The tilt pour gravity casting process coupled with the Controlled Diffusion Solidification (CDS) process technology was employed to demonstrate the ability to cast AA7050 wrought alloy into high integrity near net shaped components with high strength and ductility. The CDS technology involves mixing two precursor alloys at different thermal mass and subsequently casting the resultant mixture into near net shaped cast components. The process enables casting of the high performance Al wrought alloys into near net shaped components by circumventing the problem of hot tearing by obtaining a non-dendritic morphology of the primary Al phase. This study presents the process and alloy parameters necessary for the casting of 7050 Al wrought alloy (Al-Zn-Mg-Cu) using the CDS process technology. The uniaxial tensile properties after various heat treatment conditions such as as-cast, solutionizing and annealing mandatory to the development of the artificial ageing were investigated and presented along with in-depth and quantitative microstructural analyses.

Controlled Diffusion Solidification (CDS) of Al-Zn-Mg-Cu (7050): Microstructure, heat treatment and mechanical properties

This paper describes a heat treatment process using Taguchi’s robust design method to determine the optimal heat treatment conditions for Al-Mg-Si wrought alloys, which is an alloy used in automotive body panels. Important factors influencing the optimization criteria include solution temperature, solution time, pre-aging temperature, and pre-aging time. The optimal heat-treatment conditions for both microhardness and paint-bake response of the Al-Mg-Si wrought alloys are a solution temperature of 550°C, solution time of 30 min, pre-aging temperature of 90°C, and pre-aging time of 60 min. The optimal conditions for tensile strength are a solution temperature of 530°C, solution time of 60 min, pre-aging temperature of 90°C, and pre-aging time of 30 min. Experimental results indicate that solution temperature is the most significant factor for both microhardness and alloy elongation, solution time is the important factor for yield-strength, and pre-aging temperature is the most significant factor for paint-bake response and ultimate tensile strength.

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Optimization of the Heat Treatment Parameters for Al-Mg-Si Wrought Alloys Using Taguchi Approach

The Controlled Diffusion Solidification (CDS) is an innovative casting technology that is being successfully employed in the near net shaped casting of Al based wrought alloys; specifically the 2xxx and 7xxx series. The novelty of the CDS technology is that it could mitigate the notable casting defect in conventional solidification of the Al wrought alloys, namely, hot tearing and enables the alloy to be cast into near net shaped components. This publication presents an overview of the phenomenological mechanism that lead to the non-dendritic morphology of the primary Al phase in the as-cast microstructure that facilitates a viable opportunity to cast the wrought alloys into high integrity and structural near net shaped castings. The salient mechanical properties of the cast component with AA7050 alloy in various heat treatment tempers will be presented along with micro structural characterization. Additionally, potential application for the technology to be adapted to various Al shaped casting processes will be explored and presented.

Control Diffusion Solidification (CDS): An Overview of Mechanism and Application

Effect of heat treatment on mechanical properties of forged aluminium alloy AA2219

Post weld heat treatment, or PWHT, is often used to improve the mechanical properties of materials that have been welded. Several publications have investigated the effects of the PWHT process using experimental designs. However, the modeling and optimization using the integration of machine learning (ML) and metaheuristics have yet to be reported, which are fundamental steps toward intelligent manufacturing applications. This research proposes a novel approach using ML techniques and metaheuristics to optimize PWHT process parameters. The goal is to determine the optimal PWHT parameters for both single and multiple objective perspectives. In this research, support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), and random forest (RF) were ML techniques employed to obtain a relationship model between PWHT parameters and mechanical properties: ultimate tensile strength (UTS) and elongation percentage (EL). The results show that the SVR demonstrated superior performance among ML techniques for both UTS and EL models. Then, SVR is used with metaheuristics such as differential evolution (DE), particle swarm optimization (PSO), and genetic algorithms (GA). SVR-PSO shows the fastest convergence among other combinations. The final solutions of single-objective and Pareto solutions were also suggested in this research.

https://www.mdpi.com/1996-1944/16/5/2081/pdf?version=1677837879

Chin Chun Chang

Papers

Optimization of Heat Treatment Parameters with the Taguchi Method for the A7050 Aluminum Alloy