To study the microstructures and mechanical properties of the alloys in the cast, annealed and rolled conditions. But the hardness of the alloys was dependent on the Sn content in the alloy.
Y values corresponding to (a) first exothermic peak and (b) second exothermic peak of Alloy-A. 1000/T) to give. Y values corresponding to (a) first exothermic peak and (b) second exothermic peak of Alloy-D. 1000/T) to give.
List of Tables
Nomenclature
Greek
Introduction
The strengthening of these alloys after deformation is achieved by age hardening (or precipitation hardening). These studies also include the effect of sport additions of Sn on the age hardening behavior, the precipitation kinetics and the high temperature deformation behavior of these alloys.
Literature Survey
Introduction
The mechanical properties of these alloys are affected by the composition, strain history and microstructure resulting from the thermo-mechanical treatment given before end use. The generation of processing maps is important as they demonstrate the hot workability of these compounds, which is important for commercial viability.
Aluminum alloys
The 7xxx series alloys with Zn as the main alloying element (along with other elements, mainly Mg and/or Cu) are heat treated aluminum alloys that exhibit the highest strength. The 2xxx series has Cu as the main alloying element with minor amounts of other elements.
Effect of alloying elements in Aluminum alloys
Mg in Al alloys increases strength by solid solution hardening and improves their strain hardening ability. Up to 0.3 % Cd in Al-Cu alloys accelerates the age hardening rate in addition to increasing strength and corrosion resistance.
Microalloying
Precipitation strengthening
The strength and hardness of metal alloys can be improved by the precipitation of extremely small and uniformly distributed second phase particles in the alloy matrix. Such alloys should show a considerable solubility of one component in the other and a rapid decrease in solubility limit of the solute atom with a decrease in temperature.
Precipitation strengthening mechanisms
During the application of an external load, plastic deformation begins with the movement of dislocations through the matrix. In the case of the Al-Cu alloy system, θ´´ is usually coherent with the matrix, while the solid phase θ (CuAl2) is the incoherent particle.
Precipitation strengthening process
Most alloys require artificial aging, and the aging temperature is usually between room temperature and solution treatment temperature. The enhancement achieved is determined by the size, crystal structure and distribution of the second phase particles, which in turn depend on the precipitation sequence or phases involved.
Precipitation stages
High temperature deformation behavior
- Constitutive models
The initial development of the constitutive relations was based on the assumption that the flow stress (σ) is a function only of the instantaneous values of ε, ε• and T [GEOR1988], i.e. A computer representation of constitutive relations (MATMOD) was developed [SCHM1981] for analyzing high temperature deformations.
A sine hyperbolic relationship particularly useful for correlating stress, T and ε• under hot working conditions was first proposed by Sellars and Tegart [SELL1966, TEGA1968]. The Zener-Hollomon parameter has also been found to be a useful tool for describing the high temperature deformation behavior of metallic materials.
Zener-Hollomon parameter (Z) and activation energy (Q)
Artificial neural network (ANN) modeling
Once the ANN architecture is established, the output can be predicted for any combination of input variables. In the forward pass, the input signals propagate from the network input to the output.
Processing map for hot workability
The constitutive response of the material at a given T during hot deformation essentially depends on ε• and to a small extent on ε, i.e. the workpiece is viscoplastic. Adiabatic shear bands: During deformation at higher ε• the lack of sufficient time to dissipate the generated heat causes a local temperature rise in the workpiece.
Flow behavior and processing maps
Multiple Linear Regression (MLR) analysis
In all cases, the estimation measure is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function, which can be described by a probability distribution. The performance of regression analysis methods in practice depends on the form of the data generating process and how it relates to the regression approach used.
Since the true form of the data generation process is unknown, regression analysis depends to some extent on assumptions about this process.
Applications of MLR to high temperature metal working process
Objectives of the present investigations
Influence of trace additions of Sn on the microstructure and mechanical properties of the Al-Cu-Mg alloys under cast, annealed and rolled conditions. Effect of Sn addition on the age-hardening conditions required to achieve maximum alloy hardness. Effect of Sn addition on the high temperature deformation behavior of the Al-Cu-Mg alloys.
To study the effect of Sn addition on the high temperature deformation behavior of the studied alloy system.
Processing of Al–Cu–Mg alloys by casting technique
- Equipment used for the preparation of the alloys
Details of the experimental setups designed and constructed during the experimental investigations are given below. The characterization techniques used and experimental procedures adopted in the research conducted in this thesis are also detailed below.
Design and fabrication of resistance furnace
Metallic moulds
- Raw materials used
- Casting procedure
- Composition analysis
- Post casting heat treatments
- Warm rolling of cast ingots
- Microstructural characterization
- Metallographic sample preparation
- Optical microscopic (OM) investigation
- Scanning electron microscopic (SEM) investigation
- Differential Scanning Calorimeter (DSC) studies
- X-Ray diffraction (XRD) analysis
- Mechanical property determination
- Hardness test
- Tensile test
- High temperature compression test procedure
An appropriate amount of Al–25wt.%Sn main alloy was then added to the melt. Microstructural characterization of the alloys was performed both in as-cast and heat-treated conditions. Once the appropriate network architecture for the alloy was achieved, it was possible to successfully predict the current stress (σ) for any combination of input parameters (ε, ε& and T) in a given region of the domain. d) Current stress values predicted by ANN were used to generate treatment maps for all alloys.
Once the yield stress value (σ) is determined at a particular ε•, the same can be generated in adjacent ranges of ε• values as explained in Figure 3.6.
