Research in the Jin’s Research Laboratory is aimed at developing models for analyzing, assessing the performance and health conditions of engineering assets in smart manufacturing systems and complex engineering systems, and providing predictive and adaptive decision support tools for optimal operations, maintenance and control. Focus areas include:

– Predictive analytics for advanced manufacturing processes and systems
– Machine diagnostics and prognostics
– Sensor data analytics
– Physics-informed machine learning
– Roll-to-roll process for flexible electronics printing
– High-precision manufacturing processes

Resiliency of Mutualistic Supplier-Manufacturer Networks

Mengkai Xu, Srinivasan Radhakrishnan, Sagar Kamarthi, and Xiaoning Jin*

Current Supplier-Manufacturer (SM) networks are highly complex and susceptible to local and global disruptions, due to connectivity and interdependency among suppliers and manufacturers. Resiliency of supply chains is critical for organizations to remain operational in the face of disruptive events. In this work we investigate resiliency of SM networks using the quantitative methods employed to study mutualistic ecological systems. We create a bipartite representation and generate a multidimensional nonlinear model that captures the dynamics of a SM network and predicts the point of collapse. We extensively validate the model using real-world global automotive SM networks. The current work offers a means for designing resilient supply chains that can remain robust to local and global perturbations. An interactive visualization tool of the SM network and its resilience analysis has been developed by Capstone Project team. See https://rainbowfalcons.herokuapp.com/.

 Roll-to-Roll Process Monitoring and Diagnostics

Huanyi Shui, Xiaoning Jin, Jun Ni

Roll-to-Roll (R2R) manufacturing has emerged as one of the most cost-effective manufacturing techniques for high-volume production of flexible printing electronics, thin-film batteries and solar cells, as opposed to low-volume batch production. However, in practice, real-time monitoring of the R2R process and in-situ measurement of fabricated materials during production are challenging because of the high speed and dynamic variation of the web.  This paper aims to develop analytical models that can serve as virtual sensing and metrology tools to quantify process state variation and estimate product quality in R2R processes with limited accessibility of in-situ sensor.

NARNET-Based Prognostic Modeling

Anqi He, Xiaoning Jin

This paper presents a new prognostics modeling method based on a nonlinear autoregressive neural network (NARNET) for computing the remaining useful life (RUL) of a deteriorating system under dynamic operating conditions. We particularly investigate how the degradation process is affected by the unit-specific operating conditions. The operating conditions are forecasted by a NARNET model based on the unit’s historical operating conditions. We show that the prognostics model integrating the operating condition forecast provides more accurate and efficient RUL prediction.

Stochastically-dependent Multi-component Degradation and Failure

Mengkai Xu, Xiaoning Jin, Sagar Kamarthi, Md. Noor-E-Alam

Unexpected component failures in a mechanical system always cause loss of performance and functionality of the entire system. Condition based maintenance decisions for a multi-component mechanical system are challenging because the interdependence of individual components’ degradation is not fully understood and lack of physical models.  An extended proportional hazard model (PHM) is developed to characterize the failure dependence and estimate the influence of degradation state of one component on the hazard rate of another. An optimization model is developed to determine the optimal hazard-based threshold for a two-component repairable system.

Graduate Students

No funded graduate student positions are available at the moment, although applications are always welcome.

Post-docs

One post-doctoral position is currently available, please contact Prof. Jin for details.

Visiting  Students/Scholars

Interested students/scholars from other institutions are welcome to contact Prof. Jin by email for the position of visiting students.