Generation and categorisation of surface texture data using a modified progressively growing adversarial network

Eastwood, Joe; Newton, Lewis; Leach, Richard; Piano, Samanta

Abstract

As machine learning becomes more popular in the precision engineering sector, the need for large datasets of measurement data increases. Due to the often manual, user dependent and labour-intensive measurement processes, collecting a large amount of data is often infeasible. It would, therefore, be desirable to collect a small amount of data on which to train a model to generate synthetic data that is representative of the real measurement data. To this end, we present an approach to numerical surface texture generation based on a progressively growing generative adversarial network. We show that by encoding height data into grayscale values within an image, the network can create realistic synthetic surface data both qualitatively and quantitatively. The proposed approach is general to any encoded surface; we demonstrate the model trained on two example datasets consisting of surfaces from different manufacturing processes and measured with different techniques. We finally present an extension to the generator model which automatically categorises the produced surfaces, allowing a surface of a desired category to be generated. Finally, we calculate the distributions of areal surface texture parameters for each type of surface and show that there is good agreement between the synthetic and real data.

Citation

Eastwood, J., Newton, L., Leach, R., & Piano, S. (2022). Generation and categorisation of surface texture data using a modified progressively growing adversarial network. Precision Engineering, 74, 1-11. https://doi.org/10.1016/j.precisioneng.2021.10.020

• Journal Article Type: Article
• Acceptance Date: Oct 29, 2021
• Online Publication Date: Nov 2, 2021
• Publication Date: Mar 1, 2022
• Deposit Date: Nov 9, 2021
• Publicly Available Date: Nov 9, 2021
• Journal: Precision Engineering
• Print ISSN: 0141-6359
• Electronic ISSN: 0141-6359
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 74
• Pages: 1-11
• DOI: https://doi.org/10.1016/j.precisioneng.2021.10.020
• Public URL: https://nottingham-repository.worktribe.com/output/6674906
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0141635921002658