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

James Marcus Griffin, Fernando Torres

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)935-953
Number of pages19
JournalThe International Journal of Advanced Manufacturing Technology
Volume81
Issue number5-8
DOIs
Publication statusPublished - 2015

Fingerprint

Acoustic emissions
Plastic deformation
Signal to noise ratio
Titanium
Plastics
Neural networks
Geometry
Sensors
Air

Keywords

  • Acoustic emission
  • Feature extraction
  • Precision control
  • Single-grit scratch
  • CART
  • Neural networks
  • Simulations
  • Embedded controllers

Cite this

Dynamic precision control in single-grit scratch tests using acoustic emission signals. / Griffin, James Marcus; Torres, Fernando.

In: The International Journal of Advanced Manufacturing Technology, Vol. 81, No. 5-8, 2015, p. 935-953.

Research output: Contribution to journalArticle

@article{f924b280f65749f984b323646cb300b4,
title = "Dynamic precision control in single-grit scratch tests using acoustic emission signals",
abstract = "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.",
keywords = "Acoustic emission, Feature extraction, Precision control, Single-grit scratch, CART, Neural networks, Simulations, Embedded controllers",
author = "Griffin, {James Marcus} and Fernando Torres",
year = "2015",
doi = "10.1007/s00170-015-7081-7",
language = "English",
volume = "81",
pages = "935--953",
journal = "The International Journal of Advanced Manufacturing Technology",
issn = "0268-3768",
publisher = "Springer Verlag",
number = "5-8",

}

TY - JOUR

T1 - Dynamic precision control in single-grit scratch tests using acoustic emission signals

AU - Griffin, James Marcus

AU - Torres, Fernando

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - Acoustic emission

KW - Feature extraction

KW - Precision control

KW - Single-grit scratch

KW - CART

KW - Neural networks

KW - Simulations

KW - Embedded controllers

U2 - 10.1007/s00170-015-7081-7

DO - 10.1007/s00170-015-7081-7

M3 - Article

VL - 81

SP - 935

EP - 953

JO - The International Journal of Advanced Manufacturing Technology

JF - The International Journal of Advanced Manufacturing Technology

SN - 0268-3768

IS - 5-8

ER -