Defining a material model

#import ansys.mapdl.materials.aml as aml 
#import numpy as np

#Material Definition
mymaterial = aml.material(1)
mylist = [10,-1,1e-3]
myfld = dict()
myfld['temp'] = 0.0
mymaterial.add_table("hype","moon",myfld,mylist)
mymaterial.print("hype","moon")

Initializing the solve object to setup the material calls

mymaterial.initialize()

Setting up the state variables if needed

Material state is a dictionary object that represents the current state of the material object and encapsulates current total strain, plastic strain, and other material specific state variables. In this step, the currentstate variable holds the material state object's data.

currentstate = mymaterial.get_state()
currentstate.set_state['epto'] = [0.001, 0, 0, 0, 0, 0]
mymaterial.set_state(currentstate)

Calling the driver and getting results

disp = 0.1
l1 = 1+disp
l2 = (1/(l1))**0.5

#Set solution parameters
solutiondata = dict()
solutiondata['time'] = 101.0
solutiondata['timinc'] = 0.0 
defgrad_t = [1,0,0,0,1,0,0,0,1]
defgrad = [l1,0,0,0,l2,0,0,0,l2]
outputstress = []
jacobian = []
mymaterial.initialize()
[outputstress,jacobian,status] = mymaterial.evaluate(solutiondata,defgrad_t,defgrad)
print("Stress is ", outputstress)
jacobmatrix = np.array(jacobian)

jacobmatrix = jacobmatrix.reshape((6,6))
print("Jacobian :")
print(jacobmatrix)

#get updated state
currentstate = mymaterial.get_state()
print(currentstate('epto'))

The aml.fit class is equivalent to the TBFT command but with a Python interface.

Defining a material model

myliste = [Ce1,Ce2]
mylistb = [s1,s2]
flddata = dict()
flddata['temp'] = 0.0
material.add_table("elas","isot",flddata,myliste)
material.print("elas","isot")
material.add_table("plas","biso",flddata,mylistb)
material.print("plas","biso")

Creating a fitting object and importing parameters

curvefit = aml.fit(1)
curvefit.create_fit('myfit')

Adding experimental data

curvefit.add_experiment('unia','Ex4_biso.exp')

Fixing the parameters

curvefit.fix_parameter_value('myfit',1,True)

Performing the optimize operation

solpars = dict()
solpars['numiterations'] = 20
solpars['errornorm'] = 1
solpars['rctolerance'] = 0.0 # Residual Change Tolerance
solpars['cctolerance'] = 0.0 # 
curvefit.solve('myfit',solpars)
curvefit.print()

Getting fitted parameter values and fitted values

coeffs = curvefit.get_parameter_values('myfit')
print(curvefit.generate_fitted_data("myfit",expid))