{"title":"热成形-淬火和热处理工艺对 AA6016 铝合金板材机械性能的影响","authors":"Jiahong Lu, Baitong Liu, Shiyao Huang, Zuguo Bao, Yutong Yang, Xilin Li, Zhenfei Zhan, Qing Liu","doi":"10.3390/met14050599","DOIUrl":null,"url":null,"abstract":"This study explored the impact of Hot Forming–Quenching (HFQ) and heat treatment processes on the mechanical properties of AA6016 sheets. The experimental findings demonstrated that at high-temperature pre-straining (HT-PS) of 15%, the strength performance of the AA6016 sheet exhibited enhancement, with a progressive increase in both the heat treatment temperature and duration. Conversely, under HT-PS conditions of 3% and 7%, the heat treatment process exhibited a relatively modest impact on the mechanical properties of the AA6016 sheet. Differential scanning calorimetry (DSC) was employed to understand the influence of different process conditions on the precipitated phases. By comparing the precipitation peaks of the β″ phase at HT-PS of 3% and 15%, it was observed that the precipitation peak of the β″ phase decreased with an increase in HT-PS. This indicated that HT-PS promoted the precipitation of the β″ phase. In order to forecast the mechanical performance of the AA6016 sheets after applying various pre-straining and heat treatment parameters, two models were used: a backpropagation (BP) neural network and a genetic algorithm (GA)-BP neural network. These models were evaluated for their fitting and predictive capabilities. The research findings demonstrated that the GA-BP neural network model exhibited superior fitting and predictive accuracy compared to the BP neural network model.","PeriodicalId":510812,"journal":{"name":"Metals","volume":"85 14","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Effect of Hot Forming–Quenching and Heat Treatment Processes on the Mechanical Properties of AA6016 Aluminum Alloy Sheets\",\"authors\":\"Jiahong Lu, Baitong Liu, Shiyao Huang, Zuguo Bao, Yutong Yang, Xilin Li, Zhenfei Zhan, Qing Liu\",\"doi\":\"10.3390/met14050599\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study explored the impact of Hot Forming–Quenching (HFQ) and heat treatment processes on the mechanical properties of AA6016 sheets. The experimental findings demonstrated that at high-temperature pre-straining (HT-PS) of 15%, the strength performance of the AA6016 sheet exhibited enhancement, with a progressive increase in both the heat treatment temperature and duration. Conversely, under HT-PS conditions of 3% and 7%, the heat treatment process exhibited a relatively modest impact on the mechanical properties of the AA6016 sheet. Differential scanning calorimetry (DSC) was employed to understand the influence of different process conditions on the precipitated phases. By comparing the precipitation peaks of the β″ phase at HT-PS of 3% and 15%, it was observed that the precipitation peak of the β″ phase decreased with an increase in HT-PS. This indicated that HT-PS promoted the precipitation of the β″ phase. In order to forecast the mechanical performance of the AA6016 sheets after applying various pre-straining and heat treatment parameters, two models were used: a backpropagation (BP) neural network and a genetic algorithm (GA)-BP neural network. These models were evaluated for their fitting and predictive capabilities. The research findings demonstrated that the GA-BP neural network model exhibited superior fitting and predictive accuracy compared to the BP neural network model.\",\"PeriodicalId\":510812,\"journal\":{\"name\":\"Metals\",\"volume\":\"85 14\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Metals\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/met14050599\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Metals","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/met14050599","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Effect of Hot Forming–Quenching and Heat Treatment Processes on the Mechanical Properties of AA6016 Aluminum Alloy Sheets
This study explored the impact of Hot Forming–Quenching (HFQ) and heat treatment processes on the mechanical properties of AA6016 sheets. The experimental findings demonstrated that at high-temperature pre-straining (HT-PS) of 15%, the strength performance of the AA6016 sheet exhibited enhancement, with a progressive increase in both the heat treatment temperature and duration. Conversely, under HT-PS conditions of 3% and 7%, the heat treatment process exhibited a relatively modest impact on the mechanical properties of the AA6016 sheet. Differential scanning calorimetry (DSC) was employed to understand the influence of different process conditions on the precipitated phases. By comparing the precipitation peaks of the β″ phase at HT-PS of 3% and 15%, it was observed that the precipitation peak of the β″ phase decreased with an increase in HT-PS. This indicated that HT-PS promoted the precipitation of the β″ phase. In order to forecast the mechanical performance of the AA6016 sheets after applying various pre-straining and heat treatment parameters, two models were used: a backpropagation (BP) neural network and a genetic algorithm (GA)-BP neural network. These models were evaluated for their fitting and predictive capabilities. The research findings demonstrated that the GA-BP neural network model exhibited superior fitting and predictive accuracy compared to the BP neural network model.