Papers by Author: J.F. Durodola

Abstract: A method is presented to demonstrate the use of artificial neural networks (ANNs) in providing additional information regarding defects or flaws when used in conjunction with the ultrasonic A-scan method. ANNs were employed both as pattern classifiers and as function approximators to maximise the amount of data available from the temporal A-scan signal. A steel bar was modelled in 2D using ABAQUS finite element analysis (FEA) software. A single defect was introduced to the bar, modelled as a void, and parametric studies conducted to record data with the defect at various locations. An ultrasonic Lamb wave was introduced at the top of the bar. The longitudinal wave propagated along the length of the bar and was partially reflected by the defect. Multiple cases were simulated, modelling voids between 1mm and 6mm in width in various locations. Mean displacement of all the nodes at the top of the bar was recorded throughout the simulation, and features extracted from this waveform to create the data set for the ANNs. The ANNs were trained with a percentage of the data collected, selected at random, and assessed with the remaining data. The target data for the ANNs were the depth and size of the defect. The case of two separate defects was also investigated. The procedure was carried out in the same manner as for one defect, but in this case the target data for the ANNs were the depth of the first defect and the distance between the defects. A separate ANN was employed as a pattern classifier, to determine if the reflected A-scan signal represented one or two defects. The final system was tested using previously unseen data, and provided very good results both in determining the number of defects and the size and location of the defects, even with data to which noise had been added.

Abstract: Locating defects and classifying them by their size was done with an Adaptive Neuro Fuzzy Procedure (ANFIS). Postulated void of three different sizes (1x1 mm, 2x2 mm and 2x1 mm) were introduced in a bar with and without a notch. The size of a defect and its localization in a bar change its natural frequencies. Accordingly, synthetic data was generated with the finite element method. A parametric analysis was carried out. Only one defect was taken into account and the first five natural frequencies were calculated. 495 cases were evaluated. All the input data was classified in three groups. Each one has 165 cases and corresponds to one of the three defects mentioned above. 395 cases were taken randomly and, with this information, the ANN was trained with the backpropagation algorithm. The accuracy of the results was tested with the 100 cases that were left. This procedure was followed in the cases of the plain bar and a bar with a notch. In the next stage of this work, the ANN output was optimized with ANFIS. The accuracy of the localization and classifications of the defects was improved.

Abstract: An inverse artificial neural network (ANN) assessment for locating defects in bars with or without notches is presented in the paper. Postulated void defects of 1mm x 1mm were introduced into bars that were impacted with an impulse step load; the resultant elastic waves propagate impinging on the defects. The resultant transient strain field was analyzed using the finite element method. Transient strain data was collected at nodal points or sensors locations on the boundary of the bars and used to train and assess ANNs. The paper demonstrates quantitatively, the effects of features such as the design of ANN, sensing parameters such as number of data collection points, and the effect of geometric features such as notches in the bars.

Abstract: The levels of stress and strain experienced by the windings of a superconducting magnet during its manufacture and operation are dependent on the mechanical properties of the multifilamentary composite wire that the windings are comprised of. It is also influenced by the change in dimensions of the wire during the reaction heat-treatment stage of the manufacturing process. Using specimens cut from a spool of 1.5mm diameter niobium tin type superconducting wire, the influence of the heat-treatment process on the mechanical properties and dimensions have been investigated. The heat-treatment was carried out in an inert atmosphere using apparatus specially developed for the purpose. For heat-treatment durations up to that required to complete the reaction of the niobium filaments into Nb3Sn, the volume and diameter of the wires increased with increasing heat-treatment duration. The maximum increase was 2% and 0.8% respectively. The length of the wires decreased slightly for the shorter heat-treatments, but increased up to 0.33% for the longer ones. The mechanical properties were significantly different for specimens that had no heat-treatment compared to those that had only a short heat-treatment, one that was insufficient to convert much of the niobium into Nb3Sn. Before heat-treatment the bronze within the wires is in a work-hardened state, but gets annealed during the heat-treatment and this is probably the major cause of the change in mechanical properties. Apart from becoming more brittle, the mechanical properties do not change much for different durations of heat-treatment. This is quite remarkable considering that the composition changes dramatically with the length of the heat-treatment.

Abstract: A new inverse method has been developed for the simultaneous derivation of tensile and compressive stress strain behaviour from bending tests only. This new procedure can be applied to materials having asymmetric tensile and compressive stress strain behaviour and also materials that have been previously strain hardened (Bauschinger Effect). This paper presents results obtained using the new method and compares them with experimentally obtained tensile and compressive stress strain curves. The agreement of the derived stress strain data in tension and compression is encouraging.

Abstract: High stress gradients occur at metal-to-ceramic joints due to the different thermal and mechanical properties of the materials. In some cases, the magnitude of the highly localized stresses lead to failure thus compromising the structural integrity of such joints. The study of notched ceramic bars with high stress gradients can assist with the prediction of failure of metal ceramic joints. Experiments and fracture mechanics analysis were performed on notched and un-notched POCO E.D.M 3 graphite and AS800 Silicon Nitride bars with different notch parameters. The twoparameter, multi-axial Weibull statistics method and a brittle fracture criterion based on the average stress over an area approach were used to predict the failure of the bars and the results obtained were compared with experimental results. The brittle failure criterion appears to give much better correlation with experimental results than the multi-axial Weibull statistics approach. The findings also appear to highlight the limitations of the Weibull’s statistics method in cases involving very high stress gradients.