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The objective of this work is to analyse the performance of clear aligners made of thermoplastic materials. Within this framework, the damage evolution stages and damage states of the aligners at different cycles of the compressive loading are evaluated using the Acoustic Emission (AE) technique. Three different clear aligner systems were prepared: thermoformed PET-g (polyethylene terephthalate glycol) and PU (polyurethane), and additively manufactured PU. Cyclic compression tests are performed to simulate 22500 swallows. The mechanical results show that the energy absorbed by the thermoformed PET-g aligner remains stable around 4 Nmm throughout the test. Although the PU-based aligners show a higher energy absorption of about 7 Nmm during the initial phase of the cyclic loading, this gradually decreases after 12500 cycles. The time-domain based, and frequency-based parameters of the stress wave acoustic signals generated by the aligners under compression loading are used to identify the damage evolution stages. The machine learning-based AE results reveal the initiation and termination of the different damage states in the aligners and the frequency-based results distinguish the different damage sources. Finally, the microscopy results validated the damage occurrences in the aligners identified by the AE results. The mechanical test results indicate that the thermoformed PET-g has the potential to match the performance and requirements of the dentistry of the popular Invisalign (additively manufactured PU). The AE results have the potential to identify at which cycles the aligners may start losing their functionality. [Display omitted] • Clear Aligners (CAs) undergo long deformations under compression cycles. • Understanding the work cycle at failure is imperative to improve their functionality. • Material and Manufacturing methods are critical factors in performance metrics. • Acoustic Emission technique potentially can evaluate the behaviour of CAs. [ABSTRACT FROM AUTHOR]

In this research work, a deep Convolutional Neural Network (CNN) was trained for image-based Acoustic Emission (AE) waveform classification. AE waveforms from different damage modes of Carbon Fibre Reinforced Polymer (CFRP) composites were used to train the CNN for online damage monitoring. Spectrograms of AE Waveforms from four different damage modes, matrix cracking, delamination, debonding, and fibre breakage, were obtained in their Mel scale and used as the training data and test data for the CNN. The overall prediction accuracy of the CNN is 97.9%, while the fibre breakage and delamination events were able to be predicted with 100% accuracy. Then this pre-trained CNN is used for online damage monitoring of mode I delamination test of CFRP specimens. AE waveforms generated during the mode I test are classified using the trained CNN and the results are analysed in terms of the classified AE descriptors. The classified AE descriptors proved to identify the occurrences of different damage modes, thereby validating the damage classification accuracy of the CNN. [ABSTRACT FROM AUTHOR]

• New methodologies of using AE technique for damage characterization is introduced. • Laplacian Score is used for selecting the best AE features. • k-means data clustering technique is used for categorizing the AE data. • Different damage modes are assessed by clustered AE data and load curves. Carbon Fiber Reinforced Polymer (CFRP) composites, adhesively bonded in a Single Lap Shear (SLS) configuration, are tested in this study. The damage characteristics are analysed using Acoustic Emission (AE) data recorded during the test. Among the different features of the AE data, the most suitable features are selected using a methodology developed based on the Laplacian scores for feature selection. The selected features are then analysed using k-means clustering algorithm for establishing a relationship among them. Amplitude of the recorded AE signals and their Frequency Centroid (C-Freq) had a strong relationship, which is used for characterizing the damage modes. The cumulative counts of the clustered data of Amplitude vs C-Freq are used for characterizing the damage modes. A comprehensive characterization of the damage modes in each stage of loading and the final failure is made using this method. [ABSTRACT FROM AUTHOR]