Thermographical Investigation of Crack Initiation Using Artificial Neural Networks

Abstract

In this study, a thermographic infrared imaging system was used to detect the temperature rise of AISI37 steel specimen under reverse bending fatigue. Fatigue behavior of metals shows temperature profiles with three stages: an initial increase of the specimen mean temperature region, a constant (equilibrium) temperature region, an abrupt temperature increase region at end of which the specimen fails and its temperature falls instantly. In order to recognize critical third region, it is necessary to observe endurance state of the specimen being tested. In this study, the temperature profiles of the specimen under testing are recorded by thermal camera and transferred to the image processing program. The artificial neural networks obtain spot temperatures of the inspected specimen by using its temperature profiles. By analyzing the values of obtained data, we detect spots of highest temperatures as ones that are exposed to most intensive deformation. These regions considered to be probable crack initiation sites.

Determining fatigue limits with thermal analysis of static traction tests

“…They have used infrared analysis to observe that the propagation stage of the crack constitutes a small portion of the specimen's lifetime. Selek et al . and Wagner et al .…”

“…Equations (), () and () show the time dependence of the following parameters as determined by the experimental behaviour during the steel−s complete plastic phase: where σ r is the fracture stress and t r is duration of time during which the specimen exhibits thermoplastic behaviour in the test.…”

Recent progress in diagnosing the reliability of electrical equipment by using infrared thermography

“…Rapid development in computer vision based on image processing techniques and the integration of artificial intelligence has provided advantages in monitoring and diagnosing problems of the power equipments. Currently, automatic inspections of IRT images are widely applied in medical imaging [25][26][27][28][29][30][31][32], nondestructive testing [33,34], defect detection in structures [35,36] and so on. In electrical power applications, automated diagnosis of IRT images employing intelligent systems is still in the early stages.…”

Fatigue limit by thermal analysis of specimen surface in mono axial traction test

“…They observed by infrared analysis that the propagation stage of the crack constitutes a small part of the lifetime of the specimen. Selek et al [35] studied the crack initiation by artificial neural networks using thermographical temperature profiles of AISI 37 under reverse bending. Lazzarin et al [36] studied the local energy density in welded joint and the value of the strain energy averaged over a well-defined control volume was given in closed form.…”

“…In the literature there are many analytical and numerical models that link the damage with energy and thermal variation for fatigue loading [25][26][27][28][29][30][31][32][33][34][35][36] and for static tensile test [37,38] and the hypothesis concerning the plasticization model, internal heat and external transmission, can help to find qualitative solutions only. Following the above easy engineering model, using the similar conditions assumed for the thermo-elasticity theory (adiabatic phenomena are accepted even at moderate strain rates), following the thermoelastic phase, the temperature vs test time can be written as: and ȕ =0,9 (constant) even if it is known that the Tailor coefficient is not constant particularly in the first strain rates [42,43].…”