Composites: Made Faster - Rapid, physics-based simulation tools for composite manufacture

复合材料：制造得更快 - 用于复合材料制造的快速、基于物理的模拟工具

• 批准号: EP/V039210/1
• 负责人: Stephen Hallett
• 金额: $ 103.56万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV039210%2F1
• 关键词: Composites; Made; Faster; Rapid; physics

Composite materials are becoming increasingly important for light-weight solutions in the transport and energy sectors. Reduced structural weight, with improved mechanical performance is essential to achieve aerospace and automotive's sustainability objectives, through reduced fuel-burn, as well as facilitating new technologies such as electric and hydrogen fuels. The nature of fibre reinforced composite materials however makes them highly susceptible to variation during the different stages of their manufacture. This can result in significant reductions in their mechanical performance and design tolerances not being met, reducing their weight saving advantages through requiring "over design". Modelling methods able to simulate the different processes involved in composite manufacture offer a powerful tool to help mitigate these issues early in the design stage. A major challenge in achieving good simulations is to consider the variability, inherent to both the material and the manufacturing processes, so that the statistical spread of possible outcomes is considered rather than a single deterministic result. To achieve this, a probabilistic modelling framework is required, which necessitates rapid numerical tools for modelling each step in the composite manufacturing process.Focussing specifically on textile composites, this project will develop a new bespoke solver, with methods to simulate preform creation, preform deposition and finally, preform compaction, three key steps of the composite manufacturing process. Aided by new and developing processor architectures, this bespoke solver will deliver a uniquely fast, yet accurate simulation capability. The methods developed for each process will be interrogated through systematic probabilistic sensitivity analyses to reduce their complexity while retaining their predictive capability. The aim being to find a balance between predictive capability and run-time efficiency. This will ultimately provide a tool that is numerically efficient enough to run sufficient iterations to capture the significant stochastic variation present in each of the textile composite manufacturing processes, even at large, component scale.The framework will then be applied to industrially relevant problems. Accounting for real-world variability, the tools will be used to optimise the processes for use in design and to further to explore the optimising of manufacturing processes.Close collaboration with the project's industrial partners and access to their demonstrator and production manufacturing data will ensure that the tools created are industry relevant and can be integrated within current design processes to achieve immediate impact. This will enable a step change in manufacturing engineers' ability to reach an acceptable solution with significantly fewer trials, less waste and faster time to market, contributing to the digital revolution that is now taking place in industry.

复合材料对于运输和能源领域的轻量化解决方案越来越重要。降低结构重量，提高机械性能，通过减少燃料消耗以及促进新技术（如电燃料和氢燃料）的发展，对于实现航空航天和汽车的可持续发展目标至关重要。然而，纤维增强复合材料的性质使得它们在其制造的不同阶段期间极易受到变化的影响。这可能导致其机械性能和设计公差的显著降低，通过要求“过度设计”而降低其重量节省优势。能够模拟复合材料制造中所涉及的不同过程的建模方法提供了一个强大的工具，以帮助在设计阶段的早期缓解这些问题。实现良好模拟的一个主要挑战是考虑材料和制造过程固有的可变性，以便考虑可能结果的统计分布，而不是单个确定性结果。为了实现这一目标，需要一个概率建模框架，这就需要快速的数值工具来模拟复合材料制造过程中的每一个步骤。该项目将专门针对纺织复合材料开发一个新的定制求解器，该求解器将模拟复合材料制造过程中的三个关键步骤：预制件创建、预制件沉积和最终预制件压实。在新的和不断发展的处理器架构的帮助下，这个定制的求解器将提供一个独特的快速，但准确的模拟能力。将通过系统的概率敏感性分析来询问为每个过程开发的方法，以降低其复杂性，同时保留其预测能力。目标是在预测能力和运行时效率之间找到平衡。这将最终提供一个工具，是数值上足够有效的运行足够的迭代，以捕捉显着的随机变化存在于每个纺织复合材料的制造过程中，即使在大的，组件scale.The框架将被应用到工业相关的问题。考虑到现实世界的可变性，这些工具将用于优化设计过程，并进一步探索制造过程的优化。与项目的工业合作伙伴密切合作，并访问他们的演示和生产制造数据，将确保创建的工具与行业相关，并可以集成到当前的设计过程中，以实现即时影响。这将使制造工程师能够以更少的试验、更少的浪费和更快的上市时间来获得可接受的解决方案，从而为目前正在工业中发生的数字革命做出贡献。

项目成果

That's how the preform crumples: Wrinkle creation during forming of thick binder-stabilised stacks of non-crimp fabrics

这就是预成型件起皱的方式：在厚的粘合剂稳定的无卷曲织物堆叠成型过程中产生皱纹

• DOI: 10.1016/j.compositesb.2024.111269
• 发表时间: 2024
• 期刊: Engineering
• 影响因子: 12.8
• 作者: Broberg P

Automatic ply detection and finite element model generation for composite laminates

复合材料层压板的自动层数检测和有限元模型生成

• DOI: 10.1177/26349833231178331
• 发表时间: 2023
• 期刊: Composites and Advanced Materials
• 影响因子: 1.6
• 作者: Lau P

Thickness Control of Autoclave-Molded Composite Laminates

热压罐成型复合材料层压板的厚度控制

• DOI: 10.1115/1.4062581
• 发表时间: 2023
• 期刊: Journal of Manufacturing Science and Engineering
• 作者: Gongadze E

Fast optimisation of the formability of dry fabric preforms: A Bayesian approach

快速优化干织物预成型件的成型性：贝叶斯方法

• DOI: 10.1016/j.matdes.2023.111986
• 发表时间: 2023
• 期刊: Materials & Design
• 影响因子: 8.4
• 作者: Chen S

An accurate forming model for capturing the nonlinear material behaviour of multilayered binder-stabilised fabrics and predicting fibre wrinkling

精确的成型模型，用于捕获多层粘合剂稳定织物的非线性材料行为并预测纤维起皱

• DOI: 10.1016/j.compositesb.2024.111268
• 发表时间: 2024
• 期刊: Engineering
• 影响因子: 12.8
• 作者: Broberg P