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March 01, 2019 | Artificial Intelligence | Back | Share Via

Case Study: National Composite Centre (NCC)

Case Study: National Composite Centre (NCC)

Application of Machine Learning to Autoclave Failure Prediction

Problem:

Autoclave processing is a key step in producing composite structures but comes with a large cost in terms of duration and energy consumption. When the process fails the structure being cured may have to be scrapped at a significant cost. The financial loss is further increased when the wasted energy and time is factored in.

Business Case:

Potential savings from using an early warning system for curing failure could be significant. For this particular project, with data spanning 5 years, 15 curing runs failed leading to part scrappage. Assuming that costs associated with each failed run could be up to £100k, the total potential savings would be in the region of £1.5m. These estimates are for a single autoclave and for a relatively small production volume. Larger manufacturers with a larger number of autoclaves and higher production volumes would benefit from potential costs savings several orders of magnitude above this value.

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