Dynamic precision control in single-grit scratch tests using acoustic emission signals

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• 作者：James Marcus Griffin ; Fernando Torres
• 关键词：Acoustic emission ; Feature extraction ; Precision control ; Single ; grit scratch ; CART ; Neural networks ; Simulations ; Embedded controllers
• 刊名：The International Journal of Advanced Manufacturing Technology
• 出版年：2015
• 出版时间：November 2015
• 年：2015
• 卷：81
• 期：5-8
• 页码：935-953
• 全文大小：4,884 KB
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• 作者单位：James Marcus Griffin (1)
  Fernando Torres (2)
  
  1. Faculty of Engineering and Computing, Engineering and Computing Building, Coventry University, Gulson Road, Coventry, CV1 2JH, UK
  2. Fablab, Department of Mechanical Engineering, School of Physical and Mathematical Sciences, Beauchef 851, University of Chile, Santiago de Chile, 8370456, Chile
  
• 刊物类别：Engineering
• 刊物主题：Industrial and Production Engineering
  Production and Logistics
  Mechanical Engineering
  Computer-Aided Engineering and Design
  
• 出版者：Springer London
• ISSN：1433-3015

文摘

Acoustic emission (AE) is very sensitive to minuscule molecular changes which allow it to be used in a dynamic control manner. The work presented here specifically investigates approaching grit and workpiece interaction during grinding processes. The single grit (SG) tests used in this work display that the intensities from air, occurring in between the grit and workpiece, show an increasing intensity as the grit tends towards the workpiece with 1-μm increments. As the grit interacts with the workpiece, a scratch is formed; different intensities are recorded with respect to a changing measured depth of cut (DOC). In the first instance, various AE were low tending towards high signal to noise ratios which is indicative of grit approaching contact; when contact is made, frictional rubbing is noticed, then ploughing with low DOC and, finally, actual cutting with a higher associated DOC. Dynamic control is obtained from the AE sensor extracting increasing amplitude significant of elastic changing towards greater plastic material deformation. Such control methods can be useful for grinding dressing ratios as well as achieving near optimal surface finish when faced with difficult to cut geometries. Two different materials were used for the same SG tests (aerospace alloys: CMSX4 and titanium-64) to verify that the control regime is robust and not just material dependent. The AE signals were then classified using neural networks (NNs) and classification and regression trees (CART)-based rules. A real-time simulation is provided showing such interactions allowing dynamic micro precision control. The results show clear demarcation between the extracted synthesized signals ensuring high accuracy for determining different phenomena: 3–1 μm approaching touch, touch, slight plastic deformation and, increasing plastic deformation. In addition to dressing ratios, the results are also important for micron accuracy set-up considerations.