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Machine learning applied to optical fiber production control systems




2: Mechanical Engineering

Faculty Supervisor:

Brian Anthony

Faculty email:


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Project Description

The goal of the project is to develop a data driven model of the fiber draw manufacturing process and to simulate new fiber draw control strategies. You will be supplied with production data from multiple draw towers and specification(s) of the drawing process. You will develop a full simulation of the fiber draw process taking into account and modeling the control system as currently implemented. You won’t develop models from complete first principles; feedback loop structures will serve as constraints on the data analysis learning algorithms. You will learn an aggregate model of the full system. You will develop a realistic virtual model of the full controllers and system as implemented – to use as a simulation tool. Then, in simulation, you will modify the controllers in order to predict how a new control setting or system will perform. The simulation will serve as a design and decision tool to guide experiments and deployment on real draw towers. Impact: This project is of interest to fiber draw processing and other continuous production systems.


1. This project will utilize machine learning and data analytics, regression and autoregression, data visualization, process modeling, and hybrid modeling skills. Background/interest in these skills are helpful and students can expect to enhance these skills through the project. 2. This project is offered as part of the MechE Alliance industry connected ELO cohorts. Applicants will be expected to participate in the cohort program to be eligible for the position. More information can be found in this Google Doc: https://drive.google.com/file/d/1YbGwDXGAvPDSN6DEDGBw-VDJWS7jnp65/view?usp=sharing This is a remote UROP opportunity.