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Effect of short heat treatment on mechanical properties and shape memory properties of Cu–Al–Ni shape memory alloy

An erratum for this article can be found here: https://doi.org/10.1515/eng-2023-0101
  • Raad Suhail Ahmed Adnan ORCID logo EMAIL logo , Iman Adnan Annon ORCID logo , Sundus M. Noori and Dauod Selman Dauod ORCID logo
Published/Copyright: April 8, 2024
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Abstract

The transformation of shape-memory alloys to high-temperature shape-memory alloys can be achieved through either the addition of alloying elements or heat treatment. However, heat treatment is more effective in improving the properties of the alloys. This research paper explores the impact of annealing on the mechanical and shape memory properties of Cu–Al–Ni shape memory alloys. The alloys were first cast, homogenized, and then machined before being aged at temperatures of 250, 300, and 350°C, and finally air-cooled. The results showed an increase in transformation temperature and recovery strain, as well as shape memory effect, and a decrease in hardness. Moreover, there was an increase in yield stress and strain. In conclusion, aging was found to improve the shape memory properties and mechanical properties better than thermomechanical treatment and some alloying elements.

1 Introduction

Shape Memory Alloys (SMA) have been in high demand since the beginning of the century for their applications in fields ranging from biomedical to actuators. They are considered smart materials that can be improved and modified [1]. The SMA’s mechanical and shape memory properties can be improved by adding alloying elements [2,3,4] or by using heat treatment methods such as annealing, aging, thermomechanical, etc. Cu-based SMAs are in demand due to economic reasons, and they are manufactured by casting or powder metallurgy. Although they are hard, they can be machined by EDM machines. Researchers like Abbass et al. [5] have studied the effect of heat treatment on the martensitic transformation of Cu-based shape memory alloys, and they found that it increases the hardness and decreases elongation. Aksu Canbay and Karagoz [6] found that aging increases the transformation temperatures, transformation hysteresis, and successive martensitic transitions. The enthalpy of reverse transformation increases with aging temperature, but it exhibits no systematic dependence on aging time. Cu–Al–Ni shape memory alloy parameters are affected by aging. Agarwal and Dube [7] found that Heat treatment reduces the alloys’ mechanical properties. It increases the alloy’s hardness. Payandeh et al. [8] studied the effect of heat treatment on the martensitic transformation of Cu-based shape memory alloys with the increase of hardness and decrease of Elongation. Suresh and Ramamurty [9,10] found that The differential scanning calorimetry (DSC) thermograms of the aged samples show an increase in transformation temperatures as well as transformation hysteresis with aging. Both the tensile and the compression responses and their variation with aging were evaluated. With aging, successive martensitic transitions and the DSC thermogram of the aged samples show an increase in transformation temperatures as well as transformation hysteresis. Dagadelen et al. [11] found it was also confirmed that the transformation temperatures increased with thermal treatment temperature and time. In addition, an enthalpy of reverse transformation increased with aging temperature, but it exhibited no systematic dependence on the aging time. Nevin Balo and Neslihan [12] found that the parameters of the Cu–Al–Ni shape memory alloy are affected by aging. Ivanić et al. [13] found that the heat treatment procedure reduced the alloys’ mechanical properties and increased the hardness of the alloy ness. Yavuzer et al. [14] studied the effect of heat treatment the effects of milling time and heat treatment on the microstructure and wear behavior of Cu–Al–Ni shape memory alloys produced by the mechanical alloying method were investigated. Cu–Al–Ni shape memory alloys were produced by mechanical alloying in four different durations, Rashidi et al. [15] modified the formed martensite phases and MT temperatures, which is possible by controlling the quenching rate and aging temperature. Damping ability is one of the distinct advantages of these Cu-based SMAs. Too many applications, one of the most common applications of shape memory alloys is in the aviation field specifically airplanes such as the Airbus A320 flaps [16], the drones, and the UMAV since the Cu–Al–Ni shape memory alloys are more economical than other types, another application is the seismic field, and since the devastating earthquakes in Syria and Turkey, the need for safety systems for old buildings is in high demand. Cu-based SMAs are the best choice for non-medical applications such as these systems because they have a low cost compared to other shape memory alloys such as nitinol or iron-based shape memory alloys [17]. So an anti-earthquake system is required for the safety of old and historic buildings, and Cu-based shape memory alloys are the best choice. This article aims to study the effect of low-temperature heat treatment (250, 300, and 350°C) on mechanical properties by applying compression, hardness tests, and shape memory properties for the Al–Cu–Ni SMA.

