Abstract

Deformation prediction is the basis of deformation control in manufacturing process planning. This paper presents an on-line part deformation prediction method using a deep learning model during numerical control machining process, which is different from traditional methods based on finite element simulation of stress release prior to the actual machining process. A fourth-order tensor model is proposed to represent the continuous part geometric information, process information, and monitoring information, which is used as the input to the deep learning model. A deep learning framework with a conventional neural network and a recurrent neural network has been constructed and trained by monitored deformation data and process information associated with interim part geometric information. The proposed method can be generalised for different parts with certain similarities and has the potential to provide a reference for an adaptive machining control strategy for reducing part deformation. The proposed method was validated by actual machining experiments, and the results show that the prediction accuracy has been improved compared with existing methods. Furthermore, this paper shifts the difficult problem of residual stress measurement and off-line deformation prediction to the solution of on-line deformation prediction based on deformation monitoring data.

中文翻译：

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基于深度学习的在线零件变形预测

摘要

变形预测是制造过程计划中变形控制的基础。本文提出了一种在数控加工过程中使用深度学习模型的在线零件变形预测方法，该方法不同于基于在实际加工过程之前进行应力释放的有限元模拟的传统方法。提出了四阶张量模型来表示连续零件的几何信息，过程信息和监视信息，这些信息用作深度学习模型的输入。具有常规神经网络和递归神经网络的深度学习框架已通过监视的变形数据和与中间零件几何信息相关的过程信息进行构建和训练。所提出的方法可以推广到具有某些相似性的不同零件，并有可能为减少零件变形的自适应加工控制策略提供参考。通过实际加工实验验证了该方法的有效性，结果表明与现有方法相比，该方法的预测精度有所提高。此外，本文将残余应力测量和离线变形预测的难题转移到基于变形监测数据的在线变形预测的解决方案上。结果表明，与现有方法相比，预测精度有所提高。此外，本文将残余应力测量和离线变形预测的难题转移到基于变形监测数据的在线变形预测的解决方案上。结果表明，与现有方法相比，预测精度有所提高。此外，本文将残余应力测量和离线变形预测的难题转移到基于变形监测数据的在线变形预测的解决方案上。