Skip to main content

Research Repository

Advanced Search

All Outputs (4)

Towards a more reliable interpretation of machine learning outputs for safety-critical systems using feature importance fusion (2021)
Journal Article
Rengasamy, D., Rothwell, B. C., & Figueredo, G. P. (2021). Towards a more reliable interpretation of machine learning outputs for safety-critical systems using feature importance fusion. Applied Sciences, 11(24), Article 11854. https://doi.org/10.3390/app112411854

When machine learning supports decision-making in safety-critical systems, it is important to verify and understand the reasons why a particular output is produced. Although feature importance calculation approaches assist in interpretation, there is... Read More about Towards a more reliable interpretation of machine learning outputs for safety-critical systems using feature importance fusion.

Machine learning to determine the main factors affecting creep rates in laser powder bed fusion (2021)
Journal Article
Sanchez, S., Rengasamy, D., Hyde, C. J., Figueredo, G. P., & Rothwell, B. (2021). Machine learning to determine the main factors affecting creep rates in laser powder bed fusion. Journal of Intelligent Manufacturing, 32(8), 2353–2373. https://doi.org/10.1007/s10845-021-01785-0

There is an increasing need for the use of additive manufacturing (AM) to produce improved critical application engineering components. However, the materials manufactured using AM perform well below their traditionally manufactured counterparts, par... Read More about Machine learning to determine the main factors affecting creep rates in laser powder bed fusion.

Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management (2020)
Journal Article
Rengasamy, D., Jafari, M., Rothwell, B., Chen, X., & Figueredo, G. P. (2021). Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management. Sensors, 20(3), https://doi.org/10.3390/s20030723

Deep learning has been employed to prognostic and health management of automotive and aerospace with promising results. Literature in this area has revealed that most contributions regarding deep learning is largely focused on the model’s architectur... Read More about Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management.

Deep learning approaches to aircraft maintenance, repair and overhaul: a review (2018)
Conference Proceeding
Rengasami, D., Morvan, H., & Patrocinio Figueredo, G. (2018). Deep learning approaches to aircraft maintenance, repair and overhaul: a review. In 21st IEEE International Conference on Intelligent Transportation Systemshttps://doi.org/10.1109/ITSC.2018.8569502

The use of sensor technology constantly gathering aircrafts' status data has promoted the rapid development of data-driven solutions in aerospace engineering. These methods assist, for instance, with determining appropriate actions for aircraft maint... Read More about Deep learning approaches to aircraft maintenance, repair and overhaul: a review.