Results and Discussion
Composition and alloy designation
Microstructure of the alloys
- Microstructure of as-cast alloys
- Cast and homogenized alloys
- Cast and solutionized alloys
Changes in the average size of alloys with Sn content in as-cast and molten conditions are shown in Figure 4.5. The micrographs reveal the removal of dendritic structures present in the cast state by heat treatment. This is further supported by Figure 4.5, which shows the change in average grain size with Sn content after dissolution.
For the dissolved and water-cooled alloys, the average grain size of the base alloy increased from 65 µm to 104 µm when the Sn content is increased from 0 to 0.06 wt%.
Mechanical Properties
- Hardness
- Tensile properties
- Cast and homogenized alloys
- Tensile properties of rolled alloys Rolled and homogenized alloys
YS and UTS of the alloy increased with an increase in Sn content up to 0.06 wt%. The percent elongation of the alloy decreased with an increase in Sn content from 0.02 wt% to 0.06 wt%. The percentage elongation of the Al-Cu-Si-Mg alloy was higher due to the addition of 0.1 wt% Sn.
The maximum strength between rolled and homogenized Al-Cu-Mg alloys was observed during microalloying with 0.06 wt.% Sn.
Rolled and peak aged alloys
Age-hardening behavior
Further aging resulted in a decrease in hardness, which is the typical age hardening behavior of Al-Cu alloys. For a constant aging time, the hardness increased with increasing Sn content up to 0.06 wt%. This variation of hardness with Sn content at any aging time resembles the data shown in Figure 4.11.
But Figure 4.18 shows that the addition of trace amounts of Sn has no significant effect on the peak aging time of Al-Cu-Mg alloys, although the alloy hardness is affected by the Sn content.
Ageing Time (hours)
Precipitation strengthening and reaction kinetics
- Differential Scanning Calorimeter (DSC) studies
- X-Ray diffraction (XRD) studies
The exothermic peak(s) in the low temperature range correspond to the precipitation reaction(s), while the endotherm(s) at the high temperature range correspond to the melting of the alloy phase(s). These plots are shown in Figure 4.23 to Figure 4.28 for the exothermic peaks observed in the DSC curves of the investigated alloys. Values of average ∆E and k0 obtained for the exothermic peaks observed in the DSC curves of the alloys.
Functional forms of f(Y) and E(Y) obtained for the exothermic peaks observed in the DSC curves of the alloys.
High temperature deformation behavior
- Flow stress behavior
- Constitutive modeling
Trace amounts of Sn in alloy-D and alloy-F did not show any significant influence on the unit cell volume of the two constituent phases. Oven-cooled samples (XRD patterns not shown here) also revealed the presence of these two main constituent phases after heat treatment.
Zener-Hollomon parameter approach
The maximum yield stress (σp) for all the alloys determined at different deformation conditions is shown in Table 4.12. Therefore, in the present analysis, the optimal value of α = 0.01 was used for all the studied alloys. These plots for all the Al-Cu-Mg alloys studied are shown in Figure 4.44 to Figure 4.49.
The values of ln(Z) at different ε• and deformation T are shown in Table 4.14 to Table 4.19 for.
Prediction of peak flow stress
Prediction of flow stress by Multiple Linear Regression (MLR) analysis
In the present analysis, 90 sets of independent variables were used to estimate the regression coefficients. Therefore, matrix Y (90×1) shows the flow stresses, matrix X (90×4) shows the input variables (ε,ε• ,T) and matrix B (4×1) shows the regression coefficients. 2.39) determined by MLR analysis for all connections and given in the table are the values of σ under different deformation conditions. For a perfect prediction, all points should lie on the solid line shown in the figure, which is inclined at 45° to the x-axis.
Values of the coefficients of the regression analysis equation used to predict σ values of the alloys.
Prediction of flow stress by Artificial Neural Network modeling
Maximum absolute error, maximum percentage error and RMS error obtained for the alloys during training, testing and validation. Comparison of experimental and predicted values of yield stress, σ for the Alloy-E validation test. Comparison of experimental and predicted values of yield stress, σ for the Alloy-F validation test.
Variation of predicted current stress with experimental current stress for the validation data set of (a) Alloy-A (b) Alloy-B (c) Alloy-C (d) Alloy-D (e) Alloy-E and (f) Alloy-F.
Generation of processing maps for hot workability
4.57 (a) and (d) show contour plots for power dissipation efficiency and m at different T and ε• for alloy-A after its deformation to an ε of 0.2. The maximum power dissipation efficiency value of 70 % covering almost the whole temperature range studied was obtained for Alloy-F at. The maximum in power dissipation efficiency is obtained where m has its maximum value.
With the addition of Sn up to 0.06 wt.%, the ratio of stable/unstable region increases.
Conclusion and Scope of Future Work
Conclusions
The high activation energy of the alloys with Sn wt.% > 0.04 shows that these alloys are relatively difficult to deform, despite the fact that these alloys exhibit better mechanical properties. For all these alloys, yield stresses at different strain rates, strain rates, and temperatures during hot deformation were predicted using MLR analysis and ANN modeling. The power dissipation efficiency maps of these alloys showed that the maximum efficiency was between 60% and 70% for all alloys, except for the alloy with 0.06 wt% Sn, which showed a low value of 40.
Microstructural investigations of the hot-worked samples in combination with modeling studies provided an in-depth understanding of the metallurgical changes that occur during hot deformation of these alloys.
Scope for future work