2 Experimental work

2.1 Melting and casting

The materials were pure Cu wires (99.99%), Al foils (99.99%), and Ni powder (99.99%), and the metals were melted and cast at 1,200°C in an induction furnace under argon atmosphere, then homogenized at 950°C for 30 min, and quenched in ice brine solution; the alloys were subjected to a heat treatment performed at 250, 300, and 350°C for 4 h and then air-cooled in carbonate furnace as shown in Figure 1.

Figure 1 
                  Heat treatment furnace.
Figure 1

Heat treatment furnace.

2.2 Machining

They machined in an EDM wire cutting machine into two ASTM-E9 specimens 1.4 mm in diameter and 28 mm in height for compressive test and thermomechanical coupling with a tiny specimen for DSC and another for HV hardness of 14 mm diameter and 5 mm thickness, as shown in Figure 2. Also, specimens for SEM were ground and polished, and etching was performed with (FeCl3·6H2O + HCl + Methanol) for 4 min. The alloys were then categorized depending on treatment as shown in Table 1.

Figure 2 
                  (a) SEM and (b) compression specimens.
Figure 2

(a) SEM and (b) compression specimens.

Table 1

Categorization of the alloys

Alloy Temp. (°C)
A1
A2 250
A3 300
A4 350

2.3 Chemical composition and X-ray diffraction

Chemical composition was performed by an AVE 3000 data analyzer. Table 2 shows the results of the analysis. Also, the XRD test was performed by a Shmidzoo 6,000 device as shown in Figure 3.

Table 2

The result of the chemical analysis

Element Cu Al Ni
Percentage (%) Rem 14 4.5
Figure 3 
                  X-ray diffraction device.
Figure 3

X-ray diffraction device.

2.4 Mechanical properties

The alloys were put into compression test in Instron 9000, as shown in Figure 4, and a thermomechanical test was applied with limit loading and unloading compression test up to 25 KN by a Larray 800 device to calculate the recovery strain, as for Vickers hardness test shown in Figure 5.

Figure 4 
                  Instron 9000 device.
Figure 4

Instron 9000 device.

Figure 5 
                  Hardness device.
Figure 5

Hardness device.

2.5 Diffraction scanning calorimetry

The Diffraction Scanning Calorimetry test was performed by the SATRAN Labsys 300 device as shown in Figure 6. It operates on a range of 700 to −170°C and 25–350°C in both exothermic and endothermic directions.

Figure 6 
                  DSC device.
Figure 6

DSC device.

3 Results and discussion

The results section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.

3.1 Shape memory properties

3.1.1 DSC thermogram

Table 2 shows the results of the DSC thermogram of the alloys, with the calculation of equilibrium temperature (T°) from equation (1) [18], Spread (S) from equation (2), and Hysteresis (H) from equation (3) [19]; these equations are necessary to observe the effectiveness of the transformation process.

(1) T = A f + M s 2 ,

(2) S = A f A s ,

(3) H = A s M f ,

A f is the austenite finish temperature (°C), A s is the austenite start temperature (°C), M s is the martensite start temperature (°C), and M f is the martensite finish temperature (°C)

Table 3 shows the results of the DSC Thermogram of the alloys, with the calculation of equilibrium Temperature from equation (1), Spread from equation (2), and Hysteresis from Heat flow (μV) (equation (3)). There is a shift in the transformation temperature, and it is found that the transformation temperature had risen as shown in Table 3.

