Fatigue criteria for short fiber-reinforced thermoplastic validated over various fiber orientations, load ratios and environmental conditions
Source abstract
Abstract
Abstract Design of reinforced plastic parts against fatigue failure has become a serious issue these last few years for increasing engineering field applications. This is indeed a complex problem due to the strong anisotropy induced by the orientation of the fibers during the injection process and to the non-linear dissipative behavior of the matrix. This generates strong influences of numerous parameters (fiber orientation, environment, strain rate, load ratio, …), which complicates both the description of the constitutive response and the definition of a robust and efficient fatigue criterion. This paper presents a very wide fatigue database (480 tension-compression tests) obtained on Polyamide 66 reinforced with 50% of glass fibers (PA66 GF50), for an extended range of load ratios (from −0.5 to 0.7), three orientations from the injection direction (0 ° , 45 ° , 90 ° ) and two environmental conditions (50% humidity ratio and 80 ° C, 80% humidity ratio and 23 ° C). Beyond these experimental results, the goal of this paper is to compare the ability of numerous fatigue criteria to provide a robust description of the fatigue database, possibly with a unified set of parameters. Classical models as well as very recent ones are discussed and two variants of a new fatigue criterion are finally defined. This criterion is able to describe very accurately the full database with only four parameters. Finally, a focus is given to determine the minimal amount of experimental data needed to obtain a reliable description of the fatigue results, on the full ranges of load ratios, orientations and environmental conditions.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Mechanical Behavior of Composites · Mechanical Engineering and Vibrations Research · Fatigue and fracture mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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