Category: Research

by Siyuan Chen, Stephen Hallett and Jonathan Belnoue.

As the demand for carbon fibre-reinforced composites structures is rapidly growing, the industrial community continues to seek new manufacturing technologies that are low-defect, low-cost, highly efficient and environmental-friendly. Liquid molding is regarded as a cheaper alternative to the traditional prepreg/autoclave approach, however, the latter is often the favoured manufacturing route in the aerospace sector (where safety is paramount) as it allows for the production of better quality parts. One of the many challenges with infusion is the high deformability of the dry fibrous precursor materials that exposes the final structure to risks of defects and part to part variability. If the material and process (including their variabilities) are not controlled to a sufficient level, meeting design tolerances can prove challenging. These risks are traditionally mitigated through “over design” but this reduces a lot of the lightweighting advantages of using composites. 

At BCI, we explore the possibility of achieving reduced-defect forming processes in 3 different ways. Firstly, design tools accounting for manufacturing constraint that are faster than current methods available commercially are being developed [1, 2]. These tools can be used to run moderate numbers (i.e., up to a 100) of simulations and allow to explore the impact of different combination of process control parameters on final part quality. This provide the possibility of optimising the forming process. The robustness of the optimisation is then improved by building Gaussian process (GP) emulator using the dataset produced using our fast simulation tools [3]. These GP emulators can achieve a good accuracy (error < 10%) and model the impact of several input parameters by running only tens of simulations. By introducing dimension reduction and active learning algorithms, the emulators can be expended to much more complex processes with over 10 input parameters [4]. After being trained, the emulators can also provide immediate predictions for final part quality. This open the door for digital twinning where in-process sensing and real-time simulation are combined. Thus, although forming processes can be optimised using deterministic FE simulations, real-world cases are affected by lots of factors such as material and process variability. Some of these variabilities are difficult to avoid but can be measured. Quantifying them (e.g., fibre direction misalignment, tow waviness, etc), a feedback loop whereby real-time simulations are informed by live data of the process and used to adapt the manufacturing condition to improve the final part quality can be set. A forming test cell instrumented with a stereo imaging system is currently being built in our labs. This will be used to construct a prototype digital twin for forming process. 

To summarise, in our vision right first-time design and manufacture of composites can be achieved through a combination of (physics-based) digital design accounting for manufacturing constraints, fast process optimisation using data generated from process models and self-adapting manufacturing hardware controlled through emulators build from process models. 

[1] Composites: Made Faster – Rapid, physics-based simulation tools for composite manufacture (ukri.org) 

[2] JPH Belnoue, SR Hallett A rapid multi-scale design tool for the prediction of wrinkle defect formation in composite components, Materials & Design, 2020. 

[3] S. Chen, A.J. Thompson, T.J. Dodwell, S.R. Hallett, J.P.-H. Belnoue, Fast optimisation of the formability of dry fabric preforms: A Bayesian approach, Materials & Design, 230:111986, 2023. 

[4] S. Chen, A.J. Thompson, T. J. Dodwell, S.R. Hallett, J. P.-H. Belnoue. A Bayesian surrogate framework for the optimisation of high-dimensional composites forming process. In 5th International Conference on Uncertainty Quantification in Computational Science and Engineering, 2023. 

Compliant materials and slender structures are susceptible to a variety of different instabilities under external stimulus or loading. Traditionally, these instabilities are avoided and classified as failure modes. In recent years, researchers at the BCI have instead attempted to use complex nonlinear behaviour for novel functionality. Simply put, if nonlinearities are understood, then they can be exploited to create well-behaved nonlinear structures. In a recent publication in Physical Review B [1], we employed ‘active modal nudging’ as a novel actuation mechanism for soft robots. In essence, we programmed a soft metamaterial to shape-shift in a rapid and energetically efficient manner by employing embedded actuation to switch between different stable post-buckled modes. 

Our work focused on a latticed metamaterial consisting of an elastomeric matrix with a 3 by 3 square array of circular holes, as shown in Figure 1. We discovered that this metamaterial has three stable post-buckling modes under pure compression, i.e. two sheared modes (sheared left and sheared right) and one symmetric polarised mode. The metamaterial was programmed to favour one of the sheared modes under axial compression via modal nudging [2]. An actuator was then embedded within the central hole to trigger a mode switch between the favoured sheared mode and the polarised mode. We demonstrated that this combination of active and passive nudging is more energetically efficient and requires smaller actuation force than the more widely used global actuation method, as shown in Figure 1(a). By toggling the metamaterial between the sheared and polarised state, we were able to make the metamaterial crawl. The effective locomotion could be employed in soft robotics systems (Figure 2). 

While in this study, we consider a specific type of soft metamaterial and a specific application, the design paradigm introduced can be extended to other scenarios where energetically efficient shape shifting may be beneficial, such as lightweight adaptive wing structures or adaptive façade and ventilation systems for net-zero buildings. 

Figure 1 The actuation force–displacement curve of the lattice metamaterial to achieve a us/L = 0.20 shear displacement amplitude, using: (a) increasing compression from the pre-buckling state; and (b) active nudging from the symmetric deformation mode. 

Figure 2 (a) A typical actuation cycle for the robot. Yellow and red lines are the reference line indicating the initial and final positions of the right and left edges. The yellow arrows represent the motion of the fixture within the step. (b) The location of the crawling robot in the initial state, after four and eight iterations. A movie of the movement of the demo robot can be found in https://journals.aps.org/prb/supplemental/10.1103/PhysRevB.107.214103/DemoCrawlingRobot.mp4 

References: 

[1] Shen, J., Garrad, M., Zhang, Q., Leao, O., Pirrera, A., & R. M. J. (2023). Active reconfiguration of multistable metamaterials for linear locomotion. Physical Review B, 107(21), 214103. 

[2] Cox, B. S., Groh, R. M. J., Avitabile, D., & Pirrera, A. (2018). Modal nudging in nonlinear elasticity: tailoring the elastic post-buckling behaviour of engineering structures. Journal of the Mechanics and Physics of Solids, 116, 135-149.