Table 3

Transformation temperatures for the alloys

Alloy A s (°C) A f (°C) M s (°C) M f (°C) T° H S
A1 129 165 157 100 179 29 36
A2 135 180 123 86 178 49 45
A3 241 301 143 89 286 152 60
A4 246 279 115 89 291 157 33

Also, Figure 7(a–d) shows the thermograms of alloys, and there is a shift in the transformation temperatures to reach outside the domain with a rise in transformation temperature in the same case [9] due to enthalpy changes. Niven Balo and Neslihan [12] attributed that to short aging time and it affected the hysteresis; also, it is caused by the bigger transformation in the martensite phase, and the grain size was shorter and finer, and more austenite phase formation with a very high increase in equilibrium temperature

Figure 7 
                     DSC thermograms for SMA: (a) base alloy, (b) aged at 250°C, (c) aged at 300°C, and (d) aged at 350°C.
Figure 7

DSC thermograms for SMA: (a) base alloy, (b) aged at 250°C, (c) aged at 300°C, and (d) aged at 350°C.

3.1.2 Recovery strain

Recovery strain is the strain at the end point of the unloading stress–strain diagram for shape memory alloys calculated and shows how elastic the alloy is [19].

(4) = l l l % .

Figure 8 shows the recovery strain for the alloys; there is a decrease in the recovery stain with the rise of aging temperature due to the increase of the austenite phase over the martensite phase as Abbass et al. [5] found after thermomechanical treatment. The same result was found by Dawood and Adnan [20] with more austenite phase after a thermomechanical treatment with less load, compared with adding alloying element shape memory properties which was better than the work of Adnan et al. [3,4] by adding Sn and Ce.

Figure 8 
                     Stress–strain diagram of recovery strain for the alloys.
Figure 8

Stress–strain diagram of recovery strain for the alloys.

Table 4 shows the recovery strain for the alloys; there is a decrease in the recovery stain with the rise of aging temperature due to the increase of the austenite phase over the martensite phase as Abbas et al. [5] found after thermomechanical treatment. Also, the shape memory effect increased to a near super elastic limit, and the Modulus of elasticity for martensite (E M) and austenite (E A) can be calculated as suggested by Lagoudas [21] by taking the slope of a tangent in both martensite and austenite directions; the result showed that the modulus of elasticity was less than what Abbass et al. [5] found when they used thermomechanical treatment due to the increase in the austenite phase was less than this work.

Table 4

Shape memory properties for the alloys

Alloy ε Recovery (%) SME (%) E M (GPa) E A (GPa)
A1 1.39 97 149.54 21.90
A2 0.67 97.5 15.4 3.91
A3 0.5 98.5 2.08 8.34
A4 0.39 99 1.07 5.52

3.2 Mechanical properties

Figure 9 shows the stress–strain of the alloys, and there is an increase in yield stress and maximum compression test with a rise of aging temperature with an increase in strain and a decrease in hardness; this is due to the increase in austenite phase, and also, the short time this was found also in the works by Ivanić et al. [13]; there was also an increase in yield stress and maximum compression stress which indicates the improvement in mechanical properties with rising temperature and the reduction of martensite due to dislocation. Compared with adding alloying elements, mechanical properties were better than those of the work of Adnan et al. [3,4] by adding Sn and Ce.

Figure 9 
                  Stress–strain diagram for the shape memory alloys.
Figure 9

Stress–strain diagram for the shape memory alloys.

Table 5 shows the mechanical properties of the alloys. There is a decrease in Hardness for The Alloys. The effect of time is found also in the study by Sampath [22] and Suresh and Ramamurty [9] with the same treatment time which can be attributed to the fine grain size. Also, since the austenite phase is less hard than martensite and since it is the predominant phase, this behavior of SMA is found in the study by Abbass et al. [5] but hardness results of thermomechanical treatment are less than aging and annealing by Canbay et al. [6] but in the same behavior martensite is a hard phase, comparing the results with the addition of elements in the work of Adnan et al. [3,4].

