About
Merrypopins is a Python library that loads and ingests data from the nano-indentation experiment, preprocesses the data to remove known artifacts, calculates stress-strain curves for the data, uses different ai/machine learning models for locating pop ins in the data, and facilitates statistical analysis across many iterations of the nanoindentation experiment.
Nano-indentation
Nano-indentation is a relatively simple science in terms of the vast amount of material science options available out there. You poke a material with a microscopic “tip” with more and more force and measure the response from the tip in terms of the force applied and the current location of the tip to see what happens. This simplicity allows for strict control of the system while producing rather dynamic behaviors.
Input data format
Merrypopins was developed using datasets generated by the Bruker Hysitron TI 990 TriboIndenter — a high-precision nanoindentation platform.
The library natively supports .txt and .tdm/.tdx file formats exported by the Hysitron software suite.
Typical indentation experiments conducted with the TI 990 include:
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Force-depth curve acquisition at nano/micro scale
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High-resolution pop-in event detection
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Automated test grid data export
The preprocessing and pop-in detection tools in Merrypopins are tuned to handle the structural patterns and noise profiles specific to these datasets.