重型载重汽车用聚合物及复合材料的单调疲劳试验

Kevan W. F. Gahan, J. Parmigiani
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引用次数: 0

摘要

改进工程聚合物和复合材料的材料模型,包括单调和疲劳特性,对于创建更精确的重型卡车数字模拟是必要的。与卡车设计中常用的钢和其他合金不同,这些先进的聚合物材料没有预先存在的疲劳特性数据。此外,在一个研究项目中,没有一个单独的标准测试程序可以被普遍引用和遵循。这些材料存在于卡车的引擎盖、仪表板、车身面板和防溅罩中,并且在车辆的整个使用或“占空比”中受到不同幅度和持续时间的循环加载条件的影响。不同型号的卡车所施加的载荷不同,因为一些卡车将用于职业用途,而其他卡车将留在高速公路上。本文介绍了各向同性非增强和各向异性玻璃纤维增强聚合物的试验,以及随后描述其在各种载荷条件下行为所需的单调和疲劳性能的计算。材料特性是使用一系列恒幅应变控制疲劳试验来测量的,这些试验遵循ASTM D638(塑料拉伸性能的标准试验方法)、ASTM E606(应变控制疲劳试验的标准实践)方法和SAE J1099(铁和有色材料的低周疲劳性能技术报告)的标准实践。ASTM D638 1型试样几何形状用于所有材料,具有不同的样品厚度和长度。在所有测试过程中,采用轴向延伸计测量应变数据,在循环测试期间安装防屈曲夹具,以消除在完全反向波形的压缩部分中试样的任何弯曲。在材料卷的规长上还安装了横向延伸计,用于测量单调测试时的瞬时横截面积和泊松比。通过单调试验程序收集的数据用于计算杨氏模量、泊松比、极限抗拉强度、伸长率(应变%)、屈服强度和应变以及真断裂强度和应变。疲劳试验过程产生的数据可用于计算疲劳强度系数(σf′)、疲劳强度指数(b)、疲劳延性系数(εf′)和疲劳延性指数(c)。这些参数为所讨论的材料提供准确的应力-应变曲线、循环应力-应变曲线和应变-寿命曲线。本文还提出了一种利用应变控制数据计算应力-寿命疲劳参数、应力范围截距和斜率的方法。在此基础上,应用模流分析方法预测了注射成型时材料流动方向对增强纤维的总体取向的影响。计算出的单调和疲劳参数与模具流分析相结合,可以立即应用于数字模拟,从而提高卡车零件预期寿命估计的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Monotonic and Fatigue Testing of Polymer and Composite Materials Used in Heavy Duty Trucks
Improved material models for engineered polymer and composite materials including both monotonic and fatigue characteristics are necessary for creating more accurate digital simulations for heavy duty trucks. Unlike steel and other alloys that are commonly included in truck designs, these advanced polymer materials do not have pre-existing fatigue characteristic data. Additionally, there are no individual standard test procedures that can be commonly cited and followed during a research program. These materials are found in hoods, dashboards, body panels and splash shields of trucks, and are subject to cyclic loading conditions at various amplitudes and durations throughout the entire use or “duty cycle” of the vehicle. The applied loads vary between truck models, as some trucks will be used for vocational purposes and others will remain on the highway. This paper describes the testing of isotropic non-reinforced, and anisotropic glass-fiber-reinforced polymers and the subsequent calculation of the monotonic and fatigue properties that are needed to describe their behavior under various loading conditions. Material characteristics are measured using a series of constant amplitude strain-controlled fatigue tests that follow standard practices from ASTM D638 (Standard Test Method for Tensile Properties of Plastics), ASTM E606 (Standard Practice for Strain-Controlled Fatigue Testing) methods, and SAE J1099 (Technical Report on Low Cycle Fatigue Properties of Ferrous and Non-Ferrous Materials). The ASTM D638 Type 1 coupon geometry is used for all materials, with a varied sample thickness and length. An axial extensometer is incorporated to measure strain data through the duration of all tests, and an anti-buckling fixture is installed during cyclic tests to eliminate any bending in the specimen during the compressive portion of the fully-reversed waveform. A transverse extensometer is also installed on the gauge length of the material coupons to measure instantaneous cross-sectional area as well as Poisson’s ratio during monotonic testing. The data collected through the monotonic testing procedure is used to calculate Young’s Modulus, Poisson’s ratio, ultimate tensile strength, elongation (% strain), yield strength and strain, and true fracture strength and strain. The fatigue testing procedure yields data that can be used to calculate the fatigue strength coefficient (σf′), fatigue strength exponent (b), fatigue ductility coefficient (εf′), and fatigue ductility exponent (c). These parameters provide accurate stress-strain, cyclic stress-strain, and strain-life curves for the materials in question. A method will also be suggested for calculating the stress-life fatigue parameters, stress range intercept and slope, from the strain-controlled data. Furthermore, mold-flow analysis is applied to predict general orientation of the reinforcement fibers induced by the direction of material flow as a part is injection-molded. The calculated monotonic and fatigue parameters in conjunction with mold-flow analysis can immediately be applied within digital s imulations, allowing improved accuracy in life-expectancy estimations for truck parts.
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