Intelligent composites forming - simulations for faster, higher quality manufacture

This project aims to explore an intelligent way to optimize and accelerate our computer assistant modelling tool which simulates the textile forming process. A machine learning (ML) based surrogate model is being developed, which aims to provide live prediction to fabric forming industry. This surrogate model is trained by a set of data generated by finite element (FE) simulation tool. Considering the high calculation cost when obtaining an accurate FE simulation data point, the method of sampling and supplementary point selecting should be well designed, in order to make the training cost as low as possible meanwhile control the predicting deviations.
First of all, in the aspect of FE simulation, a way to the destination of reducing defect level in dry textile forming process is explored. A shell-membrane hybrid FE modelling tool is adopted to simulate the behaviour of textile during forming on an industry-inspired tool. A series of springs or other controlling method will be adopted to adjust the forming controls in the FE model, in order to simulate the different wrinkling and bridging level under different forming parameters. In current research, the positions and stiffnesses, together with the pressure applied on the top, are regarded as input parameters and can be modified to control the deformation of textile during forming. By providing a set of combinations of input parameters, hundreds of simulations will be conducted to obtain a data set, which will be used as the training set for surrogate model.
The long-term research will explore the feasibility of the framework of using ML-based time-efficient surrogate model to assist forming process optimization. In the current work, the Gaussian Process Regression (GPR) method is used to develop the surrogate model, for its applicability on small data set problem. On the other hand, GPR method naturally features uncertainty quantification ability, which can be used to predict and quantify the potential variances in the forming process.

This method will be tested and developed as a tool for our industry partners, which is expected to greatly reduce forming defects as well as shorten the parameter test period.
With the maturation of this surrogate model, the model together with the entire method can be compiled into software and integrated in forming process equipment in the future. With the use of sensors, real-time parameters such as the local temperature, tensile force, and shear angle of fabrics and moulds can be detected and collected during the process. By importing these real-time data into the surrogate model, the software can calculate the point with the highest probability of producing the optimal result, so that the forming rig can fine-tune the control parameters to optimize the quality of the forming.
This technology can not only be applied in the forming process. In the various steps of composites part production, such as compaction, curing or AFP layup, this technology can be used for real-time optimization of processing quality. It can be said that this technology is an important way for the intelligent upgrading of the manufacturing industry.

The project will explore the possibility of applying artificial intelligence and machine learning methods to optimization of composites fabric forming process, and furthermore, other complex manufacturing process. In the context of the rapid development of machine learning theories and computing capabilities, the use of artificial intelligence for process optimization will be a major trend in the development of the manufacturing industry in the future. This will help the country's manufacturing level to continue to maintain its leading position in the world and bring a lot of benefits.

There are seven principal groups of beneficiaries for our new EPSRC Centre for Doctoral Training in Composites Science, Engineering, and Manufacturing.

1. Collaborating companies and organisations, who will gain privileged access to the unique concentration of research training and skills available within the CDT, through active participation in doctoral research projects. In the Centre we will explore innovative ideas, in conjunction with industrial partners, international partners, and other associated groups (CLF, Catapults). Showcase events, such as our annual conference, will offer opportunities to a much broader spectrum of potentially collaborating companies and other organisations. The supporting companies will benefit from cross-sector learning opportunities and

- specific innovations within their sponsored project that make a significant impact on the company;
- increased collaboration with academia;
- the development of blue-skies and long-term research at a lowered risk.

2. Early-stage investors, who will gain access to commercial opportunities that have been validated through proof-of-concept, through our NCC-led technology pull-through programme.

3. Academics within Bristol, across a diverse range of disciplines, and at other universities associated with Bristol through the Manufacturing Hub, will benefit from collaborative research and exploitation opportunities in our CDT. International visits made possible by the Centre will undoubtedly lead to a wider spectrum of research training and exploitation collaborations.

4. Research students will establish their reputations as part of the CDT. Training and experiences within the Centre will increase their awareness of wider and contextually important issues, such as IP identification, commercialisation opportunities, and engagement with the public.

5. Students at the partner universities (SFI - Limerick) and other institutions, who will benefit from the collaborative training environment through the technologically relevant feedback from commercial stakeholder organisations.

6. The University of Bristol will enhance their international profile in composites. In addition to the immediate gains such as high quality academic publications and conference presentations during the course of the Centre, the University gains from the collaboration with industry that will continue long after the participants graduate. This is shown by the

a) Follow-on research activities in related areas.
b) Willingness of past graduates to:

i) Act as advocates for the CDT through our alumni association;
ii) Participate in the Advisory Board of our proposed CDT;
iii) Act as mentors to current doctoral students.

7. Citizens of the UK. We have identified key fields in composites science, engineering and manufacturing technology which are of current strategic importance to the country and will demonstrate the route by which these fields will impact our lives. Our current CDTs have shown considerable impact on industry (e.g. Rolls Royce). Our proposed centre will continue to give this benefit. We have built activities into the CDT programme to develop wider competences of the students in:

a) Communication - presentations, videos, journal paper, workshops;
b) Exploitation - business plans and exploitation routes for research;
c) Public Understanding - science ambassador, schools events, website.