Abstract:

In a machining process,changes of workpiece geometric features can lead to variation in the statistical characteristics of cutting forces,causing significant degradation in the accuracy ability of traditional data-driven cutting force prediction models.and different processing conditions make the collected cutting force modeling data have obvious data distribution differences,causing significant degradation in the generalization ability of traditional data-driven cutting force prediction models.To address these problems,this paper proposes a cutting force prediction method incorporating workpiece geometric features under variable machining conditions.First,data preprocessing is carried out,including the workpiece geometric features and working condition information coding processing,cutting force signal removing trend items,and cutting force statistical features removing outliers.By considering the workpiece geometric features and working condition changes caused by the data distribution differences,the data set is divided into source domain data and target domain data;then the source domain data and the target domain data are divided into training sets and test sets according to the rules,and a variable cutting force prediction model incorporating geometric features of the workpiece is constructed based on transfer learning.Finally experimental verification is carried out from different data quantities,single processing geometry characteristics,variable working conditions,and different algorithms.Experimental results show that the method is more suitable for predicting cutting forces under changing working conditions and workpiece geometrical characteristics than the traditional data driven cutting force prediction model,while maintaining a higher prediction accuracy with fewer data samples and a better generalization performance,so that it can provide a better practicality.

Key words: cutting force prediction, machining geometric features, transfer learning, data driven