Sasidhar Gurugubelli, V. Chintada, Rama Bhadri Raju Chekuri
{"title":"用有限元和人工智能方法研究5000系列铝材的力学性能","authors":"Sasidhar Gurugubelli, V. Chintada, Rama Bhadri Raju Chekuri","doi":"10.4273/ijvss.15.2.07","DOIUrl":null,"url":null,"abstract":"The analysis of experimental and simulation findings of an aluminium alloy using finite element and artificial intelligence approaches is compared in this paper. The magnesium content in aluminium-magnesium alloys ranges from 0.5 to 13%, with other elements added in lower amounts. The purpose of this work is to manufacture aluminium alloy in a well-equipped furnace under the proper thermal circumstances to reach the required temperature and then to sand cast aluminium alloy (5000 series) to acquire the desired specimen. The specimens were made using varying quantities of aluminium 88% and magnesium 12% by weight fraction. When it comes to strength testing, the most common one is the static tensile test, which was computer-simulated in this work using Python and the design tool Ansys. Results from the computer simulations were compared to those from the tensile tests and they were determined to be equivalent. In spite of this, computer simulations and artificial intelligence are feasible alternatives to time-consuming and costly laboratory experiments.","PeriodicalId":14391,"journal":{"name":"International Journal of Vehicle Structures and Systems","volume":"6 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Investigation on Mechanical Properties of Fabricated Aluminium 5000 Series using Finite Element and Artificial Intelligence Methods\",\"authors\":\"Sasidhar Gurugubelli, V. Chintada, Rama Bhadri Raju Chekuri\",\"doi\":\"10.4273/ijvss.15.2.07\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The analysis of experimental and simulation findings of an aluminium alloy using finite element and artificial intelligence approaches is compared in this paper. The magnesium content in aluminium-magnesium alloys ranges from 0.5 to 13%, with other elements added in lower amounts. The purpose of this work is to manufacture aluminium alloy in a well-equipped furnace under the proper thermal circumstances to reach the required temperature and then to sand cast aluminium alloy (5000 series) to acquire the desired specimen. The specimens were made using varying quantities of aluminium 88% and magnesium 12% by weight fraction. When it comes to strength testing, the most common one is the static tensile test, which was computer-simulated in this work using Python and the design tool Ansys. Results from the computer simulations were compared to those from the tensile tests and they were determined to be equivalent. In spite of this, computer simulations and artificial intelligence are feasible alternatives to time-consuming and costly laboratory experiments.\",\"PeriodicalId\":14391,\"journal\":{\"name\":\"International Journal of Vehicle Structures and Systems\",\"volume\":\"6 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Vehicle Structures and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4273/ijvss.15.2.07\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Vehicle Structures and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4273/ijvss.15.2.07","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
Investigation on Mechanical Properties of Fabricated Aluminium 5000 Series using Finite Element and Artificial Intelligence Methods
The analysis of experimental and simulation findings of an aluminium alloy using finite element and artificial intelligence approaches is compared in this paper. The magnesium content in aluminium-magnesium alloys ranges from 0.5 to 13%, with other elements added in lower amounts. The purpose of this work is to manufacture aluminium alloy in a well-equipped furnace under the proper thermal circumstances to reach the required temperature and then to sand cast aluminium alloy (5000 series) to acquire the desired specimen. The specimens were made using varying quantities of aluminium 88% and magnesium 12% by weight fraction. When it comes to strength testing, the most common one is the static tensile test, which was computer-simulated in this work using Python and the design tool Ansys. Results from the computer simulations were compared to those from the tensile tests and they were determined to be equivalent. In spite of this, computer simulations and artificial intelligence are feasible alternatives to time-consuming and costly laboratory experiments.
期刊介绍:
The International Journal of Vehicle Structures and Systems (IJVSS) is a quarterly journal and is published by MechAero Foundation for Technical Research and Education Excellence (MAFTREE), based in Chennai, India. MAFTREE is engaged in promoting the advancement of technical research and education in the field of mechanical, aerospace, automotive and its related branches of engineering, science, and technology. IJVSS disseminates high quality original research and review papers, case studies, technical notes and book reviews. All published papers in this journal will have undergone rigorous peer review. IJVSS was founded in 2009. IJVSS is available in Print (ISSN 0975-3060) and Online (ISSN 0975-3540) versions. The prime focus of the IJVSS is given to the subjects of modelling, analysis, design, simulation, optimization and testing of structures and systems of the following: 1. Automotive vehicle including scooter, auto, car, motor sport and racing vehicles, 2. Truck, trailer and heavy vehicles for road transport, 3. Rail, bus, tram, emerging transit and hybrid vehicle, 4. Terrain vehicle, armoured vehicle, construction vehicle and Unmanned Ground Vehicle, 5. Aircraft, launch vehicle, missile, airship, spacecraft, space exploration vehicle, 6. Unmanned Aerial Vehicle, Micro Aerial Vehicle, 7. Marine vehicle, ship and yachts and under water vehicles.