Sensing System Research Center (SSRC), National Institute of Advanced Industrial Science and Technology (AIST);
Sensing System Research Center (SSRC), National Institute of Advanced Industrial Science and Technology (AIST).
J Vis Exp. 2023 Jan 6(191). doi: 10.3791/64118.
In this study, methods for the mechanoluminescent (ML) visualization of crack propagation and mechanical behavior to evaluate adhesive joints are demonstrated and explained. The first step involved sample preparation; an air spray was used to apply ML paint to the surface of the adhesive joint specimens. The performance of the ML sensor was described to examine the measurement conditions. The results of ML sensing during a double cantilever beam (DCB) test and a lap-shear (LS) test are demonstrated as these are the most frequently and widely used methods for evaluating adhesives. Originally, it was difficult to directly quantify the crack tip and strain/stress distribution and concentration because the crack tip was too small, and the effects of the strain could not be observed. The mechanoluminescence, crack propagation, and mechanical behavior during mechanical testing can be visualized via the ML pattern during the adhesive evaluation. This allows for the recognition of the precise position of the crack tips and other mechanical behaviors related to structural failure.
在本研究中，展示并解释了用于机械发光（ML）可视化裂纹扩展和机械行为以评估胶接接头的方法。第一步涉及样品制备；使用空气喷涂将 ML 涂料施加到胶接接头试样的表面。描述了 ML 传感器的性能，以检查测量条件。展示了在双悬臂梁（DCB）测试和搭接剪切（LS）测试中进行 ML 感应的结果，因为这些是最常用和广泛使用的评估胶粘剂的方法。最初，由于裂纹尖端太小，无法直接量化裂纹尖端和应变/应力分布和集中，并且无法观察到应变的影响。在机械测试过程中的机械发光、裂纹扩展和机械行为可以通过胶接评估过程中的 ML 模式进行可视化。这使得能够识别裂纹尖端的精确位置和与结构失效相关的其他机械行为。