Table 5

Mechanical properties for the SMAs

Alloy σ y (MPa) σ Max (N/mm2) ε max (%) HV
A1 430 768 12.5 363
A2 738 911 12.64 286
A3 747 939 17.84 253
A4 753 966 20.83 218

3.3 Microstructure

Figure 7(a–d) shows the microstructure of the shape memory alloys. Figure 10(a) shows the base alloy both the martensite phases are in two forms a needle shape (β′) and (γ′) stack shape form in pairs or two pairs as was found in Figure 2(a–d); the martensite base had been decreased with the austenite phase being the predominant phase with the sign of oxidation due to air-penetrated furnace atmosphere.

Figure 10 
                  SEM images for SMA: (a) base alloy, (b) aged at 250°C, (c) aged at 300°C, and (d) aged at 350°C.
Figure 10

SEM images for SMA: (a) base alloy, (b) aged at 250°C, (c) aged at 300°C, and (d) aged at 350°C.

The fine grain size gives an improvement in mechanical properties with small precipitations which can affect martensite and the stacks (ϒ′) are shorter as is the needle shape (β′) phase and affine grains which can improve mechanical properties. There is an oxidation effect is also found in the microstructure in the aged alloys, austenite (β) is found in the aged alloys due to the effect of heat treatment and is the more dominant phase with rising aging temperature; this behavior was also found by Abbass et al. [5] and Dawood and Adnan [20] when they used thermo mechanical treatment.

3.4 X-Ray diffraction

Figure 11 shows the X-ray Diffraction test. The results show the main phases in the Cu-based shape memory alloy as Table 6 shows the formation of the Phases of the alloy the martensite β′ and γ′ in their respective angles

Figure 11 
                  XRD results for the base alloys.
Figure 11

XRD results for the base alloys.

Table 6

XRD phases for base alloys

Alloy 2θ° dm A d°A I/I° (%) Symbol
AlCu3 44.808 1.998 2.021 100
Al19Cu23Ni 46.609 1.947 1.9307 80
Al7Cu23Ni 42.707 2.088 2.114 65

4 Conclusions

From the obtained results it can be concluded that:

  1. Mechanical properties have improved which is because of the reduction in the martensite phase and the increase in austenite phase growth that causes an increase in yield stress and maximum compression stress of the alloys.

  2. Heat treatment has reduced hardness because of the growth of the austenite phase and reduction of the martensite phase better than thermomechanical treatment and the addition of some alloying elements.

  3. Heat treatment has also caused a rise in transformation temperature beyond the limits of the domain of the Cu–Al–Ni shape memory alloys with an increase in spread and hysteresis which is better than thermomechanical treatment.

  4. Recovery strain had been decreased with rising aging temperature and the growth of Martensite with better than alloying elements and thermomechanical treatment.

  5. The short time and low temperature of heat treatment reduced the Martensite phase due to dislocation and precipitation.

Acknowledgments

Many thanks to University of Technology – Department of Materials Engineering – Iraq, Laboratory of Strength of Materials, and DSC Lab.

  1. Conflict of interest: The authors declare that there are no conflicts of interest.

  2. Data availability statement: All data are fully available without restriction.

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Received: 2023-06-20
Revised: 2024-02-25
Accepted: 2024-03-08
Published Online: 2024-04-08

