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Prediction of depth removal in leather surface grit blasting using neural networks and Box-Behnken design of experiments

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Abstract

In this work, leather material is for the first time prepared by grit blasting process in order to improve peel strength when bonding. Peel tests show that it is the surface depth of removal rather than surface roughness that dominates the bonding performance. Therefore, measurement of surface removal is critical for surface preparation of using a grit blasting process. Indirect measurement of preparation performance is essential due to the hazardous conditions for conventional sensing equipment in the blasting chamber. A neural network modelling approach is proposed for the prediction of surface removal of leather materials, and the neural network model also characterizes the process, which is very useful for machine design and optimum control. The data used for the training of the artificial neural network is collected through screening experiments, which was efficiently planned using the Box-Behnken design method.

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Authors and Affiliations

  1. Department of Mechanical & system Engineering, Newcastle University upon Tyne, NE1 7RU, Dummy, UK

    Zhongxu Hu, Robert Bicker & Chris Marshall

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  1. Zhongxu Hu
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  2. Robert Bicker
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  3. Chris Marshall
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Correspondence to Zhongxu Hu.

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Hu, Z., Bicker, R. & Marshall, C. Prediction of depth removal in leather surface grit blasting using neural networks and Box-Behnken design of experiments. Int J Adv Manuf Technol 32, 732–738 (2007). https://doi.org/10.1007/s00170-005-0381-6

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  • DOI: https://doi.org/10.1007/s00170-005-0381-6

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