Original scientific paper

Prediction of dimensional deviation of workpiece using regression, ANN and PSO models in turning operation

David Mocnik ; Techne d.o.o., Rakičan, Panonska ulica 36, 9000 Murska Sobota, Slovenia
Matej Paulic ; University of Maribor, Faculty of Mechanical Engineering, Smetanova ulica 17, 2000 Maribor, Slovenia
Simon Klancnik ; University of Maribor, Faculty of Mechanical Engineering, Smetanova ulica 17, 2000 Maribor, Slovenia
Joze Balic ; University of Maribor, Faculty of Mechanical Engineering, Smetanova ulica 17, 2000 Maribor, Slovenia

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.

Keywords

artificial neural network; dimensional deviation; particle swarm optimization; regression

Hrčak ID:

116575

URI

https://hrcak.srce.hr/116575

Publication date:

21.2.2014.

Article data in other languages: croatian

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