Machine tool response modeling method and system based on transfer learning and response prediction method

Abstract

The invention discloses a machine tool response modeling method, a modeling system and a response prediction method based on transfer learning. The modeling method comprises the following steps: training a source domain response prediction model by utilizing source domain data; a self-adaptive layer is added to the source domain response prediction model, parameters of the source domain response prediction model are reversely adjusted with the target that a loss function is smaller than a preset value, a domain adaptation initial model is obtained, and the loss function comprises classification loss and domain adaptation loss; inputting the target domain data into the domain adaptation initial model for fine tuning to obtain a domain adaptation model; inputting the source domain data into the domain adaptation model to obtain auxiliary training data; and training the target domain response prediction model by using the auxiliary training data and the target domain data. According to the method, model migration and sample migration are combined, multiplexing of source domain data is achieved, the demand quantity of model establishment for new data under the new working condition is reduced, and therefore the experiment cost of data collection for various different working conditions in actual production is reduced.

Description

technical field [0001] The invention belongs to the technical field of neural network learning, and more specifically relates to a machine tool response modeling method, modeling system and response prediction method based on migration learning. Background technique [0002] In the modern machining process, using computer technology to establish a machine tool response model can quickly predict the machine tool response under the corresponding process parameters. Nowadays, data-driven modeling is one of the most commonly used methods to predict the machine tool response. Establishing a model with excellent performance can quickly predict Get the response from the machine tool. Many researchers have been working on the modeling of specific scenarios. However, when the working conditions change, it is necessary to re-model according to the new process conditions. For example, when predicting spindle power through cutting modeling, different processing conditions have differen...

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Problems solved by technology

[0003] In view of the above defects or improvement needs of the prior art, the present invention provides a machine tool response modeling method, modeling system and response prediction method based on transfer learning, the purpose of which is to combine existing data sets under different working conditions Modeling, thereby solving the technical problems of low efficiency and time-consuming caused by reacquiring experimental data under the current working conditions for modeling

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