Automated Composite Manufacturing

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With increasing use of composites in aircraft and automotive structures, automated composite manufacturing techniques have become essential and are attracting much interest from industry. In particular, the Automated Fiber Placement (AFP) process provides a new approach to the manufacturing of large-scale complex composite structures in comparison to traditional manufacturing techniques. AFP is a process in which composite tapes are laid onto a tool surface in a layer-by-layer manner and can be used in manufacturing both thermoset and thermoplastic composites. The AFP process has many advantages over traditional techniques such as reducing material waste and increasing rate of deposition etc. Thermoplastic composites, in comparison with thermoset composites, are of special interest due to their superior properties, especially with regards to fatigue and impact issues, infinite shelf life, weldability, toughness, chemical resistance etc. A unique advantage of thermoplastic composites is the possibility of in-situ consolidation that can be achieved using the AFP process alone thus avoiding secondary processes such as autoclave treatments which leads to significant manufacturing cost/energy savings. In addition, the possibility of recycling thermoplastic matrix would categorize thermoplastic composites in the field of green technologies. Two main challenges that impede the wide application of thermoplastic composites are the quality of AFP in-situ consolidated laminates and the throughput of the process. This proposal focuses on quality and throughput improvements via introducing innovative interventions and simulation of AFP in-situ consolidation process. The first intervention is incorporating oscillatory motion to the AFP roller's mechanism to improve shear mixing and enhance bonding between in-situ consolidated composite layers. The second proposed intervention to improve bond strength and quality is adding extra amount of polymer to the AFP tape prior to the in-situ manufacturing. The effect of these interventions will be systematically studied in this research program. Furthermore, this proposal aims to take advantage of recent development of Artificial Intelligence (AI) and Machine Learning (ML) techniques. For the first time, we will develop a data-driven model of in-situ AFP process based on experimental data, capable of considering large number of inputs affecting the quality of the process. This data-driven model can be later exploited for process optimization and control to increase throughput and improve quality. The anticipated technical knowledge and the model developed during the course of this research program will have a big impact on the thermoplastic composites manufacturing technology and will improve Canadian composite industries' competitiveness. Student training during this program is of utmost importance and will help to address the shortage of engineers with expertise in advanced composite manufacturing.