Geneseo Photography Collection

Professor Michael Teres has been teaching at SUNY Geneseo since 1966. He is a professor of studio art, photography, graphic design, and has also taught several other classes through Geneseo’s (now defunct) Art Department. For the past 49 years, Professor Teres has been collecting his student’s photography. It started off as simply a means to provide examples of outstanding photography to future students, but Professor Teres soon realized he had created an expansive archive of student’s work. He has accumulated a collection of over 2,000 photographs, each one documenting a unique perspective from the eyes of individual students over nearly five decades. His goal has become that of sharing this characteristic part of the SUNY Geneseo history with the local community and the rest of the public eye. Professor Teres has recently received a grant to pursue this goal of archiving the collection of photography, and we have been working together to contact the alumni whose photography is a part of the collection. We have also been experimenting with graphic designs to create a logo, or potential company brand, to distinguish this particular collection. Plans for a website are in the making to document the photos as well, to provide a fast and easy way to maneuver through the array of photographs. With the loss of the Art Department at SUNY Geneseo, it means a great deal now—in fact more than ever—to archive these photographs and allow them to be seen, heard, and felt again

A machine learning assisted preliminary design methodology for repetitive design features in complex structures

The current industrial practice used at the preliminary design stage of complex structures involves the use of multifidelity submodelling simulations to predict failure behaviour around geometric and structural design features of interest, such as bolts, fillets, and ply drops. A simplified global model without the design features is first run and the resulting displacement fields are transferred to multiple local models containing the design features of interest. The creation of these high-fidelity local feature models is highly expert dependent, and their subsequent simulation is highly time-consuming. These issues compound as these design features are typically repetitive in complex structures. This leads to long design and development cycles. Application of machine learning to this framework has the potential to capture a structural designer’s modelling knowledge and quickly suggest improved design feature parameters, thereby addressing the current challenges. In this work, we provide a proof of concept for a machine learning assisted preliminary design workflow, see Figure 1, whereby feature-specific surrogate models may be trained offline and used for faster and simpler design iterations. The key challenge is to maximise the prediction accuracy of failure metrics whilst managing the high dimensions required to represent design feature simulation parameters in a minimum training dataset size. These challenges are addressed using: a modified Latin Hypercube Sampling scheme adjusted to improve design of experiment in composite materials; a bi-linear work-equivalent homogenisation scheme to reduce the number of nodal degrees of freedom; a non-local volume-averaged stress-based approach to reduce the number of target features; and linear superposition of stacked bi-directional LSTM neural network models. This methodology is demonstrated in a case study of predicting the stresses of open hole composite laminates in an aerospace C-spar structure. Results highlight the high accuracy (>90%) and time saving benefit (>15x) of this new approach. This methodology may be used to faster correct and iterate the preliminary design of any large or complex structure where there are repetitive localised design features that may contribute to failure, such as in Formula 1 or wind turbines. Combined with exascale computing this methodology may also be applied for predictive virtual testing of digital twins

On the dynamic tensile behaviour of thermoplastic composite carbon/polyamide 6.6 using split Hopkinson pressure bar

A dynamic tensile experiment was performed on a rectangular specimen of a non-crimp fabric (NCF) thermoplastic composite T700 carbon/polyamide 6.6 specimens using a split Hopkinson pressure (Kolsky) bar (SHPB). The experiment successfully provided useful information on the strain-rate sensitivity of the NCF carbon/thermoplastic material system. The average tensile strength at three varying strain rates: 700, 1400, and 2100/s was calculated and compared to the tensile strength measured from a standardized (quasi-static) procedure. The increase in tensile strength was found to be 3.5, 24.2, and 45.1% at 700, 1400, and 2100/s strain rate, respectively. The experimental findings were used as input parameters for the numerical model developed using a commercial finite element (FE) explicit solver LS-DYNA®. The dynamic FE model was validated against experimental gathering and used to predict the composite system’s behavior in various engineering applications under high strain-rate loading conditions. The SHPB tension test detailed in this study provided the enhanced understanding of the T700/polyamide 6.6 composite material’s behavior under different strain rates and allowed for the prediction of the material’s behavior under real-world, dynamic loading conditions, such as low-velocity and high-velocity impact