Neural network modeling and analysis of the material removal process during laser machining.
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| Title: | Neural network modeling and analysis of the material removal process during laser machining. |
|---|---|
| Authors: | Yousef, Basem F.1, Knopf, George K.1 gknopf@engga.uwo.ca, Bordatchev, Evgueni V.2, Nikumb, Suwas K.2 |
| Source: | International Journal of Advanced Manufacturing Technology. Sep2003, Vol. 22 Issue 1/2, p41-53. 13p. |
| Subjects: | Manufacturing processes, Artificial neural networks, Models & modelmaking, Process control systems, Machining, Laser beams |
| Abstract: | To manufacture parts with nano- or micro-scale geometry using laser machining, it is essential to have a thorough understanding of the material removal process in order to control the system behaviour. At present, the operator must use trial-and-error methods to set the process control parameters related to the laser beam, motion system, and work piece material. In addition, dynamic characteristics of the process that cannot be controlled by the operator such as power density fluctuations, intensity distribution within the laser beam, and thermal effects can significantly influence the machining process and the quality of part geometry. This paper describes how a multi-layered neural network can be used to model the nonlinear laser micro-machining process in an effort to predict the level of pulse energy needed to create a dent or crater with the desired depth and diameter. Laser pulses of different energy levels are impinged on the surface of several test materials in order to investigate the effect of pulse energy on the resulting crater geometry and the volume of material removed. The experimentally acquired data is used to train and test the neural network's performance. The key system inputs for the process model are mean depth and mean diameter of the crater, and the system outputs are pulse energy, variance of depth and variance of diameter. This study demonstrates that the proposed neural network approach can predict the behaviour of the material removal process during laser machining to a high degree of accuracy. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 16717924 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Neural network modeling and analysis of the material removal process during laser machining. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yousef%2C+Basem+F%2E%22">Yousef, Basem F.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Knopf%2C+George+K%2E%22">Knopf, George K.</searchLink><relatesTo>1</relatesTo><i> gknopf@engga.uwo.ca</i><br /><searchLink fieldCode="AR" term="%22Bordatchev%2C+Evgueni+V%2E%22">Bordatchev, Evgueni V.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Nikumb%2C+Suwas+K%2E%22">Nikumb, Suwas K.</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Advanced+Manufacturing+Technology%22">International Journal of Advanced Manufacturing Technology</searchLink>. Sep2003, Vol. 22 Issue 1/2, p41-53. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Manufacturing+processes%22">Manufacturing processes</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Models+%26+modelmaking%22">Models & modelmaking</searchLink><br /><searchLink fieldCode="DE" term="%22Process+control+systems%22">Process control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Machining%22">Machining</searchLink><br /><searchLink fieldCode="DE" term="%22Laser+beams%22">Laser beams</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To manufacture parts with nano- or micro-scale geometry using laser machining, it is essential to have a thorough understanding of the material removal process in order to control the system behaviour. At present, the operator must use trial-and-error methods to set the process control parameters related to the laser beam, motion system, and work piece material. In addition, dynamic characteristics of the process that cannot be controlled by the operator such as power density fluctuations, intensity distribution within the laser beam, and thermal effects can significantly influence the machining process and the quality of part geometry. This paper describes how a multi-layered neural network can be used to model the nonlinear laser micro-machining process in an effort to predict the level of pulse energy needed to create a dent or crater with the desired depth and diameter. Laser pulses of different energy levels are impinged on the surface of several test materials in order to investigate the effect of pulse energy on the resulting crater geometry and the volume of material removed. The experimentally acquired data is used to train and test the neural network's performance. The key system inputs for the process model are mean depth and mean diameter of the crater, and the system outputs are pulse energy, variance of depth and variance of diameter. This study demonstrates that the proposed neural network approach can predict the behaviour of the material removal process during laser machining to a high degree of accuracy. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00170-002-1441-9 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 41 Subjects: – SubjectFull: Manufacturing processes Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Models & modelmaking Type: general – SubjectFull: Process control systems Type: general – SubjectFull: Machining Type: general – SubjectFull: Laser beams Type: general Titles: – TitleFull: Neural network modeling and analysis of the material removal process during laser machining. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yousef, Basem F. – PersonEntity: Name: NameFull: Knopf, George K. – PersonEntity: Name: NameFull: Bordatchev, Evgueni V. – PersonEntity: Name: NameFull: Nikumb, Suwas K. IsPartOfRelationships: – BibEntity: Dates: – D: 08 M: 09 Text: Sep2003 Type: published Y: 2003 Identifiers: – Type: issn-print Value: 02683768 Numbering: – Type: volume Value: 22 – Type: issue Value: 1/2 Titles: – TitleFull: International Journal of Advanced Manufacturing Technology Type: main |
| ResultId | 1 |
