Automating Nanoindentation: Optimizing Workflows for Precision and Accuracy
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910919140835328 |
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| author | Chawla, Vivek Penumadu, Dayakar Kalinin, Sergei |
| author_facet | Chawla, Vivek Penumadu, Dayakar Kalinin, Sergei |
| contents | Nanoindentation is vital for probing mechanical properties, yet traditional grid-based workflows are inefficient for targeting specific microstructural features. We present an automated nanoindentation framework that integrates machine learning, real-time alignment, and adaptive indentation strategies. The system operates in three modes: standard automation, feature-based indentation via image-to-coordinate mapping, and large-scale indentation with full x, y, and z axis alignment. A key challenge (precise sample positioning across imaging and indentation stages) was met by correcting initial travel-distance errors (2.5-6 micrometres) through pixel-to-micron calibration, reducing alignment errors to the submicron level. Benchmark tests demonstrate phase-specific and orientation-guided indentation enabled by self-organising maps and macro imaging. The framework markedly improves precision, minimises user intervention, and enables efficient, targeted characterisation of complex materials. By providing a direct interface between nanoindentation instruments and Python-based automation frameworks, it can be adopted on most existing platforms. This work lays the foundation for next-generation autonomous mechanical testing tailored to microstructurally complex materials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18525 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automating Nanoindentation: Optimizing Workflows for Precision and Accuracy Chawla, Vivek Penumadu, Dayakar Kalinin, Sergei Instrumentation and Detectors Materials Science Nanoindentation is vital for probing mechanical properties, yet traditional grid-based workflows are inefficient for targeting specific microstructural features. We present an automated nanoindentation framework that integrates machine learning, real-time alignment, and adaptive indentation strategies. The system operates in three modes: standard automation, feature-based indentation via image-to-coordinate mapping, and large-scale indentation with full x, y, and z axis alignment. A key challenge (precise sample positioning across imaging and indentation stages) was met by correcting initial travel-distance errors (2.5-6 micrometres) through pixel-to-micron calibration, reducing alignment errors to the submicron level. Benchmark tests demonstrate phase-specific and orientation-guided indentation enabled by self-organising maps and macro imaging. The framework markedly improves precision, minimises user intervention, and enables efficient, targeted characterisation of complex materials. By providing a direct interface between nanoindentation instruments and Python-based automation frameworks, it can be adopted on most existing platforms. This work lays the foundation for next-generation autonomous mechanical testing tailored to microstructurally complex materials. |
| title | Automating Nanoindentation: Optimizing Workflows for Precision and Accuracy |
| topic | Instrumentation and Detectors Materials Science |
| url | https://arxiv.org/abs/2504.18525 |