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ARUP; ISBN: 978-0-9562121-5-3 - CMBBE 2012 - Cardiff University

ARUP; ISBN: 978-0-9562121-5-3 - CMBBE 2012 - Cardiff University

ARUP; ISBN: 978-0-9562121-5-3 - CMBBE 2012 - Cardiff University

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Cook’s equation (Eqn 1). In this, Ai and Ai 0 represents the<br />

total and quasi static values of the material parameters, is the strain rate and Ci is a<br />

scaling parameter. The reference strain rate for quasi-static response was assumed to be<br />

0.001/s. However, a constitutive model that can take into account of this behavior is not<br />

readily available in commercial packages. Therefore a VUSDFLD subroutine was<br />

written in Abaqus/Explicit TM ε<br />

. Most material properties in ABAQUS/Explicit can be<br />

defined as functions of field variables, fi. Subroutine VUSDFLD allows the user to<br />

define fi at every integration point of an element. In the subroutine, the material<br />

properties can be a function of parameters like σ, ε, εpl, ε ̇, computed at solution time.<br />

The computed material properties inside VUSDFLD routine are then passed to the main<br />

code for stress updation. The user subroutine developed was validated using single<br />

element simulations and compiled in double precision along with the main model.<br />

.<br />

3.4. Optimisation module<br />

0 ε<br />

Ai = Ai 1+ log<br />

.<br />

⎛ ⎞<br />

⎜ ⎟<br />

⎝<br />

⎜ 0.001<br />

⎠<br />

⎟ *C ⎡<br />

⎤<br />

⎢<br />

⎥<br />

i<br />

⎢<br />

⎥<br />

⎣<br />

⎦<br />

Genetic algorithms (GA) are widely used in search and optimisation problems. They use<br />

procedures motivated by evolution such as selection, inheritance, mutation and<br />

crossover. When the location of the optimum of the problem cannot be analytically<br />

reduced to a subset of the solution space, GA can be used to extract a good solution<br />

though a reduced search process. For the current study an open source C++ GA code<br />

developed by Sastry and Goldberg [12] was used. Some studies have used optimization<br />

techniques to derive material parameters for bones under impact starting with CT scan.<br />

The present study differs in that subject specific material assignment and analysis was<br />

done instead of working with a nominal geometric model and establishing nominal<br />

material properties. A schematic diagram of the workflow of the optimisation procedure<br />

is shown in Figure 1.<br />

Figure 1 Schematic diagram of the workflow of the optimisation procedure.<br />

4. RESULTS AND DISCUSSIONS<br />

The material property sets arrived at are listed in Table 1, where C1, C2 and C3 are the<br />

scaling parameters for modulus of elasticity, yielding and failure used in Eqn 1<br />

respectively. Using the obtained material parameters individual tests were simulated by

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