A. Tobias

Bio: A. Tobias is an academic researcher from Central Electricity Generating Board. The author has contributed to research in topics: Acoustic emission & Sensor array. The author has an hindex of 1, co-authored 1 publications receiving 223 citations.

Acoustic-emission source location in two dimensions by an array of three sensors

Abstract: Sensors on the surface of a material under stress can detect acoustic emissions from a defect within the material. The difference in time of detection of an emission from the defect at different sensors gives a way of finding where it is. This paper shows a general method of calculating the location of defects in two dimensions from the arrival times at the sensors. The ACEMAN system, which uses this method can derive the resolution properties of a sensor array in about 7.5 min.

A new algorithm for acoustic emission localization and flexural group velocity determination in anisotropic structures

Abstract: This paper presents a new in situ Structural Health Monitoring (SHM) system able to identify the location of acoustic emission (AE) sources due to low-velocity impacts and to determine the group velocity in complex composite structures with unknown lay-up and thickness. The proposed algorithm is based on the differences of stress waves measured by six piezoelectric sensors surface bonded. The magnitude of the Continuous Wavelet Transform (CWT) squared modulus was employed for the identification of the time of arrivals (TOA) of the flexural Lamb mode ( A 0 ). Then, the coordinates of the impact location and the flexural wave velocity were obtained by solving a set of non-linear equations through a combination of global Line Search and backtracking techniques associated to a local Newton’s iterative method. To validate this algorithm, experimental tests were conducted on two different composite structures, a quasi-isotropic CFRP and a sandwich panel. The results showed that the impact source location and the group speed were predicted with reasonable accuracy (maximum error in estimation of the impact location was approximately 2% for quasi-isotropic CFRP panel and nearly 1% for sandwich plate), requiring little computational time (less than 2 s).

Impact detection in anisotropic materials using a time reversal approach

Abstract: This article presents an in situ imaging method able to detect in real-time the impact source location in reverberant complex composite structures using only one passive sensor. This technique is b...

A critical analysis of effectiveness of acoustic emission signals to detect tool and workpiece malfunctions in milling operations

Abstract: The industrial demands for automated machining systems to increase process productivity and quality in milling of aerospace critical safety components requires advanced investigations of the monitoring techniques. This is focussed on the detection and prediction of the occurrence of process malfunctions at both of tool (e.g. wear/chipping of cutting edges) and workpiece surface integrity (e.g. material drags, laps, pluckings) levels. Acoustic emission (AE) has been employed predominantly for tool condition monitoring of continuous machining operations (e.g. turning, drilling), but relatively little attention has been paid to monitor interrupted processes such as milling and especially to detect the occurrence of possible surface anomalies. This paper reports for the first time on the possibility of using AE sensory measures for monitoring both tool and workpiece surface integrity to enable milling of “damage-free” surfaces. The research focussed on identifying advanced monitoring techniques to enable the calculation of comprehensive AE sensory measures that can be applied independently and/or in conjunction with other sensory signals (e.g. force) to respond to the following technical requirements: (i) to identify time domain patterns that are independent from the tool path; (ii) ability to “calibrate” AE sensory measures against the gradual increase of tool wear/force signals; (iii) capability to detect workpiece surface defects (anomalies) as result of high energy transfer to the machined surfaces when abusive milling is applied. Although some drawbacks exist due to the amount of data manipulation, the results show good evidence that the proposed AE sensory measures have a great potential to be used in flexible and easily implementable solutions for monitoring tool and/or workpiece surface anomalies in milling operations.