Material identification based on machine-learning algorithms for hybrid workpieces during cylindrical operations

被引：0
作者：
Berend Denkena
Benjamin Bergmann
Matthias Witt
机构：
[1] Leibniz Universität Hannover,Institute of Production Engineering and Machine Tools
来源：
Journal of Intelligent Manufacturing | 2019年 / 30卷
关键词：
Machine-learning; Monitoring; Turning; Hybrid parts;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
New design concepts for high-performance components are part of the current research. Because of various material properties and chemical composition, the cutting characteristics and chip formation mechanisms change during the machining process. Thus, it can be mandatory to identify the material and adapt the process parameters during machining. As a result, the workpiece quality is optimized while increasing the tool life. Therefore, this paper investigates a new approach to determine the machined material in-process by machine-learning. A cylindrical turning process is performed for friction welded EN-AW6082/20MnCr5 and C22/41Cr4 shafts. Acceleration and process force signals as well as control signals are measured and monitoring features are generated. These features are ranked and selected based on the information value by the joint mutual information method. Afterwards, four machine-learning models are trained to identify the machined material based on the signal features. The monitoring quality is evaluated during various cylindrical turning processes and the most appropriate machine-learning algorithm is determined. Thus, a new methodology for in-process material identification in CNC turning machines based on signal analysis and machine-learning algorithm is proposed.
引用
收藏
页码：2449 / 2456
页数：7
相关论文
共 58 条
  • [1] Altman NS(1992)An introduction to kernel and nearest-neighbor nonparametric regression The American Statistician 46 175-185
  • [2] Battiti R(1994)Using mutual information for selecting features in supervised neural netlearning IEEE Transactionson Neural Networks 5 537-550
  • [3] Bennasar M(2015)Feature selection using joint mutual information maximization Expert Systems with Applications 42 8520-8532
  • [4] Hicks Y(2017)Investigation of the coating thickness of plasma-transferred arc deposition welded and cross wedge rolled hybrid parts Production Engineering Research and Development 11 255-263
  • [5] Setchi R(2012)Conditional likelihood maximisation: A unifying framework for information theoretic feature selection Journal of Machine Learning Research 13 27-66
  • [6] Blohm T(2005)Tool breakage detection using support vector machine learning in a milling process International Journal of Machine Tools and Manufacture 45 241-249
  • [7] Mildebrath M(1995)Support vector networks Machine Learning 20 273-297
  • [8] Stonis M(2009)Normalized mutual information feature selection IEEE Transactionson Neural Networks 20 189-201
  • [9] Langner J(2009)Tool breakage diagnosis in face milling by support vector machine Journal of Material Processing Technology 209 145-152
  • [10] Hassel T(2017)Monitoring tool wear using classifier fusion Mechanical Systems and Signal Processing 85 651-661
123456