Projects
Projects: Custom Search |
||
| Reference Number | UKRI221 | |
| Title | AI-driven Design for Forming High-Performance Vehicle Parts | |
| Status | Started | |
| Energy Categories | Energy Efficiency (Transport) 40%; Not Energy Related 60%; |
|
| Research Types | Basic and strategic applied research 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 50%; ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering) 50%; |
|
| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Nan Li Imperial College London |
|
| Award Type | Standard | |
| Funding Source | EPSRC | |
| Start Date | 01 October 2025 | |
| End Date | 01 October 2028 | |
| Duration | 36 months | |
| Total Grant Value | £524,466 | |
| Industrial Sectors | Unknown | |
| Region | London | |
| Programme | NC : Engineering | |
| Investigators | Principal Investigator | Nan Li , Imperial College London |
| Web Site | ||
| Objectives | ||
| Abstract | The ultimate goal of this project is to pioneer the world's first AI-driven Design for Forming (AI-DfF) platform, facilitating versatile applications in design development of high-performance manufacturable vehicle components. The transport sector accounts for about a quarter of global CO2 emissions. Lightweighting vehicles, especially their body parts, is crucial to increase fuel efficiency, which can reduce fuel consumption by up to 30%. Sheet forming processes, contributing to approximately 250-300 parts in every car, are essential in this regard because of their cost-effectiveness and superior stiffness-to-weight ratios. However, challenges such as manufacturing defects and a disjointed design process complicate achieving desired outcomes. An AI-driven solution is proposed to integrate manufacturability and performance metrics early in the design process. AI applications are emerging in the forming sector to enhance efficiency and product quality. Despite initial successes in AI-based modelling of forming processes, current models struggle with complex real-world scenarios, particularly intricate 3D geometries in automotive designs. Advanced Graph Neural Networks (GNNs) offer hope for simulating intricate physical systems, hinting at the potential for broader AI application in the forming sector. The project intends to meld knowledge from various domains, such as materials, mechanics, and AI, to tackle challenges in the forming space. Key objectives include creating an efficient database for forming, developing methods for complex geometric representation in AI-compatible formats, developing GNN-based surrogate models for AI-driven simulations, and establishing a platform that synergises data and AI models for optimal system design and performance. The envisioned AI platform aims to make design processes vastly more efficient, potentially accelerating the creation of novel vehicle parts by tenfold and significantly boosting their performance, manufacturability, and eco-friendliness. This innovation is crucial for the UK's manufacturing competitiveness in the rapidly advancing age of AI | |
| Data | No related datasets |
|
| Projects | No related projects |
|
| Publications | No related publications |
|
| Added to Database | 07/01/26 | |
