Optimizing the Manufacturing Process of Composite Materials

 

International Conferences on Composite Materials

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Optimizing the Manufacturing Process of Composite Materials

Although this tailorability increases design options, it can negatively impact costs, productivity, and sustainability during manufacture. This can be particularly apparent in machining, where FRP part-specific defects occur.

A common cause for prescribing overly-conservative cutting tool use limits is process uncertainties that result in a large, unpredictable defect generation based on part quality criteria. An application-specific approach is required to identify the most effective cutting strategies due to the wide array of available tool designs and workpiece material configurations.

Using an exhaustive, wide-boundary, DoE-based approach, optimal cutting parameters can be found, with slow and costly testing needed to identify absolute tool life limits.

A novel machine learning-based method is established by the work described in this article to predict tool life from start-of-life performance data, minimizing experimental cost and time. As the original dataset was sparse, with 82% of the target data missing, the project was particularly challenging.

Intellegens’ novel machine learning software, Alchemite™,1 builds comprehensive models from sparse and noisy data and leverages the unique insights of deep learning. In this study, the AMRC provided tooling time series data on 55 drill/composite pairs, recording 23 machining responses, including hole quality metrics and in-process measurements. Using its intuitive drag-and-drop interface, this data was easily uploaded into the Alchemite™ Analytics software. 

The deep learning model was trained on the tooling dataset. Alchemite™ was able to train a model with a high coefficient of determination of 0.73, despite the missing 82% of data. The core Alchemite™ algorithm was used in combination with various data pre-processing steps to achieve this high accuracy and reduce the inherent noise. 

As Figure 1 shows, these steps included data grouping, followed by aggregation. Analysis showed that, although data from a typical testing dataset of over 1,000 holes was available, 200 data points were adequate for providing deep insight into a tool’s future cutting performance with the right aggregation.

• Widespread in aerospace, laminated fiber-reinforced polymer matrix composites are also increasingly used in other sectors, including the automotive industry. The identification of optimal cutting parameters for a given tool composite pair is of utmost importance, as this can significantly reduce component non-conformance. During final aircraft assembly, surface delamination during machining results in 60% of part rejections.

  Although this tailorability increases design options, it can negatively impact costs, productivity, and sustainability during manufacture. This can be particularly apparent in machining, where FRP part-specific defects occur.

  A common cause for prescribing overly-conservative cutting tool use limits is process uncertainties that result in a large, unpredictable defect generation based on part quality criteria. An application-specific approach is required to identify the most effective cutting strategies due to the wide array of available tool designs and workpiece material configurations.

  Using an exhaustive, wide-boundary, DoE-based approach, optimal cutting parameters can be found, with slow and costly testing needed to identify absolute tool life limits.

  A novel machine learning-based method is established by the work described in this article to predict tool life from start-of-life performance data, minimizing experimental cost and time. As the original dataset was sparse, with 82% of the target data missing, the project was particularly challenging.

  Intellegens’ novel machine learning software, Alchemite™,1 builds comprehensive models from sparse and noisy data and leverages the unique insights of deep learning. In this study, the AMRC provided tooling time series data on 55 drill/composite pairs, recording 23 machining responses, including hole quality metrics and in-process measurements. Using its intuitive drag-and-drop interface, this data was easily uploaded into the Alchemite™ Analytics software. 

  The deep learning model was trained on the tooling dataset. Alchemite™ was able to train a model with a high coefficient of determination of 0.73, despite the missing 82% of data. The core Alchemite™ algorithm was used in combination with various data pre-processing steps to achieve this high accuracy and reduce the inherent noise. 

  As Figure 1 shows, these steps included data grouping, followed by aggregation. Analysis showed that, although data from a typical testing dataset of over 1,000 holes was available, 200 data points were adequate for providing deep insight into a tool’s future cutting performance with the right aggregation.