Automobile engine hood mechanical property rapid evaluation method based on machine learning
A technology of automobile engine and machine learning, which is applied in the direction of instruments, computer components, special data processing applications, etc., can solve the problems of low stability and long engine cover development cycle, etc., and achieve the effect of good stability and high prediction accuracy
Pending Publication Date: 2022-01-11
HUNAN UNIV AISHENG AUTO TECH DEV
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[0005] The purpose of this application is to apply the prediction results of multiple machine learning algorithm models to the evaluation of the mechanical properties of the engine hood in a fusion manner, so as to solve the problems of the long development cycle of the engine hood and the low prediction accuracy and stability
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[0034] In order to better understand the above-mentioned purpose, features and advantages of the present application, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.
[0035] In the following description, a lot of specific details are set forth in order to fully understand the application, however, the application can also be implemented in other ways different from those described here, therefore, the protection scope of the application is not limited by the following disclosure Limitations of specific embodiments.
[0036] Typically, an engine cover structure has more than 10 key parameters. According to statistics, in the process of engine hood structure design, it takes about 20 seconds to modify a design parameter, about...
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The invention discloses an automobile engine hood mechanical property rapid evaluation method based on machine learning, and the method comprises the steps: carrying out multiple times of training and testing based on an original data set and a plurality of initial algorithm models, and extracting initial feature vectors of the plurality of initial algorithm models and corresponding prediction data sets; calculating and generating an intermediate training data set according to the initial feature vector and the prediction data set; according to the initial center point set of the original data set and a preset condition function, calculating a termination center point set corresponding to the initial center point set when the preset condition function converges; generating a training data set according to the termination center point set and the intermediate training data set; according to the training data set, calculating a corresponding hybrid intelligent model when the target function value is minimum, and using the hybrid intelligent model for rapidly evaluating the mechanical property of the automobile engine hood. According to the technical scheme, the problems that the research and development period of the engine hood is long, and prediction precision and stability are low are solved.
Description
technical field [0001] The present application relates to the technical field of automobiles, and in particular, relates to a machine learning-based rapid evaluation method for the mechanical properties of automobile hoods. Background technique [0002] With the rapid development of the global automotive industry and the continuous reduction of the automotive design cycle, it is difficult for traditional auto parts design methods to meet the rapid iteration of the market. The rapid design method of parts can provide a solution to this problem. As an important part of the car body structure, the performance of the automobile hood directly affects the performance of the vehicle, especially the stiffness and mode of the hood need to meet the performance requirements of the vehicle, so the study of the mechanical properties of the hood is of great importance. important value and significance. [0003] However, in the existing hood structure design process, it is first necessary...
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IPC IPC(8): G06F30/15G06F30/27G06K9/62
CPCG06F30/15G06F30/27G06F18/23213G06F18/214
Inventor 张承霖陈少伟杨昊
Owner HUNAN UNIV AISHENG AUTO TECH DEV