To address the limitations of traditional orthogonal
experimental methods, which rely on experience and have limited parameter search capabilities, and
response surface methodology, which is heavily reliant on the number of
finite element simulation samples and has high computational costs, in optimizing the structural parameters of a six-dimensional
force sensor elastomer, this invention proposes a method for optimizing the structural parameters of a six-dimensional
force sensor elastomer based on a
physical information neural network. The technical approach involves: taking the six-dimensional
force sensor elastomer structure as the
research object, selecting the length of the floating beam, the length of the strain beam, the width of the floating beam, the width of the strain beam, and the beam thickness as design variables; constructing multiple combinations of structural dimensions through orthogonal experiments, and obtaining the
strain response data corresponding to each dimension combination using finite
element analysis; establishing an elastomer mechanical model, and introducing the constitutive relations, governing equations, and boundary conditions of the mechanical model as physical constraints into the neural network training process; simultaneously using finite element strain data to supervise and correct the network, thereby constructing a high-precision mapping model between structural parameters and
strain response; and further combining this mapping model with a multi-objective
genetic algorithm (NSGA-III) to optimize the search for the elastomer structural parameters. This invention reduces the requirement for
finite element simulation samples while achieving high-precision prediction of the mechanical response of the elastomer structure, significantly improving the efficiency of structural parameter optimization.