| Naslov: | Prediction of dimensional deviation of workpiece using regression, ANN and PSO models in turning operation |
|---|
| Avtorji: | ID Močnik, David (Avtor)
ID Paulič, Matej (Avtor)
ID Klančnik, Simon (Avtor)
ID Balič, Jože (Avtor) |
|---|
| Datoteke: |  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
|
|---|
| Jezik: | Angleški jezik |
|---|
| Vrsta gradiva: | Znanstveno delo |
|---|
| Tipologija: | 1.01 - Izvirni znanstveni članek |
|---|
| Organizacija: | FS - Fakulteta za strojništvo
|
|---|
| Opis: | 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. |
|---|
| Ključne besede: | artificial neural network, dimensional dviation, particle swarm optimization, regression |
|---|
| Status publikacije: | Objavljeno |
|---|
| Verzija publikacije: | Objavljena publikacija |
|---|
| Leto izida: | 2014 |
|---|
| Št. strani: | str. 55-62 |
|---|
| Številčenje: | Letn. 21, št. 1 |
|---|
| PID: | 20.500.12556/DKUM-66823  |
|---|
| ISSN: | 1330-3651 |
|---|
| UDK: | 004.89:621.9 |
|---|
| COBISS.SI-ID: | 17628438 |
|---|
| ISSN pri članku: | 1330-3651 |
|---|
| NUK URN: | URN:SI:UM:DK:2M4RH0MS |
|---|
| Datum objave v DKUM: | 12.07.2017 |
|---|
| Število ogledov: | 1400 |
|---|
| Število prenosov: | 201 |
|---|
| Metapodatki: |    |
|---|
| Področja: | Ostalo
|
|---|
|
:
|
Kopiraj citat |
|---|
| | | | Skupna ocena: | (0 glasov) |
|---|
| Vaša ocena: | Ocenjevanje je dovoljeno samo prijavljenim uporabnikom. |
|---|
| Objavi na: |  |
|---|
Postavite miškin kazalec na naslov za izpis povzetka. Klik na naslov izpiše
podrobnosti ali sproži prenos. |
