Automated Composite Manufacturing

随着复合材料在飞机和汽车结构中的应用越来越多，自动化复合材料制造技术已经变得必不可少，并引起了工业界的极大兴趣。特别是，与传统制造技术相比，自动纤维放置（AFP）工艺为大规模复杂复合材料结构的制造提供了一种新的方法。AFP是一种将复合胶带一层一层地铺在工具表面的工艺，可用于制造热固性和热塑性复合材料。与传统工艺相比，AFP工艺具有减少材料浪费、提高沉积速度等优点。与热固性复合材料相比，热塑性复合材料由于其优越的性能，特别是在疲劳和冲击问题、无限保质期、可焊性、韧性、耐化学性等方面，受到了特别的关注。热塑性复合材料的一个独特优势是原位固结的可能性，可以单独使用AFP工艺来实现，从而避免了二次加工，例如高压灭菌器处理，从而节省了大量的制造成本/能源。此外，热塑性材料基体可循环利用的可能性将使热塑性复合材料进入绿色技术领域。阻碍热塑性复合材料广泛应用的两个主要挑战是原位固结层合板的质量和工艺的吞吐量。该建议侧重于通过引入创新干预措施和模拟AFP原位固结过程来提高质量和吞吐量。第一个干预措施是将振荡运动结合到AFP压路机的机制中，以改善剪切混合并增强原位固结复合材料层之间的结合。第二种提高粘接强度和质量的方法是在原位制造之前向AFP胶带中添加额外的聚合物。这些干预措施的效果将在本研究项目中进行系统研究。此外，该提案旨在利用人工智能（AI）和机器学习（ML）技术的最新发展。我们将首次开发基于实验数据的原位AFP工艺数据驱动模型，能够考虑影响工艺质量的大量输入。这个数据驱动的模型以后可以用于流程优化和控制，以增加吞吐量和改进质量。在这个研究项目的过程中，预期的技术知识和开发的模型将对热塑性复合材料制造技术产生重大影响，并将提高加拿大复合材料工业的竞争力。在这个项目中，对学生的培训是至关重要的，这将有助于解决先进复合材料制造专业工程师的短缺问题。

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.