Prognostics and health management refers to a discipline that predicts the health condition and remaining useful life (RUL) of engineering systems using signals generated by sensors. Classical prognostic approaches fall into two categories: model-based and data-driven prognostics. Our research aims to develop novel data-driven methods and tools for PHM that enable manufacturers to predict the occurrence of failures as well as the RUL of engineering systems.
Smart manufacturing aims to integrate big data, advanced analytics, high performance computing, and Industrial Internet of Things (IIoT) into traditional manufacturing systems and processes to create highly customizable products with higher quality at lower costs. Our research aims to develop interoperable information and communications technologies, real-time system monitoring and data acquisition systems that enable manufacturers to monitor the health conditions of machinery.
Additive manufacturing, also known as 3D printing, refers to manufacturing processes that create 3D objects in which layers of materials are built under computer control to fabricate parts and components. Our research aims to develop novel methods that can improve the quality of 3D printed parts and the reliability of additive manufacturing processes.