从造工业使用电阻谱和支向量机器对砂粘合剂混合物的分类
Luca Bifano1, Xiaohu Ma1, Gerhard Fischerauer1
1Chair of Measurement and Control Systems, Faculty of Engineering Science, University of Bayreuth, 95440 Bayreuth, Germany.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
电阻谱学 (EIS) 显示出对造成型砂混合物监测的前景. 机器学习集成 (MLEIS) 在分类沙子类型方面实现了超过90%的准确性,从而实现了更好的过程控制.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 工业化学 工业化学 工业化学
背景情况:
- 造业务依赖于成型砂混合物,其最佳控制和再生因缺乏有效的线内监控而受到阻碍.
- 目前的监测方法不足以实时进行流程调整和沙子混合物利用和再生的质量保证.
研究的目的:
- 为了研究电阻谱学 (EIS) 的可行性,作为一个在线监测方法,用于造成型砂混合物.
- 开发和评估一种机器学习方法,使用EIS数据对不同沙子混合物进行分类.
主要方法:
- 在实验室条件下,在0.5kHz至1MHz的频率范围内使用EIS对各种成型砂混合物的表征.
- 开发一个针对不同沙子组成的EIS响应数据库.
- 支持矢量机 (SVM) 的应用与顺序特征选择用于根据EIS数据对砂混合物进行分类.
主要成果:
- EIS成功地描述了各种沙子混合物的特征.
- 支向量机实现了高分类准确度 (超过90%),用于成型砂混合物.
- 特性选择进一步提高了分类性能,并在SVM预测中显示了低标准不确定性.
结论:
- 机器学习增强的EIS (MLEIS) 是一种可行的技术,用于在造行业内在线监控散装材料.
- 开发的MLEIS方法提供了一个有前途的解决方案,通过精确和高效的砂混合物监测来优化造工艺.
关键词:
它们的分类是分类分类.电阻谱学 (EIS) 是一种电阻谱学.特性分析的特征分析.造厂 造厂 是一个机器学习是机器学习.模具材料 模具材料 模具材料这是沙子,沙子,沙子.支持矢量机器 (SVM) 的使用更多相关视频
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