Analysis of loading history influence on fatigue and fracture surface parameters using the method of induction trees

Abstract

In fatigue life testing under various loading conditions, researchers observe the profile, surface and morphology of materials. In this study authors research the fatigue life of material and the surface fracture geometry. Areal field and fractal based characterisation are evaluated for the whole fracture surfaces. Results of this test were correlated to notch geometry and loading conditions. It was confirmed, for notched specimens, that the change from torsion to proportional bending with torsion fatigue life increase significantly, the same as changing loading from bending with torsion to bending. The measurement device was equipped with a motorised nosepiece using five dedicated microscopic objective lenses from 2.5× to 100× magnification. This paper presents the application of the induction tree method for analysis of loading history influence on fatigue and fracture surface parameters. In a decision tree, nodes store tests checking values of example attributes and leaves store categories assigned to them. For each of possible test results, there is one branch coming from a node to a subtree. In this way, it is possible to represent any attributes of the hypothesis admissible for a given set. Analysis of selected parameters will estimate their impact on the surface structure.

Profile and Areal Surface Parameters for Fatigue Fracture Characterisation

“…Therefore, the results may be ambiguous. However, the influence of the stress ratio R on the value of the Sa parameter showed an increase of this parameter along with an increase in the cycle asymmetry factor from −1 to 0, which is consistent with previously obtained results [ 53 ]. The magnitude of the load also affected all roughness parameters.…”

The Use of Multicriteria Inference Method to Identify and Classify Selected Combustion Engine Malfunctions Based on Vehicle Structure Vibrations

“…The classification accuracy from the J48 algorithm, the best first tree algorithm, the random tree forest algorithm, the functional tree algorithm, and the linear tree algorithm are compared, and the best algorithm for a given system is recommended. Deptuła et al [ 36 ] use a modular decision system based on graph networks parametrically playing an important role in the acoustic diagnostics of an internal combustion engine. Currently, the classification with the use of inductive decision trees is used in determining the most important faults.…”