Thermoplastic clear dental aligners under cyclic compression loading: A mechanical performance analysis using acoustic emission technique.

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.

A Neural Network Framework for Validating Information-Theoretics Parameters in the Applications of Acoustic Emission Technique for Mechanical Characterization of Materials.

A multiparameter approach is preferred while utilizing Acoustic Emission (AE) technique for mechanical characterization of composite materials. It is essential to utilize a statistical parameter, which is independent of the sensor characteristics, for this purpose. Thus, a new information-theoretics parameter, Lempel-Ziv (LZ) complexity, is used in this research work for mechanical characterization of Carbon Fibre Reinforced Plastic (CFRP) composites. CFRP specimens in plain weave fabric configurations were tested and the acoustic activity during the loading was recorded. The AE signals were classified based on their peak amplitudes, counts, and LZ complexity indices using k-means++ data clustering algorithm. The clustered data were compared with the mechanical results of the tensile tests on CFRP specimens. The results show that the clustered data are capable of identifying critical regions of failure. The LZ complexity indices of the AE signal can be used as an AE descriptor for mechanical characterization. This is validated by studying the clustered signals in their time-frequency domain using wavelet transform. Finally, a neural network framework based on SqueezeNet was trained using the wavelet scalograms for a quantitative validation of the data clustering approach proposed in this research work. The results show that the proposed method functions at an efficiency of more than 85% for three out of four clustered data. This validates the application of LZ complexity as an AE descriptor for AE signal data analysis.