Effect of water entry temperature and aging parameters on precipitation and mechanical properties of 7075 aluminum alloy

This paper proposes a non-isothermal solid solution-forging integrated hot forming process for 7075 aluminum alloy. After solid solution treatment， the aluminum alloy is directly placed into the mold for forging， then quenched and subjected to artificial aging treatment. The influence of water entry temperature and aging parameters on the microstructure and properties of 7075 aluminum alloy is studied under this process， through the construction of a temperature-time-property（TTP） curve. Additionally， machine learning techniques are integrated to optimize and match the key process parameters. The results reveal that the nose temperature of the TTP curve is 315 ℃， and the mechanical properties of the alloy increase with the increase of water temperature after aging， a double-peak phenomenon after non-isothermal forging and aging is observed. When the inlet temperature is 380 ℃， the optimal aging parameters are 115 ℃-26 h and the peak hardness is 182HV. After training， the prediction accuracy of the BP neural network model is 94.9977%. Experimental verification of the optimal process parameters predicted by the model shows that its prediction similarity is 96.9%. Compared with traditional forging processes， this process can achieve high mechanical properties than traditional forged T6-state 7075 aluminum alloy while reducing procedural steps and energy consumption.