20.500.12556/DKUM-66823 Prediction of dimensional deviation of workpiece using regression, ANN and PSO models in turning operation Predviđanje dimenzionalnih devijacija obratka primjenom regresijskih, ANN i PSO modela u postupku tokarenja 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. Budući da proizvodna poduzeća traže kvalitetnije proizvode, mnogo svojih napora troše na praćenje i reguliranje dimenzionalne točnosti. U ovom je radu za predviđanje dimenzionalne devijacije obratka pri tokarenju 11SMn30 čelika, primijenjen konvencionalni deterministički pristup, na primjer metoda višestruke linearne regresije i dvije metode umjetne inteligencije, "back-propagation feed-forward" umjetna neuronska mreža (ANN) i optimizacija roja čestica (PSO). Kao ulazni parametri uzeti su brzina osovine, brzina napajanja, dubina rezanja, tlak rashladnog fluida za podmazivanje i broj proizvedenih dijelova , a dimenzijska devijacija obratka kao izlazni parameter. Značaj pojedinih parametara i njihovi međusobni utjecaji na dimenzionalnu devijaciju su statistički analizirani, a vrijednosti predviđene regresijskim, ANN i PSO modelima uspoređene su s eksperimentalnim rezultatima kako bi se ocijenila točnost predviđanja. Model predviđanja zasnovan na PSO pokazao se boljim od druga dva modela. Međutim, sva se tri modela mogu koristiti za predviđanje dimenzionalnih devijacija kod tokarenja. artificial neural network dimensional dviation particle swarm optimization regression umetna inteligenca optimizacija z rojem delcev inteligenca rojev regresija true false true Angleški jezik Hrvaški jezik Znanstveno delo 2017-07-12 09:28:55 2017-07-12 09:30:09 2022-08-01 13:12:43 2019-07-11 16:00:06 2014 0 0 str. 55-62 št. 1 Letn. 21 2014 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 1330-3651 004.89:621.9 15346181 17628438 1330-3651 URN:SI:UM:DK:2M4RH0MS Tehnicki_vjesnik_2014_Mocnik_et_al._Prediction_of_dimensional_deviation_of_workpiece_using_regression,_ANN_and_PSO_models_in_turning_ope.pdf Tehnicki_vjesnik_2014_Mocnik_et_al._Prediction_of_dimensional_deviation_of_workpiece_using_regression,_ANN_and_PSO_models_in_turning_ope.pdf 1 ED3C16ED7508DCFE386AEC9AB110B109 09b553f97c6791879b1a80899b8db12d25669e304a7a3632f6a252dc1ccda7e0 66c0c97d-7c0e-11eb-bb7a-00155d0001ca 20.500.12556/dkum/30c30458-a4bd-4c7f-835c-808a983d19d2 https://dk.um.si/Dokument.php?lang=slv&id=114059 http://hrcak.srce.hr/116575 1 https://dk.um.si/Dokument.php?lang=slv&id=114058 Fakulteta za strojništvo 0 0 0