Bead Geometry Prediction for Multi-layer and Multi-bead Wire and Arc Additive Manufacturing Based on XGBoost
Bead Geometry Prediction for Multi-layer and Multi-bead Wire and Arc Additive Manufacturing Based on XGBoost
复制标题
基于 XGBoost 的多层、多珠线材和电弧增材制造的珠子几何形状预测
DOI：
10.1007/978-981-13-8668-8_7
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Shanben Chen
中科院分区：
文献类型：
--
作者：
Junhao Deng;Yanling Xu;Zhangchi Zuo;Zhen Hou;Shanben Chen
在多层多胎圈钢丝电弧增材制造（WAAM）过程中，各层的几何形状对最终成形零件的尺寸精度和表面质量有重要影响。本文采用机器学习模型对多层多通道WAAM成形件的几何形貌进行预测。通过旋转组合实验进行了一系列不同参数下的WAAAM成形实验，并利用自行开发的视觉传感系统获得了成形件的焊道几何形状。针对模型训练过程中数据样本较少容易导致过拟合的问题，引入XGBoost算法进行建模。与神经网络算法相比，基于XGBoost的电弧增材制造形态回归预测模型具有更高的预测精度。
In the process of multi-layer and multi-bead wire and arc additive manufacturing (WAAM), the geometry of each layer has an important influence on the dimensional accuracy and surface quality of the final forming parts. In this paper, machine learning model is used to predict the geometrical morphology of multi-layer and multi-channel WAAM forming parts. A series of experiments of WAAAM under different parameters were carried out by rotating combination experiment, and the bead geometry of the forming parts were obtained by visual sensing system developed by ourselves. Aiming at the problem of less data samples which would lead to over-fitting in the process of model training, this paper introduces the XGBoost algorithm for modeling. Compared with the neural network algorithm, the regression prediction model of arc additive manufacturing morphology based on XGBoost has a higher prediction accuracy.
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DOI：
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发表时间：
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期刊：
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影响因子：
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作者：
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通讯作者：
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影响因子：
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发表时间：
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影响因子：
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