Intellegens > Case Studies > Tooling Optimisation for Composite Drilling Using Deep Learning

Tooling Optimisation for Composite Drilling Using Deep Learning

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Technology Category
  • Analytics & Modeling - Machine Learning
Use Cases
  • Immersive Analytics
  • Data Science Services
The Challenge

Laminated fiber-reinforced polymer (FRP) matrix composites are increasingly common in industries with a drive towards high-performance lightweight components, such as aerospace. This is due to their excellent mechanical properties and highly-tailorable design. Although such tailorability increases design options, it can negatively impact costs, productivity, and sustainability during manufacture. This is particularly apparent in machining, where FRP part-specific defects occur.

The work described here establishes a novel machine learning-based method to predict tool life from start-of-life performance data, reducing experimental time and cost. The project was particularly challenging, because the original dataset was sparse, with 82% of the target data missing.

The Customer

The University of Sheffield Advanced Manufacturing Research Centre (AMRC)

About The Customer

The University of Sheffield Advanced Manufacturing Research Centre (AMRC) is a network of world-leading research and innovation centers working with companies involved in manufacturing of all sizes from around the globe. The AMRC has undertaken a number of historic CFRP and CFRP/metallic stack drilling trials in order to help the industry develop economic methods of controlling drilling-induced delamination.

Intellegens provides a unique machine learning solution for real-world experimental and process data problems in industrial R&D and manufacturing. The Alchemite™ deep learning software, based on a methodology that originated at the University of Cambridge, can model sparse, noisy data, where other machine learning approaches fail. It has accelerated innovation in areas including alloys and component design, development of formulated products, drug discovery, additive manufacturing, and optimizing chemical processes.

The Solution

Alchemite™ is Intellegens’ novel machine learning software, which leverages the unique insights of deep learning to build comprehensive models from sparse and noisy data. In this study, tooling time series data on 55 drill/composite pairs, recording 23 machining responses, including hole quality metrics and in-process measurements, was provided by the AMRC. This data was easily uploaded into the Alchemite™ Analytics software using its intuitive drag-and-drop interface.

Operational Impact
  • [Data Management - Data Accuracy]

    By gaining insight from sparse data to quantify underlying, complex nonlinear property/property relationships, Alchemite created a tool-composite model with good predictive power

Quantitative Benefit
  • Making useful decisions based on only 20% of the typically-acquired performance data allows progress based on far fewer tests, resulting in up to 80% reductions in the direct costs associated with testing.

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