulation_steps: Nonetorch_empty_cache_steps: Nonelearning_rate: 5e-05weight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08max_grad_norm: 1num_train_epochs: 25max_steps: -1lr_scheduler_type: linearlr_scheduler_kwargs: {}warmup_ratio: 0.0warmup_steps: 0log_level: passivelog_level_replica: warninglog_on_each_node: Truelogging_nan_inf_filter: Truesave_safetensors: Truesave_on_each_node: Falsesave_only_model: Falserestore_callback_states_from_checkpoint: Falseno_cuda: Falseuse_cpu: Falseuse_mps_device: Falseseed: 42data_seed: Nonejit_mode_eval: Falseuse_ipex: Falsebf16: Falsefp16: Falsefp16_opt_level: O1half_precision_backend: autobf16_full_eval: Falsefp16_full_eval: Falsetf32: Nonelocal_rank: 0ddp_backend: Nonetpu_num_cores: Nonetpu_metrics_debug: Falsedebug: []dataloader_drop_last: Falsedataloader_num_workers: 0dataloader_prefetch_factor: Nonepast_index: -1disable_tqdm: Falseremove_unused_columns: Truelabel_names: Noneload_best_model_at_end: Falseignore_data_skip: Falsefsdp: []fsdp_min_num_params: 0fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap: Noneaccelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed: Nonelabel_smoothing_factor: 0.0optim: adamw_torchoptim_args: Noneadafactor: Falsegroup_by_length: Falselength_column_name: lengthddp_find_unused_parameters: Noneddp_bucket_cap_mb: Noneddp_broadcast_buffers: Falsedataloader_pin_memory: Truedataloader_persistent_workers: Falseskip_memory_metrics: Trueuse_legacy_prediction_loop: Falsepush_to_hub: Falseresume_from_checkpoint: Nonehub_model_id: Nonehub_strategy: every_savehub_private_repo: Nonehub_always_push: Falsegradient_checkpointing: Falsegradient_checkpointing_kwargs: Noneinclude_inputs_for_metrics: Falseinclude_for_metrics: []eval_do_concat_batches: Truefp16_backend: autopush_to_hub_model_id: Nonepush_to_hub_organization: Nonemp_parameters:auto_find_batch_size: Falsefull_determinism: Falsetorchdynamo: Noneray_scope: lastddp_timeout: 1800torch_compile: Falsetorch_compile_backend: Nonetorch_compile_mode: Nonedispatch_batches: Nonesplit_batches: Noneinclude_tokens_per_second: Falseinclude_num_input_tokens_seen: Falseneftune_noise_alpha: Noneoptim_target_modules: Nonebatch_eval_metrics: Falseeval_on_start: Falseuse_liger_kernel: Falseeval_use_gather_object: Falseaverage_tokens_across_devices: Falseprompts: Nonebatch_sampler: batch_samplermulti_dataset_batch_sampler: round_robin
Training Logs
| Epoch | Step | Training Loss | cosine_ndcg@10 |
|---|---|---|---|
| 0.8929 | 50 | - | 0.9525 |
| 1.0 | 56 | - | 0.9638 |
| 1.7857 | 100 | - | 0.9801 |
| 2.0 | 112 | - | 0.9743 |
| 2.6786 | 150 | - | 0.9849 |
| 3.0 | 168 | - | 0.9812 |
| 3.5714 | 200 | - | 0.9868 |
| 4.0 | 224 | - | 0.9962 |
| 4.4643 | 250 | - | 0.9925 |
| 5.0 | 280 | - | 0.9944 |
| 5.3571 | 300 | - | 0.9944 |
| 6.0 | 336 | - | 0.9944 |
| 6.25 | 350 | - | 0.9944 |
| 7.0 | 392 | - | 0.9981 |
| 7.1429 | 400 | - | 0.9962 |
| 8.0 | 448 | - | 0.9981 |
| 8.0357 | 450 | - | 0.9981 |
| 8.9286 | 500 | 0.3646 | 0.9981 |
| 9.0 | 504 | - | 0.9981 |
| 9.8214 | 550 | - | 0.9981 |
| 10.0 | 560 | - | 0.9981 |
| 10.7143 | 600 | - | 0.9981 |
| 11.0 | 616 | - | 0.9981 |
| 11.6071 | 650 | - | 0.9981 |
| 12.0 | 672 | - | 0.9944 |
| 12.5 | 700 | - | 0.9981 |
| 13.0 | 728 | - | 0.9981 |
| 13.3929 | 750 | - | 0.9981 |
| 14.0 | 784 | - | 0.9981 |
| 14.2857 | 800 | - | 0.9981 |
| 15.0 | 840 | - | 0.9981 |
| 15.1786 | 850 | - | 0.9981 |
| 16.0 | 896 | - | 0.9981 |
| 16.0714 | 900 | - | 0.9981 |
| 16.9643 | 950 | - | 0.9981 |
| 17.0 | 952 | - | 0.9981 |
| 17.8571 | 1000 | 0.064 | 0.9981 |
| 18.0 | 1008 | - | 0.9981 |
| 18.75 | 1050 | - | 0.9981 |
| 19.0 | 1064 | - | 0.9981 |
| 19.6429 | 1100 | - | 0.9981 |
| 20.0 | 1120 | - | 0.9981 |
| 20.5357 | 1150 | - | 0.9981 |
| 21.0 | 1176 | - | 0.9981 |
| 21.4286 | 1200 | - | 0.9981 |
| 22.0 | 1232 | - | 0.9981 |
| 22.3214 | 1250 | - | 0.9981 |
| 23.0 | 1288 | - | 0.9981 |
| 23.2143 | 1300 | - | 0.9981 |
| 24.0 | 1344 | - | 0.9981 |
| 24.1071 | 1350 | - | 0.9981 |
| 25.0 | 1400 | - | 0.9981 |
Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
Citation
BibTeX
Sentence Transformers
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
MatryoshkaLoss
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
MultipleNegativesRankingLoss
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}