Precipitation strengthening mechanisms during natural ageing and subsequent artificial aging in an Al-Mg-Si-Cu alloy

Jin, Shuoxun; Ngai, Tungwai Leo; Zhang, Gongwang; Zhai, Tianrui; Jia, Shiru; Li, Liejun

doi:10.1016/j.msea.2018.03.006

Cited by 43 publications

(15 citation statements)

References 31 publications

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“…Consequently, the best comprehensive mechanical properties were obtained at the condition of 160–175 °C. Increasing the aging temperature accelerated the aging process since the diffusion rate of Si and Mg atoms was improved; thus, the strengthening effect was enhanced [35].…”

Section: Resultsmentioning

confidence: 99%

Quantitative Relationship Analysis of Mechanical Properties with Mg Content and Heat Treatment Parameters in Al–7Si Alloys Using Artificial Neural Network

Zhang

Haiyang

et al. 2019

Materials

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In this paper, an artificial neural network (ANN) model with high accuracy and good generalization ability was developed to predict and optimize the mechanical properties of Al–7Si alloys. The quantitative correlation formulas of the mechanical properties with Mg content and heat treatment parameters were established based on the transfer function and weight values. The relative importance of the input variables, Mg content and heat treatment parameters, on the mechanical properties of Al–7Si alloys were identified through sensitivity analysis. The results indicated that the mechanical properties of Al–7Si alloys were sensitive to Mg content and aging temperature. Then the individual and the combined influences of these input variables on the properties of Al–7Si alloys were simulated and the process parameters were optimized using the artificial neural network model. Finally, the proposed model was validated to be a robust tool in predicting the mechanical properties of the Al–7Si alloy by conducting experiments.

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Section: Resultsmentioning

confidence: 99%

Quantitative Relationship Analysis of Mechanical Properties with Mg Content and Heat Treatment Parameters in Al–7Si Alloys Using Artificial Neural Network

Zhang

Haiyang

et al. 2019

Materials

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“…This led to a decrease in the density and size of β″ precipitates after a paint bake-hardening treatment [36,40]. Compared with β′ phase, the strengthening effect of β″ precipitates was more remarkable [5,41]. Therefore, the strength and hardness increment decreased with decreasing quenching rate.…”

Section: Discussionmentioning

confidence: 99%

Effects of the Quenching Rate on the Microstructure, Mechanical Properties and Paint Bake-Hardening Response of Al–Mg–Si Automotive Sheets

Gao

Wang

et al. 2019

Materials

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The quenching rate of Al–Mg–Si alloys during solution treatment is an important parameter for the automotive industry. In this work, the effect of the different quenching rates on the microstructures, mechanical properties, and paint bake-hardening response of Al–Mg–Si sheets was studied. Large dimples form on the fracture surface of a sample at a quenching rate of 0.01 °C/s. When the quenching rate increased to 58.9 °C/s, the dimples became smaller. The recrystallized grains and textures were slightly affected by quenching rates beyond 1.9 °C/s. Thus, higher r values of the samples were achieved with slower quenching rates. Furthermore, only the Al(FeMn)SiCr insoluble phases were observed in samples with a rapid quenching rate. Sufficient solute atoms and vacancies resulted in the improvement of the precipitation kinetics and paint bake-hardening capacity for Al–Mg–Si sheets at rapid rates. With a decrease in the quenching rate, the formation of the rod-like coarse β′ phases consumed many solute atoms and vacancies, leading to the deterioration of the paint bake-hardening capacity. This study provides a critical reference on quenching rates for industrial practices, so that good mechanical properties can be achieved using precision control of the quenching process.

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“…Precipitation and solid solution strengthening mechanisms contribute to the main strength of these alloys, which are largely determined by the solid solution and aging heat treatment processes. Abundant research work has been carried out regarding to the influences of the heat treatment route on the strengthening of the Al-Mg-Si-(Cu) aluminum alloys [4,5]. The heating during the processing and service processes of these alloys, intended or un-excepted, would also influence their strength.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Performance Evaluation of the Al-Mg-Si-(Cu) Aluminum Alloys after Transient Thermal Shock through an Novel Equivalent Structure Design and Finite Element Modeling

Liu

et al. 2020

Metals

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In this paper, the microstructure evolution and mechanical performance of the Al-Mg-Si-(Cu) aluminum alloy after transient thermal shock were investigated through experimental tests and finite element simulations. A novel equivalent structure was designed as a typical case in which one side of the plate was welded therefore the other side was thermally shocked with different temperature distribution and duration. The temperature gradient which influences most importantly the mechanical properties was simulated and experimentally verified. Through cutting layers and tensile testing, the mechanical response and material constitutive relation were obtained for each layer. Gurson-Tvergaard-Needlemen (GTN) damage parameters of these samples under large strains were then obtained by the Swift law inverse analysis approach. By sorting the whole welded joint into multi-material composed structure and introducing the obtained material constitutive relation and damage parameters, tensile properties were precisely predicted for typical types of weld joint such as butt, corner, and lap joints. The results show that precipitate coarsening, phase transformation from β″ phase to Q′ phase, and dissolving in the temperature range of 243.3–466.3 °C during the thermal shock induced a serious deterioration of the mechanical properties. The highest reduction of the ultimate tensile strength (UTS) and yield strength (YS) would be 38.6% and 57.4% respectively. By comparing the simulated and experimentally obtained force-displacement curves, the error for the above prediction method was evaluated to be less than 8.1%, indicating the proposed method being effective and reliable.

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Precipitation strengthening mechanisms during natural ageing and subsequent artificial aging in an Al-Mg-Si-Cu alloy

Cited by 43 publications

References 31 publications

Quantitative Relationship Analysis of Mechanical Properties with Mg Content and Heat Treatment Parameters in Al–7Si Alloys Using Artificial Neural Network

Quantitative Relationship Analysis of Mechanical Properties with Mg Content and Heat Treatment Parameters in Al–7Si Alloys Using Artificial Neural Network

Effects of the Quenching Rate on the Microstructure, Mechanical Properties and Paint Bake-Hardening Response of Al–Mg–Si Automotive Sheets

Mechanical Performance Evaluation of the Al-Mg-Si-(Cu) Aluminum Alloys after Transient Thermal Shock through an Novel Equivalent Structure Design and Finite Element Modeling

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