Doctoral Degrees (Mechanical and Mechatronic Engineering)

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Browsing Doctoral Degrees (Mechanical and Mechatronic Engineering) by Author "Atkinson, Devan James"

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    Adaptive digital image correlation using neural networks
    (Stellenbosch : Stellenbosch University, 2023-03) Atkinson, Devan James; Becker, Thorsten Hermann; Neaves, Melody; Stellenbosch University. Faculty of Engineering. Dept. of Mechanical and Mechatronic Engineering.
    ENGLISH SUMMARY: Subset size selection is crucial to the accuracy and precision of digital image correlation (DIC) measured displacements. Increasing the subset size improves noise suppression (reducing random errors) at the cost of spatial resolution (ability to accurately measure complex displacement fields). The tradition of global correlation parameter assignment is suboptimal because the speckle pattern quality and displacement field complexity can vary spatially. Dynamic subset selection (DSS), which assigns location specific optimal subset sizes, is challenging because the metrological performance of correlation is dictated by complex interactions between correlation parameters (subset size and shape function) and image set properties (noise, speckle pattern and displacement field complexity). This dissertation uses an open-source DIC framework to investigate the potential of artificial neural networks (ANNs) for error prediction and DSS, prior to the DIC process, from purely image information. ANNs are capable of modelling complex relationships within noisy, incomplete data without imposing fixed relationships, inspiring their recent resurgence for DIC applications. Despite the plethora of open-source DIC algorithms available, none offer spatially and temporally independent assignment of correlation parameters. Subsequently, a modular, open-source DIC framework capable of such flexibility is developed. This framework is predominantly consistent with current state-of-the-art practices and performs on par with well-established open-source and commercial DIC algorithms. Drawing direct links between the well-documented theory of DIC and its nuanced practical implementation, bridges this gap in literature which has acted as a barrier to newcomers intending to develop the capabilities of DIC. This framework, implemented in 117 and 202 lines of MATLAB code for 2D and stereo DIC, respectively, is attractive as a starting point to further the capabilities of DIC. The feed-forward ANN developed using this DIC framework, predicts random errors based on the speckle pattern quality (contained within a subset) and standard deviation of image noise more accurately and precisely than established theoretical derivations. A DSS framework is developed which uses this ANN to appoint subset sizes, based on the local speckle pattern, that offer random errors consistent with a stipulated threshold value. Appropriate selection of the random error threshold offers a favourable compromise between noise suppression and spatial resolution for up to moderate displacement gradients. Consequently, in the presence of varying speckle pattern quality this framework outperforms the traditional approach of trialand- error global subset size selection for the same mean subset size. Speckle pattern characteristics outside the training scope reveal the generalisability limitations of the DSS method, and associated ANN, as it performs on par with the traditional global subset size approach, motivating the need to broaden its training scope. Investigation of convolutional neural networks for dynamic shape function selection is initiated, showing they are capable of quantifying displacement field complexity between image pairs to guide spatially and temporally independent shape function assignment. The dissertation reveals that ANNs are an attractive approach to model the correlation parameter assignment. Furthermore, such models facilitate dynamic correlation parameter assignment from purely image information such that they can operate as a pre-process to DIC.