...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Surface & Coatings Technology >Prediction of control parameters corresponding to in-flight particles in atmospheric plasma spray employing convolutional neural networks
【24h】

Prediction of control parameters corresponding to in-flight particles in atmospheric plasma spray employing convolutional neural networks

机译：采用卷积神经网络的大气等离子体喷雾中飞行中颗粒对应的控制参数预测

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Optimization of control parameters for plasma spraying process is of great importance in thermal spray technology development. Engineers may limit themselves to local optimal solution by considering countable potential design solutions when selecting the plasma spray parameters in practice. This work proposes one decision support model by employing convolutional neural network (CNN) to explore the suitability of preliminary design. The approach aims to help engineers select global optimal solution with short time and low labor cost by invoking the models' capability of extracting potential features of in-flight particle characteristics. Simulation results make it possible to analyze new spraying process and train the designed model under the condition of insufficient experience and data. Firstly, the distributions of particle status obtained from simulation results act as the input and the control parameters are the output. Secondly, the projections between the in-flight particles and the control parameters are built implicitly and analyzed through CNN models. Thirdly, we validate the statistical information of particle state distributions through visualizing the feature maps and fillers. Finally, the trained CNN models are verified by the fitted Gaussian distributions with basically consistent results. By building projections of in-flight particles and control parameters, new entrants and apprentices are capable of deducing the control parameters with the help of the pre-trained CNN model, thus cutting down the threshold for new practitioners.
机译：对等离子喷涂过程的控制参数优化在热喷涂技术开发中具有重要意义。工程师可以通过考虑在实践中选择等离子体喷射参数时考虑可数潜在的设计解决方案来限制本地最佳解决方案。这项工作通过采用卷积神经网络（CNN）来提出一种决策支持模型来探索初步设计的适用性。该方法旨在帮助工程师通过调用飞行飞行粒子特征的潜在特征的模型的模型能力，帮助工程师使用短时间和低劳动力成本选择全球最佳解决方案。仿真结果使得可以在不足的经验和数据的情况下分析新的喷涂过程并培训设计的模型。首先，从仿真结果获得的粒子状态的分布充当输入和控制参数是输出。其次，通过CNN模型隐式构建飞行粒子和控制参数之间的投影。第三，我们通过可视化特征贴图和填充物来验证粒子状态分布的统计信息。最后，录制的CNN模型由拟合的高斯分布验证，具有基本一致的结果。通过建立飞行飞行粒子和控制参数的预测，新的进入者和学徒能够在预先训练的CNN模型的帮助下推导控制参数，从而减少新从业者的阈值。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号