Energy absorption study of warm-rolled dual-phase LZ۷۱ magnesium alloy hollow tube using ANN

سال انتشار: 1399
محل انتشار: مجله علم مهندسی خودرو، دوره: 11، شماره: 1
کد COI اختصاصی: JR_IJAEIU-11-1_006
زبان مقاله: انگلیسی
تعداد مشاهده: 59

نویسندگان

School of Automotive Engineering, Iran University of Science and Technology, Tehran, Iran.

چکیده

In the present work, the energy absorption study of warm-rolled LZ۷۱ sheet is done for the first time. To do so, Lithium (۷% Wt), Zinc (۱% Wt) and Magnesium are cast in ۷۷۰⁰C. After that, the billet has been warm-rolled at ۳۵۰⁰C and its thickness reduced by ۸۰%. Then, two different heat treatment situations are studied to reach an isotropic plate. Afterward, microstructures of the specimens have been studied using an optical microscope. Tensile tests of the samples are derived to study the mechanical properties and isotropy of the sheets. Moreover, the results of tensile tests applied for crushing simulations. Energy absorption study of the alloy is also done using ABAQUS/Explicit commercial code. The results of simulations are validated using experimental tests of A۶۰۸۲ and completely acceptable performance of simulations is observed. Then, the mechanical properties of LZ۷۱ are used to study the crashworthiness behavior of the mentioned alloy. Crash absorption parameters, namely peak crush force (FMax), mean crush force (FMean), Total Energy Absorption (TAE), Crush Force Efficiency (CFE), Specific Energy Absorption (SEA) and Total Efficiency (TE) of LZ۷۱ and A۶۰۸۲ are compared which are shown that the performance of LZ۷۱ is considerably more efficient than A۶۰۸۲. Lastly, by the help of Artificial Neural Network (ANN) and Taguchi Method, the effects of dimensional parameters of tube, namely diameter, length and thickness, on FMax, FMean and TAE and also the influences of dimensionless geometrical ratios, namely L/D and D/t on CFE, SEA and TE are surveyed comprehensively.

کلیدواژه ها

Crashworthiness, Warm rolling, LZ۷۱, Magnesium Alloy, Crash simulation, Crash energy absorption, Artificial Neural Network

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