|About this Abstract
||Materials Science & Technology 2020
||Additive Manufacturing: Qualification and Certification
||A Multi-Sensor Comparative Study for Fatigue Prognosis of Additively Manufactured Metallic Specimens
||Susheel Dharmadhikari, Asok Ray, Amrita Basak
|On-Site Speaker (Planned)
The research presents a novel methodology for fatigue prognosis of additively manufactured AlSi10Mg specimens by comparing two timeseries signals from ultrasonic and strain sensors and two sets of image sequences from confocal and digital microscopes. Ultrasonic investigation is used extensively to identify cracks. Similarly, hysteresis curves are known to contain the cumulative damage information indicating failures. Both sets of data work successfully in segregating cracked and uncracked specimens. Initiation of crack, on the other hand, cannot be clearly identified through these data sets due to seemingly insignificant changes in the signals. In such situations, magnified images from confocal and digital microscopes capture minute cracks and help in identifying these crack-initiation-windows in the time series signals. Using pattern recognition techniques, these windows can then be processed to identify unique features that correspond to crack initiation. With accurate calibration, the framework finds its direct application in online fatigue prognosis.