<p>The subject of this work is the problem of optimizing the configuration of cuts for skin grafting in order to improve the efficiency of the procedure. We consider the optimization problem in the framework of a linear elasticity model. We choose three mechanical measures that define optimality via related objective functionals: the compliance, the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(L^p\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mi>L</mi> <mi>p</mi> </msup> </math></EquationSource> </InlineEquation>-norm of the von Mises stress, and the area covered by the stretched skin. We provide a proof of the existence of the solution for each problem, but we cannot claim uniqueness. We compute the gradient of the objectives with respect to the cut configuration using concepts from shape calculus. To solve the problem numerically, we apply the gradient descent method, which performs well under uniaxial stretching. However, in more complex cases, such as multidirectional stretching, its effectiveness is limited due to the low sensitivity of the functionals under consideration.To avoid this difficulty, we use a combination of the genetic algorithm and the gradient descent method, which leads to a significant improvement in the results.</p>

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Optimization of the cut configuration for skin grafts

  • Helmut Harbrecht,
  • Viacheslav Karnaev

摘要

The subject of this work is the problem of optimizing the configuration of cuts for skin grafting in order to improve the efficiency of the procedure. We consider the optimization problem in the framework of a linear elasticity model. We choose three mechanical measures that define optimality via related objective functionals: the compliance, the \(L^p\) L p -norm of the von Mises stress, and the area covered by the stretched skin. We provide a proof of the existence of the solution for each problem, but we cannot claim uniqueness. We compute the gradient of the objectives with respect to the cut configuration using concepts from shape calculus. To solve the problem numerically, we apply the gradient descent method, which performs well under uniaxial stretching. However, in more complex cases, such as multidirectional stretching, its effectiveness is limited due to the low sensitivity of the functionals under consideration.To avoid this difficulty, we use a combination of the genetic algorithm and the gradient descent method, which leads to a significant improvement in the results.