Optimizing Composite Drilling with Deep Learning: A Case Study
Technology Category
- Analytics & Modeling - Machine Learning
Applicable Industries
- Aerospace
- Equipment & Machinery
Applicable Functions
- Maintenance
- Product Research & Development
Use Cases
- Additive Manufacturing
- Time Sensitive Networking
Services
- Testing & Certification
- Training
The Challenge
Laminated fibre-reinforced polymer (FRP) matrix composites are increasingly common in industries with a drive towards high-performance lightweight components, such as aerospace. Machining these composites can result in defects and uncertainties, leading to conservative cutting tool use limits and slow testing processes.
The Customer
The University of Sheffield Advanced Manufacturing Research Centre (AMRC)
About The Customer
The University of Sheffield Advanced Manufacturing Research Centre (AMRC) provided the tooling dataset for the study. They were looking for a solution to reduce experimental time and cost in identifying optimal cutting parameters for tool-composite pairs.
The Solution
The Intellegens deep learning software, Alchemite™, was used to build comprehensive models from sparse and noisy data. By training a deep learning model on tooling time series data, Alchemite™ was able to accurately predict tool life and identify factors that impact tool performance. This allowed for more efficient tooling design and selection prior to experimental campaigns.
Quantitative Benefit
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