An acousto-ultrasonic approach is used to explore the damage development in tensile shear spot welds during fatigue loading. There is reasonable data to support the hypothesis that a decrease in an AU signal is indicative of the presence of an internal crack and could be used for monitoring and evaluation purposes. / Master of Science

Τα βασικά προβλήματα που εμφανίζονται κατά τον μη καταστροφικό έλεγχο με την μέθοδο της ακουστικής εκπομπής (ΑΕ) είναι η απομόνωση του θορύβου, η αξιόπιστη επεξεργασία και αναγνώριση των σημάτων από πραγματικές αστοχίες του υλικού, ο προσδιορισμός της θέσης της αστοχίας και ο χαρακτηρισμός του τύπου και της κρισιμότητας της βλάβης στο υλικό. Κατά την διάρκεια εκπόνησης της παρούσας διδακτορικής διατριβής δόθηκε ιδιαίτερη έμφαση στην μεθοδολογία εύρεσης της θέσης της πηγής ΑΕ δεδομένου ότι είναι γνωστοί οι χρόνοι άφιξης κάποιων χαρακτηριστικών των σημάτων που έχουν καταγραφεί στους αισθητήρες. Αναπτύχθηκαν ολοκληρωμένες μέθοδοι στις οποίες επεξεργάζονται τα σήματα των αισθητήρων για να προσδιοριστούν τα χαρακτηριστικά που θα αποτελέσουν την βάση για τον υπολογισμό της θέσης της πηγής. Έχοντας εξασφαλίσει την αξιόπιστη μέθοδο προσδιορισμού των χρόνων άφιξης ορισμένων χαρακτηριστικών των σημάτων αναπτύχθηκαν μέθοδοι οι οποίοι χρησιμοποιούν όσο το δυνατό περισσότερη πληροφορία για βελτίωση της ακρίβειας εκτίμησης και μικρότερες απαιτήσεις σε επιπλέον γνώση δεδομένων. / In non-destructive control, acoustic emission signals are used for reliable construction monitoring and damage recognition. In this thesis several methods for the acoustic emission (AE) source location are developed and evaluated. Automatic estimation of minimum number and optimal placement of sensors are derived at the minimum sum of localization errors at randomly positioning AE sources. A new method was proposed and evaluated for the estimation of optimum sensors position in problems of AE localization in spherically and cylindrical structures. The particular methodology can be easily adjusted in different structures, and is of paramount important in case where the sensors must be permanently placed in a structure. Six source location methods were developed using a parametric model for the AE signal, genetic algorithm and simulated annealing. The magnitude of the Fast Fourier Transform or the position of the maximum peak of cross correlation function are extracted from the AE signals acquired by multiple sensors positioning at arbitrary locations in a plain or a cylindrical structure. The AE source is estimated at the minimum of the error function between the signal or the features derived from the acoustic signal, and the signal or features estimated from the AE signal model. Moreover, a novel source location method based on radial basis function network is presented and evaluated. The problem of AE localization in plane surfaces and cylindrical surfaces are solved in a close-form using the arrival-time differences using three or more sensors. A close-form solution for Acoustic-Emission source location (AESL) and material constant G is presented and evaluated in simulation experiments using the Time-of-Arrival (TOA) of several events detected in arbitrary positioning sensors in 3d-space in dispersive media. The normalized distances and the constant G are derived from the TOA at four arbitrary selected sensors using the events propagation velocities in a reference material. The actual AE position is derived using the multidimensional scaling method using the complete set of sensors. In simulation experiments, the advantages of the proposed method are demonstrated. Overcoming the most important weakness of the proposed method, the use of only four sensors for the estimation of the parameter G, an algorithm for successive estimation of the AESL is developed using the complete set of TOAs.An extension of the AESL method is developed using a successive approximation algorithm assuming a minimum of two known propagation velocities for the recorded events. It is proved that the proposed algorithm converges to the local minimum of the optimization function. Under few restrictions the proposed algorithm can be used to estimate the AESL even in case where the propagation velocities for all events are unknown.