Learning Controllers for Adaptive Spreading of Carbon Fiber Tows

Abstract

Carbon fiber reinforced polymers (CFRP) are lightweight but strong composite materials designed to reduce the weight of aerospace or automotive components -contributing to reduced greenhouse gas emissions. A common manufacturing process for carbon fiber tapes consists of aligning tows (bundles of carbon fiber filaments) side by side to form tapes via a spreading machine. Tows are pulled across metallic spreading bars that are conventionally kept in a fixed position. That can lead to high variations in quality metrics such as tape width or height. Alternatively, one could try to control the spreading bars based on the incoming tows' profiles. We investigate whether a machine learning approach, consisting of a supervised process model trained on real data and a process control model to choose adequate spreading bar positions, is able to improve the tape quality variations. Our results indicate promising tendencies for adaptive tow spreading.