| Title: | Prediction of dimensional deviation of workpiece using regression, ANN and PSO models in turning operation |
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| Authors: | ID Močnik, David (Author)
ID Paulič, Matej (Author)
ID Klančnik, Simon (Author)
ID Balič, Jože (Author) |
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| Files: |  Tehnicki_vjesnik_2014_Mocnik_et_al._Prediction_of_dimensional_deviation_of_workpiece_using_regression,_ANN_and_PSO_models_in_turning_ope.pdf (1,17 MB)
MD5: ED3C16ED7508DCFE386AEC9AB110B109
PID: 20.500.12556/dkum/30c30458-a4bd-4c7f-835c-808a983d19d2
 http://hrcak.srce.hr/116575
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | As manufacturing companies pursue higher-quality products, they spend much of their efforts monitoring and controlling dimensional accuracy. In the present work for dimensional deviation prediction of workpiece in turning 11SMn30 steel, the conventional deterministic approach, such as multiple linear regression and two artificial intelligence techniques, back-propagation feed-forward artificial neural network (ANN) and particle swarm optimization (PSO) have been used. Spindle speed, feed rate, depth of cut, pressure of cooling lubrication fluid and number of produced parts were taken as input parameters and dimensional deviation of workpiece as an output parameter. Significance of a single parameter and their interactive influences on dimensional deviation were statistically analysed and values predicted from regression, ANN and PSO models were compared with experimental results to estimate prediction accuracy. A predictive PSO based model showed better predictions than two remaining models. However, all three models can be used for the prediction of dimensional deviation in turning. |
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| Keywords: | artificial neural network, dimensional dviation, particle swarm optimization, regression |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Year of publishing: | 2014 |
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| Number of pages: | str. 55-62 |
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| Numbering: | Letn. 21, št. 1 |
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| PID: | 20.500.12556/DKUM-66823  |
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| ISSN: | 1330-3651 |
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| UDC: | 004.89:621.9 |
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| ISSN on article: | 1330-3651 |
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| COBISS.SI-ID: | 17628438 |
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| NUK URN: | URN:SI:UM:DK:2M4RH0MS |
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| Publication date in DKUM: | 12.07.2017 |
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| Views: | 1401 |
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| Downloads: | 201 |
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| Metadata: |    |
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| Categories: | Misc.
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