Fractional order KVFD multi-parameter machine learning optimization method for viscoelasticity mechanical characterization of soft substance

Abstract

The invention discloses a fractional order KVFD multi-parameter machine learning optimization method for viscoelasticity mechanical characterization of soft substances. The method comprises the steps:building corresponding K-tree dictionaries according to the conditions of solutions of three types of KVFD loading modes; judging the specific type of a to-be-tested curve, carrying out global search, obtaining the parameter [E0, alpha, tau] of the curve as a vector, zooming to a parameter interval, generating a preset number of curves according to the KVFD model corresponding to the to-be-testedcurve, adding random Gaussian noise, dividing into a training set and a test set, and transmitting the training set and the test set into a machine learning model for training; selecting the model with the minimum RMSE as a final model for training; performing parameter estimation on a to-be-measured curve by using the final model obtained in the step 3; and further learning a result obtained byparameter estimation through a Q-learning algorithm to obtain an optimization result. According to the method, the characteristics of parameter learning and a heuristic algorithm are combined, and theaccuracy and efficiency of parameter optimization can be greatly improved.

Description

technical field [0001] The invention belongs to the technical field of soft matter mechanical analysis based on fractional derivative model KVFD (Kelvin–Voigt fractional derivative model), relates to the field of nano-indentation, that is, machine learning, and specifically relates to a fractional KVFD multi-parameter machine learning of soft matter viscoelastic mechanical characterization Optimization. Background technique [0002] Nanoindentation technology, also known as depth-sensitive indentation technology, is one of the simplest methods to test the mechanical properties of materials and has been widely used in various fields of material science. Before testing the mechanical properties of the material, the mechanical model of the material must first be clarified. The models used in existing instruments are all elastic models. For spherical probes, the Hertz model is usually used; for conical probes, the usually used It is the Sneddon model. [0003] For the Hertz mo...

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Problems solved by technology

However, an experiment usually results in tens of thousands of curves. Manual fitting is extremely time-consuming, labor-intensive, inefficient, and has low accuracy. There is an urgent need for a method that can automatically fit curves and extract multiple parameters. This paper The method of invention is put forward under this general background

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