Applications of ultrasonic testing and machine learning methods to predict the static & fatigue behavior of spot-welded joints

© 2020 The Society of Manufacturing Engineers. This manuscript is made available under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence (CC BY-NC-ND 4.0). For further details please see: https://creativecommons.org/licenses/by-nc-nd/4.0/Ultrasonic Testing (UT) is one of the well-known Non-Destructive Techniques (NDT) of spot-weld inspection in the advanced industries, especially in automotive industry. However, the relationship between the UT results and strength of the spot-welded joints subjected to various loading conditions isunknown. The main purpose of this research is to present an integrated search system as a new approach for assessment of tensile strength and fatigue behavior of the spot-welded joints. To this end, Resistance Spot Weld (RSW) specimens of three-sheets were made of different types of low carbon steel. Afterward, the ultrasonic tests were carried out and the pulse-echo data of each sample were extracted utilizing Image Processing Technique (IPT). Several experiments (tensile and axial fatigue tests) were performed to study the mechanical properties of RSW joints of multiple sheets. The novel approach of the present research is to provide a new methodology for static strength and fatigue life assessment of three-sheets RSW joints based on the UT results by utilizing Artificial Neural Network (ANN) simulation. Next, Genetic Algorithm (GA) was used to optimize the structure of ANN. This approach helps to decrease the number of tests and the cost of performing destructive tests with appropriate reliability.Peer reviewe

Intelligent evacuation management systems: A review

Crowd and evacuation management have been active areas of research and study in the recent past. Various developments continue to take place in the process of efficient evacuation of crowds in mass gatherings. This article is intended to provide a review of intelligent evacuation management systems covering the aspects of crowd monitoring, crowd disaster prediction, evacuation modelling, and evacuation path guidelines. Soft computing approaches play a vital role in the design and deployment of intelligent evacuation applications pertaining to crowd control management. While the review deals with video and nonvideo based aspects of crowd monitoring and crowd disaster prediction, evacuation techniques are reviewed via the theme of soft computing, along with a brief review on the evacuation navigation path. We believe that this review will assist researchers in developing reliable automated evacuation systems that will help in ensuring the safety of the evacuees especially during emergency evacuation scenarios

Modeling movement artefacts in handheld laser speckle contrast perfusion imaging: Influence of wavefront types

Movement artefacts distort handheld measurements of laser speckle contrast imaging (LSCI). Enabling a robust LSCI in handheld use brings convenience for both patients and clinical staff. However, there is a lack of a comprehensive model that can predict and potentially compensate the amount of movement artefacts occurring during a handheld LSCI measurement. Here, we propose an analytical-numerical model based on the optical Doppler effect for handheld LSCI in case of translation on a high scattering static surface. The model incorporates the type of illumination as well as the imaging geometry by taking into account the spread of wavevectors for illumination and detection. We validate the theoretical model by simulated dynamic speckles and experiments for the cases of (1) planar and spherical waves illumination and (2) scrambled waves illumination. Results of the speckle simulation are in agreement with predictions of the numerical model for semi-circular form of the density functions of the incoming and outgoing wavevectors

Modelling movement artefacts in handheld laser speckle contrast imaging

Progress has been made in laser speckle contrast imaging (LSCI) of microcirculatory blood flow for biology and medicine. However, the underlying reason for occurrence of movement artefacts (MA) that compromises effective use of LSCI remains largely unexplored. Here, employing a dual-camera setup for both speckle imaging and movement tracking, we validate our analytical model that is based on optical Doppler effect for predication of speckle contrast drop as a function of applied translational speed. We perform both motorized and handheld experiments where planar and scrambled wave illumination schemes have been examined. Experimental data points fairly match the theoretical predictions. These findings indicate that the vision-based movement detection during handheld LSCI is a preferable option. Moreover, the proposed analytical model is promising for further exploration of MA in order to realize a reliable handheld LSCI