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  113. An image encryption method based on modified elliptic curve Diffie-Hellman key exchange protocol and Hill Cipher
  114. Experimental investigation of generating superheated steam using a parabolic dish with a cylindrical cavity receiver: A case study
  115. Effect of surface roughness on the interface behavior of clayey soils
  116. Investigated of the optical properties for SiO2 by using Lorentz model
  117. Measurements of induced vibrations due to steel pipe pile driving in Al-Fao soil: Effect of partial end closure
  118. Experimental and numerical studies of ballistic resistance of hybrid sandwich composite body armor
  119. Evaluation of clay layer presence on shallow foundation settlement in dry sand under an earthquake
  120. Optimal design of mechanical performances of asphalt mixtures comprising nano-clay additives
  121. Advancing seismic performance: Isolators, TMDs, and multi-level strategies in reinforced concrete buildings
  122. Predicted evaporation in Basrah using artificial neural networks
  123. Energy management system for a small town to enhance quality of life
  124. Numerical study on entropy minimization in pipes with helical airfoil and CuO nanoparticle integration
  125. Equations and methodologies of inlet drainage system discharge coefficients: A review
  126. Thermal buckling analysis for hybrid and composite laminated plate by using new displacement function
  127. Investigation into the mechanical and thermal properties of lightweight mortar using commercial beads or recycled expanded polystyrene
  128. Experimental and theoretical analysis of single-jet column and concrete column using double-jet grouting technique applied at Al-Rashdia site
  129. The impact of incorporating waste materials on the mechanical and physical characteristics of tile adhesive materials
  130. Seismic resilience: Innovations in structural engineering for earthquake-prone areas
  131. Automatic human identification using fingerprint images based on Gabor filter and SIFT features fusion
  132. Performance of GRKM-method for solving classes of ordinary and partial differential equations of sixth-orders
  133. Visible light-boosted photodegradation activity of Ag–AgVO3/Zn0.5Mn0.5Fe2O4 supported heterojunctions for effective degradation of organic contaminates
  134. Production of sustainable concrete with treated cement kiln dust and iron slag waste aggregate
  135. Key effects on the structural behavior of fiber-reinforced lightweight concrete-ribbed slabs: A review
  136. A comparative analysis of the energy dissipation efficiency of various piano key weir types
  137. Special Issue: Transport 2022 - Part II
  138. Variability in road surface temperature in urban road network – A case study making use of mobile measurements
  139. Special Issue: BCEE5-2023
  140. Evaluation of reclaimed asphalt mixtures rejuvenated with waste engine oil to resist rutting deformation
  141. Assessment of potential resistance to moisture damage and fatigue cracks of asphalt mixture modified with ground granulated blast furnace slag
  142. Investigating seismic response in adjacent structures: A study on the impact of buildings’ orientation and distance considering soil–structure interaction
  143. Improvement of porosity of mortar using polyethylene glycol pre-polymer-impregnated mortar
  144. Three-dimensional analysis of steel beam-column bolted connections
  145. Assessment of agricultural drought in Iraq employing Landsat and MODIS imagery
  146. Performance evaluation of grouted porous asphalt concrete
  147. Optimization of local modified metakaolin-based geopolymer concrete by Taguchi method
  148. Effect of waste tire products on some characteristics of roller-compacted concrete
  149. Studying the lateral displacement of retaining wall supporting sandy soil under dynamic loads
  150. Seismic performance evaluation of concrete buttress dram (Dynamic linear analysis)
  151. Behavior of soil reinforced with micropiles
  152. Possibility of production high strength lightweight concrete containing organic waste aggregate and recycled steel fibers
  153. An investigation of self-sensing and mechanical properties of smart engineered cementitious composites reinforced with functional materials
  154. Forecasting changes in precipitation and temperatures of a regional watershed in Northern Iraq using LARS-WG model
  155. Experimental investigation of dynamic soil properties for modeling energy-absorbing layers
  156. Numerical investigation of the effect of longitudinal steel reinforcement ratio on the ductility of concrete beams
  157. An experimental study on the tensile properties of reinforced asphalt pavement
  158. Self-sensing behavior of hot asphalt mixture with steel fiber-based additive
  159. Behavior of ultra-high-performance concrete deep beams reinforced by basalt fibers
  160. Optimizing asphalt binder performance with various PET types
