Automated Composite Manufacturing

随着复合材料在飞机和汽车结构中的应用越来越广泛，复合材料自动化制造技术已经成为必不可少的技术，并引起了工业界的极大兴趣。特别是，与传统制造技术相比，自动纤维贴装(AFP)工艺为制造大型复杂复合材料结构提供了一种新的方法。AFP是一种将复合胶带以一层一层的方式铺设在工具表面上的工艺，可用于制造热固性和热塑性复合材料。与传统技术相比，AFP工艺具有许多优势，如减少材料浪费和提高沉积速度等。与热固性复合材料相比，热塑性复合材料因其优越的性能而受到特别关注，特别是在疲劳和冲击问题、无限保质期、焊接性、韧性和耐化学性等方面。热塑性复合材料的一个独特优势是可以实现原位固化，仅使用AFP工艺即可实现原位固化，从而避免了二次工艺，如高压灭菌处理，从而显著节省了制造成本/能源。此外，回收热塑性塑料基质的可能性将对绿色技术领域的热塑性复合材料进行分类。阻碍热塑性复合材料广泛应用的两个主要挑战是AFP原位固结层压板的质量和工艺的生产能力。这项提案的重点是通过引入创新的干预措施和模拟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辊的机制中，以改善剪切混合并增强原位固化复合材料层之间的粘结。为提高粘结强度和质量而提出的第二种干预措施是在原位制造之前向AFP胶带中添加额外数量的聚合物。这些干预措施的效果将在本研究计划中进行系统研究。此外，这项建议旨在利用人工智能(AI)和机器学习(ML)技术的最新发展。我们将首次在实验数据的基础上开发一个数据驱动的原位AFP过程模型，能够考虑影响过程质量的大量输入。这种数据驱动的模型可以在以后用于流程优化和控制，以增加产量和改善质量。在本研究过程中开发的预期技术知识和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行业的竞争力。该项目期间的学生培训至关重要，将有助于解决先进复合材料制造方面的工程师短缺问题。

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.