Indentation: Basic usage

Introduction

The toolbox aims to help with nanoindentation data analysis using python.

Note

The following units should be used: [mN], [µm], [GPa] because the area function is unit-dependent.

Calculations are carried out by the default Oliver-Pharr Method.

Data in indentation experiment includes Method, Vendor, Indenter tip

  • Method: ISO, CSM

  • Vendor: Agilent, Hysitron, FischerScope, Micromaterials

  • Indenter Tip: shape of indenter tip and stiffness of gantry

How it works:

  1. Obtain loading and unloading data through nanoindentation, measuring force (p) and depth (h)

  2. Calculate:

    • slope [mN/µm]: Slope of unloading part (stiffness)

    • hc [µm]: Contact depth

    • Ac [µm2]: Contact area

    • hardness [GPa]

    • modulusRed [GPa]: reduced Young’s modulus

    • modulus [GPa]: Young’s modulus

  3. Plot

Initialization

Import necessary libraries:

import matplotlib.pyplot as plt
import numpy as np
from micromechanics.indentation import Indentation, Tip

Introduce tip:

ourTip = Tip()  #µm/mN

Initialization: Files containing the experimental data are in hdf5, txt or xls format:

fileName = 'Nafion_15_100_5.hdf5'

Introduce class Indentation, where nuMat is material’s Poisson’s ratio:

i = Indentation(fileName, nuMat=0.3, tip=ourTip)

Run through all indentations (Test 1, Test 2, etc) in the file with a for cycle:

for testname in i:
        <body>

In the <body> one can analyse the data and create the plots to be viewed like following:

Calculate all the relevent data from the indent: stiffness S [mN/µm], maximum depth hMax [µm], maximum force pMax [mN], reduced Young’s modulus redE [GPa], contact area A [µm2], contact depth hc [µm], Young’s modulus E [GPa], hardness H [GPa]:

i.analyse()

Plotting:

  • i.plot would plot each test(indent) independantly with the analysis data: slope, hc, power law etc

  • plt.plot(i.h, i.p) would plot all tests(indents) together. Useful to spot outliers:

    i.plot()
plt.plot(i.h, i.p)

Save all values to a dictionary. This can be appended to a user defined dataframe and further information can be added to it accordingly. E.g. the file name in this case:

meta = i.metaUser
meta["file name"]=fileName
df = df.append(meta, ignore_index=True)

Show plots:

plt.show()

Full source code of example

This is an example code analysing the hdf5 files from FischerScope nanoindenter:

import matplotlib.pyplot as plt
import pandas as pd
from nanoindentation import Indentation, Tip

fileName = "Nafion_15_100_5.hdf5"
ourTip = Tip()  #um/mN
i = Indentation(fileName, nuMat=0.5, tip=ourTip)
df = pd.DataFrame()

for testname in i:
        i.analyse()
        #i.plot()
        plt.plot(i.h, i.p)
        meta = i.metaUser
        df = df.append(meta, ignore_index=True)

print(df)
plt.show()