  161. Investigation of the hydraulic characteristics and homogeneity of the microstructure of the air voids in the sustainable rigid pavement
  162. Enhanced biogas production from municipal solid waste via digestion with cow manure: A case study
  163. Special Issue: AESMT-7 - Part I
  164. Preparation and investigation of cobalt nanoparticles by laser ablation: Structure, linear, and nonlinear optical properties
  165. Seismic analysis of RC building with plan irregularity in Baghdad/Iraq to obtain the optimal behavior
  166. The effect of urban environment on large-scale path loss model’s main parameters for mmWave 5G mobile network in Iraq
  167. Formatting a questionnaire for the quality control of river bank roads
  168. Vibration suppression of smart composite beam using model predictive controller
  169. Machine learning-based compressive strength estimation in nanomaterial-modified lightweight concrete
  170. In-depth analysis of critical factors affecting Iraqi construction projects performance
  171. Behavior of container berth structure under the influence of environmental and operational loads
  172. Energy absorption and impact response of ballistic resistance laminate
  173. Effect of water-absorbent polymer balls in internal curing on punching shear behavior of bubble slabs
  174. Effect of surface roughness on interface shear strength parameters of sandy soils
  175. Evaluating the interaction for embedded H-steel section in normal concrete under monotonic and repeated loads
  176. Estimation of the settlement of pile head using ANN and multivariate linear regression based on the results of load transfer method
  177. Enhancing communication: Deep learning for Arabic sign language translation
  178. A review of recent studies of both heat pipe and evaporative cooling in passive heat recovery
  179. Effect of nano-silica on the mechanical properties of LWC
  180. An experimental study of some mechanical properties and absorption for polymer-modified cement mortar modified with superplasticizer
  181. Digital beamforming enhancement with LSTM-based deep learning for millimeter wave transmission
  182. Developing an efficient planning process for heritage buildings maintenance in Iraq
  183. Design and optimization of two-stage controller for three-phase multi-converter/multi-machine electric vehicle
  184. Evaluation of microstructure and mechanical properties of Al1050/Al2O3/Gr composite processed by forming operation ECAP
  185. Calculations of mass stopping power and range of protons in organic compounds (CH3OH, CH2O, and CO2) at energy range of 0.01–1,000 MeV
  186. Investigation of in vitro behavior of composite coating hydroxyapatite-nano silver on 316L stainless steel substrate by electrophoretic technic for biomedical tools
  187. A review: Enhancing tribological properties of journal bearings composite materials
  188. Improvements in the randomness and security of digital currency using the photon sponge hash function through Maiorana–McFarland S-box replacement
  189. Design a new scheme for image security using a deep learning technique of hierarchical parameters
  190. Special Issue: ICES 2023
  191. Comparative geotechnical analysis for ultimate bearing capacity of precast concrete piles using cone resistance measurements
  192. Visualizing sustainable rainwater harvesting: A case study of Karbala Province
  193. Geogrid reinforcement for improving bearing capacity and stability of square foundations
  194. Evaluation of the effluent concentrations of Karbala wastewater treatment plant using reliability analysis
  195. Adsorbent made with inexpensive, local resources
  196. Effect of drain pipes on seepage and slope stability through a zoned earth dam
  197. Sediment accumulation in an 8 inch sewer pipe for a sample of various particles obtained from the streets of Karbala city, Iraq
  198. Special Issue: IETAS 2024 - Part I
  199. Analyzing the impact of transfer learning on explanation accuracy in deep learning-based ECG recognition systems
  200. Effect of scale factor on the dynamic response of frame foundations
  201. Improving multi-object detection and tracking with deep learning, DeepSORT, and frame cancellation techniques
  202. The impact of using prestressed CFRP bars on the development of flexural strength
  203. Assessment of surface hardness and impact strength of denture base resins reinforced with silver–titanium dioxide and silver–zirconium dioxide nanoparticles: In vitro study
  204. A data augmentation approach to enhance breast cancer detection using generative adversarial and artificial neural networks
  205. Modification of the 5D Lorenz chaotic map with fuzzy numbers for video encryption in cloud computing
  206. Special Issue: 51st KKBN - Part I
  207. Evaluation of static bending caused damage of glass-fiber composite structure using terahertz inspection
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