The high degree of inhomogeneity in material and intricacies created by machining of carbon fiber reinforced plastic (CFRP) composites hinder the accurate prediction of residual strength of the adhesive bond joint using analytical models. Recently, artificial intelligence techniques are effectively utilized as an alternative method for predicting the results of complex phenomena. In this paper, attempts were made to predict the bond strength of laser surface treated and adhesively bonded CFRP composite specimens using the artificial neural network (ANN) from the acoustic emission (AE) parameter recorded during the shear test. Twelve adhesively bonded specimens whose surfaces were pre-processed with 3W Nd:YAG laser at different processing parameters. ANN was trained using segregated AE data according to the failure mechanism and the percentage of failure load (5 to 100%). Predicted values were compared with experimental values and the results were analysed for the suitability of ANN with AE in the application.

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This paper deals with the investigation on the microstructure and wear behavior of uncoated, Al2O3‐13 wt% TiO2, and WC‐12 wt% Co‐coated drill bits intended for drilling reinforced concrete. Atmospheric plasma spraying technique was employed to fabricate the above coatings on high speed steel drill bits. The circularity measurements of the drilled holes were carried out using MATLAB. The holes drilled using WC‐12 wt% Co‐coated drill bit showed a better circularity depicting enhanced wear resistance as compared to that of the uncoated and Al2O3‐13 wt% TiO2 drill bits. The improvement in the wear resistance of WC‐12 wt% Co‐coated drill bit is attributed to its higher hardness and lower porosity. Thrust force was observed to be lower for WC‐12 wt% Co‐coated drill bit depicting less amount of wear. This study suggests that WC‐12 wt% Co‐coated drill bits can be a potential candidate for drilling reinforced concrete.

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Acoustic emission (AE) data have been collected from 20 randomly oriented short E-glass fibre – unsaturated polyester tensile specimens, while loading up to failure in a tensile testing machine. Peak amplitude and cumulative energy data from AE response of each specimen were classified and segregated by understanding the failure mechanism and data acquired up to 50% of the failure load was utilized for analysis. An optimized feed-forward back-propagation (FFBP) type artificial neural network (ANN) was designed and the segregated data of amplitude hits and cumulative energy was processed using it. Even though the accuracy of both networks were satisfactory, amplitude hit based network gave better predictions of the ultimate tensile strength (UTS) than the energy based network. Also the performance of various training algorithms in the designed network was analysed and the results were compared.