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College of Engineering and Computing

  • AFP prediction

Harik pushes manufacturing of advanced composite structures with NASA grant

Automated Fiber Placement (AFP) is one of the most complex processes for manufacturing compositive structures, which are lightweight and have greater strength to weight ratio than metals and used to build advanced air vehicles. Efficient manufacturing and new process planning techniques for AFP are necessary for composite manufacturing of lightweight structures to support NASA projects, such as the Advanced Air Transport Technology and the Hi-Rate Composites Aircraft Manufacturing project

Mechanical Engineering Associate Professor Ramy Harik and his research team began work this past January that will include tests to determine if capabilities exist for proposing new composite structures by incorporating several lifecycle attributes, such as design and manufacturing based on a new framework.

Harik’s research is funded by a three-year, $1.1 million NASA Established Program to Stimulate Competitive Research (EPSCoR) grant. There are also partnerships from the University of South Carolina’s Artificial Intelligence Institute, Clemson University’s Composites Center, and three NASA Centers: NASA Langley Research Center, Marshall Space Flight Center and Kennedy Space Center. The goal of the will be to highlight hybrid, physics-based and data-driven AFP research to drive manufacturing for innovation in South Carolina and help make the state a leader in creating advanced composites for nationwide use.

“The best example of composite manufacturing is the Boeing 787 Dreamliner, which is manufactured in South Carolina using AFP. New manufacturing techniques for such aircrafts are on composites manufacturing. Usually, you put sheet metal around the structural parts, but with a composite plane, its strength comes from the composite tube itself,” Harik says.

Since 2014, Harik has been worked on research to uncover composite materials properties, how they would behave when processed and manufactured, and modeling that behavior in the AFP process, which is derived from a physics-based approach. But he eventually realized that the physics-based approach would never allow him to understand the material properties and behavior. At the same time, his research team began working on AFP data modeling that was more focused on inspection instead of manufacturing. The goal was to determine how to use data-driven modeling techniques to leverage inspection knowledge once the AFP manufactured part was examined. 

“While it taught us that data-driven modeling will give us a portion of the truth, it will always need to be grounded somehow,” Harik says. “The idea was to try to tackle this problem in a hybrid approach by taking the knowledge gained from our fundamental understanding of the physics-based process and combine it with data-driven modeling to come up with something that will hopefully be able to get to a model that actually defines how the material will behave along with the manufacturing process.”

Harik’s research team aims to create tools for enabling a connection between the physics and data-driven models for optimizing the manufacturing process. 

“We are trying to create a new framework for AFP manufacturing. It will basically enable new composite structures that integrate design, manufacturing and inspection into all of our visions, especially expanding the population of composite designers and manufacturers, which eventually expands the usage of composites and creates more knowledge,” Harik says. 

Harik admits that it will be difficult for physics-based models to provide accurate representations of the complex AFP process, combined with generalization of defects for assessing physics and data-driven methods. The research includes a full assessment of physics, muti-physics and data-driven models, including geometrical, analytical and material properties models. Each model will provide various information for predicting composite behavior during the design, manufacturing and curing (hardening) phases to allow the research team to select and combine applicable models.

“The physics-based model is where we're going to try to assess different elements and how they connect with each other and how these models can be integrated. This will give us prediction capabilities of the behavior of the material when we are manufacturing,” Harik says.

Graduate research assistant Alex Brasington is currently pursuing a Ph.D. in mechanical engineering and worked for a semester at NASA Langley. He says the current issue with AFP is that there are two sides with different objectives. The modeling side is physics-based, while the manufacturing side is only interested in data and the final product. 

“We're trying to take the data from physics models and the process and merge them into one model. There are four areas of AFP: design, process planning, manufacturing and inspection. Part of our goal is to create an overall flow where you accumulate data from multiple manufacturing trial, and all the information influences your next part and process,” Brasington says. “It will connect all the data across the whole process instead of having someone design the part and someone else manufacture it without talking to one another.”

This research will focus on the aerospace sector to improve the manufacturing process and throughput, which is the number of fully manufactured planes that leave the factory. Since the current throughput is not close to meeting demand, the latest estimate is a four-to-six times increase in the number of planes leaving the factory. 

“It’s currently all about metal wings and fuselage, but composite materials are the newer processes. They need to make newer airplanes with more efficient processes,” Brasington says. “We want to focus on not only improving the life of the aircraft but the overall process of designing, manufacturing and certification. Our goal is to efficiently get something out of the door and in use.”

By the end of the project, Harik’s team intends to implement smart AFP concepts and integrate Internet of Things and data analytics to enable higher fidelity and traceability for the manufacturing process. In addition, the formulation of a hybrid physics and data-driven model that incorporates tool, material, process and environmental properties can provide a recommendation of a best set of process parameters to minimize and reduce the occurrence of the anticipated defects. 

“The end goal is to have a functional cyber physical system that can enhance the composite manufacturing process. We plan to tie in the domains of design, process planning, manufacturing and inspection,” Harik says. “We're hoping for a smart AFP process that integrates all of the new tools so we can actually have the optimized process parameters and a better AFP structure.” 

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