Susheel Dharmadhikari

Pin fins are imperative in the cooling of turbine blades. The designs of pin fins, therefore, have seen significant research in the past. With the developments in metal additive manufacturing, novel design approaches toward complex geometries are now feasible. To that end, this article presents a Bayesian optimization approach for designing inline pins that can achieve low pressure loss. The pin-fin shape is defined using featurized (parametrized) piecewise cubic splines in 2D. The complexity of the shape is dependent on the number of splines used for the analysis. From a method development perspective, the study is performed using three splines. Owing to this piece-wise modeling, a unique pin fin design is defined using five features. After specifying the design, a computational fluid dynamics-based model is developed that computes the pressure drop during the flow. Bayesian optimization is carried out on a Gaussian processes-based surrogate to obtain an optimal combination of pin-fin features to minimize the pressure drop. The results show that the optimization tends to approach an aerodynamic design leading to low pressure drop corroborating with the existing knowledge. Furthermore, multiple iterations of optimizations are conducted with varying degree of input data. The results reveal that a convergence to similar optimal design is achieved with a minimum of just twenty five initial design-of-experiments data points for the surrogate. Sensitivity analysis shows that the distance between the rows of the pin fins is the most dominant feature influencing the pressure drop. In summary, the newly developed automated framework demonstrates remarkable capabilities in designing pin fins with superior performance characteristics.

The article presents a mixed deep neural network (DNN) approach for detecting micron-scale fatigue damage in high-strength polycrystalline aluminum alloys. Fatigue testing is conducted using a custom-designed apparatus integrated with a confocal microscope and a moving stage to accurately pinpoint the instance of micron-scale crack emergence. The specimens are monitored throughout the duration of the experiment using a pair of high-frequency ultrasonic transducers. The mixed DNN is trained with ultrasonic time-series data that are obtained from two sets of specimens categorized by different stress concentration factors. To understand the effects of mixing the data from both types of specimens, a parametric analysis is performed by varying the amount of training data from each specimen to develop a series of mixed DNNs. The mixed DNN, when tested on unseen data from both specimens, exhibits an accuracy of over 95%. This article, therefore, demonstrates a successful alternative to customized DNNs for new types, geometries, or stress concentration factors in the materials under consideration.

Fatigue damage detection and its classification in metallic materials are persistently challenging the structural health monitoring community. The mechanics of fatigue damage is difficult to analyze and is further complicated because of the presence of notches of different geometries. These notches act as possible crack-nucleation sites resulting in failure mechanisms that are drastically different from one another. Often, sensor-based tools are used to monitor and detect fatigue damage in critical metallic materials such as aluminum alloys. Through deep neural networks (DNNs), such a sensor-based approach can be ubiquitously extended for a variety of geometries as appropriate for different applications. To that end, this paper presents a DNN-based transfer learning framework that can be used to classify and detect fatigue damage across candidate notch geometries. The DNNs are built upon ultrasonic time-series data obtained during fatigue testing of Al7075-T6 specimens with two types of notch geometries, namely, a U-notch and a V-notch. The baseline U-notch DNN is shown to achieve an accuracy of 96.1% while the baseline V-notch DNN has an accuracy of 95.8%. Both baseline DNNs are, thereafter, subjected to a transfer learning process by keeping a certain number of layers frozen and retraining only the remaining layers with a small volume of data obtained from the other notch geometry. When a layer of the baseline U-notch DNN is retrained with just 10% of the total V-notch data, an accuracy above 90% is observed for fatigue damage detection of V-notch specimens. Similar results are also obtained when the baseline V-notch DNN is retrained and interrogated to detect damage for U-notch specimens. These results, in summary, demonstrate the data-thrifty quality of combining the concepts of transfer learning and DNN for fatigue damage detection in different geometries of specimens made of high-performance aluminum alloys.

The paper presents a coupled machine learning and pattern recognition algorithm to enable early-stage fatigue damage detection in aerospace-grade aluminum alloys. U- and V-notched Al7075-T6 specimens are instrumented with a pair of ultrasonic sensors and, thereafter, tested on an MTS apparatus integrated with a confocal microscope and a digital microscope. The confocal microscope is focused on the notch root of the specimens, whereas the digital microscope is focused on the side of the notch. Two features, viz., the crack opening displacement (COD) and the crack length, are extracted during the tests in addition to the ultrasonic signal data. These signal data are analyzed using a machine learning framework that is built upon a symbolic time-series algorithm. This framework is interrogated for crack detection in the crack coalescence (CC) regime defined by COD of ~3 μm and detected through the confocal microscope. Additionally, the framework is probed in the crack propagation (CP) regime characterized by a crack length of ~0.2 mm and detected via the digital microscope. For the CC regime, training accuracies of 79.82% and 81.94% are achieved, whereas testing accuracies of 68.18% and 74.12% are observed for the U- and V-notched specimens, respectively. For the CP regime, overall training accuracies of 88.3% and 91.85% are observed, and accordingly, testing accuracies of 81.94% and 85.62% are obtained for the U- and V-notched specimens, respectively. The results show that a combined machine learning and pattern recognition algorithm enables robust and reliable fatigue damage detection in aerospace structural components.

Fatigue failure occurs ubiquitously in mechanical structures when they are subjected to cyclic loading well below the material’s yield stress. The tell-tale sign of a fatigue failure is the emergence of cracks at the internal or surface defects. In general, a machinery component has a finite fatigue life based on the number of cycles, it can sustain before a fracture occurs. However, the estimated life is generally conservative and often a large factor of safety is applied to make the component fail-safe. From the perspective of better utilization of a machinery component, it is, however, desirable to have maximum usage of the component without a catastrophic failure. It is, therefore, conducive to have a measure that can capture precursors to failure to facilitate active diagnosis of the machinery health. In this study, a precursor detection method is developed upon modifications of probabilistic finite state automata (PFSA). The efficacy of the proposed method is demonstrated on cold-rolled AL7075-T6 notched specimens in a computer-instrumented and computer-controlled fatigue testing apparatus. The results show that the proposed method is capable of detecting the emergence of cracks (at ∼95% accuracy) and also can capture precursors with good fidelity.