Automated Vickers hardness measurement using convolutional neural networks
| Autor: | Yutaka Seino, Yukimi Tanaka, Koichiro Hattori |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Materials science business.industry Mechanical Engineering Diagonal Process (computing) Image processing Pattern recognition 02 engineering and technology Convolutional neural network Industrial and Manufacturing Engineering Computer Science Applications 020901 industrial engineering & automation Control and Systems Engineering Minimum bounding box Indentation visual_art Vickers hardness test visual_art.visual_art_medium Artificial intelligence Ceramic business Software |
| Zdroj: | The International Journal of Advanced Manufacturing Technology. 109:1345-1355 |
| ISSN: | 1433-3015 0268-3768 |
| DOI: | 10.1007/s00170-020-05746-4 |
| Popis: | A method based on convolutional neural networks (CNN) was proposed for robust automated measurement of Vickers hardness. Vickers hardness testing is generally applied to metals but can be conducted on other materials such as ceramics. Typical image processing methods tend to fail to detect the indentations in materials with rough surfaces, noisy patterned surfaces, distorted indentation shapes, or cracks. Automated measurement methods must be robust and versatile to process materials with these anomalies. A CNN method was chosen to detect the bounding box of the Vickers indentation, as the technique automatically extracts features required to recognize the region of an object. The diagonal lengths and Vickers hardness values calculated using the CNN method were similar to the reference manual measurements, even in samples with rough surfaces, noisy patterned surfaces, distorted indentation shapes, or cracks. The CNN method is a promising automated alternative for robust and versatile prediction of Vickers hardness. |
| Databáze: | OpenAIRE |
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