Development of an intelligent model to optimize heat-affected zone, kerf, and roughness in 309 stainless steel plasma cutting by using experimental results.
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| Title: | Development of an intelligent model to optimize heat-affected zone, kerf, and roughness in 309 stainless steel plasma cutting by using experimental results. |
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| Authors: | Masoudi, Soroush1 Smasoudi86@gmail.com, Mirabdolahi, Mostafa2, Dayyani, Mohammad3, Jafarian, Farshid4, Vafadar, Ana5, Dorali, Mohammad Reza6 |
| Source: | Materials & Manufacturing Processes. 2019, Vol. 34 Issue 3, p345-356. 12p. |
| Subjects: | Stainless steel testing, Surface roughness, Plasma gases, Genetic algorithms, Artificial neural networks, Cutting (Materials) |
| Abstract: | Plasma cutting is an effective way to cut hard metals. In this process, three output parameters cutting width (kerf), surface roughness (Ra) and heat-affected zone (HAZ) are critical factors which affect the quality and efficiency of the cutting. In this paper, an experimental study was conducted to investigate the cutting quality in terms of kerf, Ra, and HAZ for the 309 stainless steel plasma cutting. First, the research tested the effect of input parameters including current, gas pressure, and cutting speed on the process outputs. Then, the results were used to develop three predictive models by intelligent systems based on genetic algorithm (GA) and artificial neural network (ANN). Finally, a hybrid technique of genetically optimized neural network systems (GONNs) was designed and employed to simultaneously optimize the process outputs. The results show that the implemented strategy is an effective method for optimizing the output parameters in the plasma cutting process. [ABSTRACT FROM AUTHOR] |
| Copyright of Materials & Manufacturing Processes is the property of Taylor & Francis Ltd 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 134345543 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Development of an intelligent model to optimize heat-affected zone, kerf, and roughness in 309 stainless steel plasma cutting by using experimental results. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Masoudi%2C+Soroush%22">Masoudi, Soroush</searchLink><relatesTo>1</relatesTo><i> Smasoudi86@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Mirabdolahi%2C+Mostafa%22">Mirabdolahi, Mostafa</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Dayyani%2C+Mohammad%22">Dayyani, Mohammad</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Jafarian%2C+Farshid%22">Jafarian, Farshid</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Vafadar%2C+Ana%22">Vafadar, Ana</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Dorali%2C+Mohammad+Reza%22">Dorali, Mohammad Reza</searchLink><relatesTo>6</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Materials+%26+Manufacturing+Processes%22">Materials & Manufacturing Processes</searchLink>. 2019, Vol. 34 Issue 3, p345-356. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Stainless+steel+testing%22">Stainless steel testing</searchLink><br /><searchLink fieldCode="DE" term="%22Surface+roughness%22">Surface roughness</searchLink><br /><searchLink fieldCode="DE" term="%22Plasma+gases%22">Plasma gases</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Cutting+%28Materials%29%22">Cutting (Materials)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Plasma cutting is an effective way to cut hard metals. In this process, three output parameters cutting width (kerf), surface roughness (Ra) and heat-affected zone (HAZ) are critical factors which affect the quality and efficiency of the cutting. In this paper, an experimental study was conducted to investigate the cutting quality in terms of kerf, Ra, and HAZ for the 309 stainless steel plasma cutting. First, the research tested the effect of input parameters including current, gas pressure, and cutting speed on the process outputs. Then, the results were used to develop three predictive models by intelligent systems based on genetic algorithm (GA) and artificial neural network (ANN). Finally, a hybrid technique of genetically optimized neural network systems (GONNs) was designed and employed to simultaneously optimize the process outputs. The results show that the implemented strategy is an effective method for optimizing the output parameters in the plasma cutting process. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Materials & Manufacturing Processes is the property of Taylor & Francis Ltd 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: 10.1080/10426914.2018.1532579 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 345 Subjects: – SubjectFull: Stainless steel testing Type: general – SubjectFull: Surface roughness Type: general – SubjectFull: Plasma gases Type: general – SubjectFull: Genetic algorithms Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Cutting (Materials) Type: general Titles: – TitleFull: Development of an intelligent model to optimize heat-affected zone, kerf, and roughness in 309 stainless steel plasma cutting by using experimental results. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Masoudi, Soroush – PersonEntity: Name: NameFull: Mirabdolahi, Mostafa – PersonEntity: Name: NameFull: Dayyani, Mohammad – PersonEntity: Name: NameFull: Jafarian, Farshid – PersonEntity: Name: NameFull: Vafadar, Ana – PersonEntity: Name: NameFull: Dorali, Mohammad Reza IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: 2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 10426914 Numbering: – Type: volume Value: 34 – Type: issue Value: 3 Titles: – TitleFull: Materials & Manufacturing Processes Type: main |
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