In-situ optimization of thermoset composite additive manufacturing via deep learning and computer vision

With the advent of extrusion additive manufacturing (AM), fabrication of high-performance thermoset composites without the need of tooling has become a reality. However, finding an optimal set of printing parameters for these thermoset composites during extrusion requires tedious experimentation as composite ink properties can vary significantly with respect to environmental parameters such as temperature and relative humidity. Addressing this challenge, this study presents a novel optimization framework that utilizes computer vision and deep learning (DL) to optimize the calibration and printing processes of thermoset composite AM. Unlike traditional DL models where printing parameters are determined prior to printing, our proposed framework dynamically and autonomously adjusts the printing parameters during extrusion. A novel DL integrated extrusion AM system is developed to determine the optimal printing parameters including print speed, road width, and layer height for a given composite ink. This closed loop system is consisted of a computer communicating with an extrusion AM system, a camera to perform in-situ imaging and several high accuracy convolution neural networks (CNNs) selecting the ideal process parameters for composite AM. The results show that our proposed process optimization framework was able to autonomously determine these parameters for a carbon fiber-composite ink. Consequently, specimens with complex geometries could be fabricated without visible defects and with maximum fiber alignment and thus enhancing the mechanical performance of the specimen’s composite material. Moreover, our proposed framework minimizes a labor-intensive procedure required to additively manufacture thermoset composites by optimizing the extrusion process without any user intervention.