The future of emerging smart manufacturing technologies is undoubtedly affected by the innovations in manufacturing processes and next-generation materials manufacture and characterization together with data science and AI. Towards this, my research presents innovations in two key areas:

1. Intelligent sensing for rapid characterization: My research has demonstrated that HM platforms, along with high-resolution acoustic emission sensors, could be employed to study the material characteristics. Processes such as machining/scratching allow us to probe into the “rich”, mostly unexplored material structure.
2. Surface quality and integrity assurance of AM components: Additive manufacturing limited in terms of controlling the surface morphologies to less than a few micrometers. My research focuses on developing advanced post-processing strategies, both from experimental as well as analytical standpoint, to address questions such as finishing complex surfaces, effect of different post-processing strategies on the surface integrity, and modeling the evolution of surface and microstructure during various post-processing steps.

Streaming data analytics forms the backbone of smart manufacturing systems to facilitate “real-time” decision-making.

For time series data, my research focused on harnessing the signal phase information (mostly neglected in the current literature) from noisy and transient streaming data via intrinsic time-scale decomposition and phase synchronization for fast and causal detection of process anomalies and change points.

For streaming image data, we developed an unsupervised image segmentation approach, with statistical consistency, by iteratively identifying the optimal graph cut and learning the parameters of the graph cut via maximum a posteriori estimation.

Most, if not all manufacturing processes involve some aspect of optimizing certain "objectives" of interest, e.g., increasing the material strength. Traditional solutions are either based on passive experimental design approaches or sometimes just on past experience. Accelerating the experimental process by adaptively updating the experimental design is the key to reducing the cycle time and material costs.

My research focuses on active learning models and approaches to integrate the knowledge of the process physics into enabling high fidelity search in the state space. On the methodological side, we will focus on the challenges emerging from the process uncertainty where it may not be feasible to guarantee strict improvements. Under such circumstances, how can we generate solutions that are correct with some “confidence”, i.e., Probably Approximately Correct. Applications include: identifying optimal process parameters in additive manufacturing, discovery of materials with novel properties, etc.