@ARTICLE{Oraon_Manish_Application_2021,
 author={Oraon, Manish and Sharma, Vinay and Oraon, Manish and Sharma, Vinay},
 volume={vol. 12},
 number={No 1},
 pages={7-23},
 journal={Management and Production Engineering Review},
 howpublished={online},
 year={2021},
 publisher={Production Engineering Committee of the Polish Academy of Sciences, Polish Association for Production Management},
 abstract={Artificial neural network (ANN), a Computational tool that is frequently applied in the modeling and simulation of manufacturing processes. The emerging forming technique of sheet metal which is typically called single point incremental forming (SPIF) comes into the map and the research interest towards its technological parameters. The surface quality of the end product is a major issue in SPIF, which is more critical with the hard metals. The part of the brass metal is demanded in many industrial uses because of its high load-carrying capacity and its wear resistance property. Considering the industrial interest and demand of the brass metal products, the present study is done with the SPIF experiment on calamine brass Cu67Zn33 followed by an ANN analysis for predicting the absolute surface roughness. The modeling result shows a close agreement with the measured data. The minimum and maximum errors are found in experiment 3 and experiment 7 respectively. The error of predicted roughness is found in the range of –30.87 to 20.23 and the overall coefficient of performance of ANN modeling is 0.947 which is quite acceptable.},
 abstract={Artificial neural network (ANN), a Computational tool that is frequently applied in the modeling and simulation of manufacturing processes. The emerging forming technique of sheet metal which is typically called single point incremental forming (SPIF) comes into the map and the research interest towards its technological parameters. The surface quality of the end product is a major issue in SPIF, which is more critical with the hard metals. The part of the brass metal is demanded in many industrial uses because of its high load-carrying capacity and its wear resistance property. Considering the industrial interest and demand of the brass metal products, the present study is done with the SPIF experiment on calamine brass Cu67Zn33 followed by an ANN analysis for predicting the absolute surface roughness. The modeling result shows a close agreement with the measured data. The minimum and maximum errors are found in experiment 3 and experiment 7 respectively. The error of predicted roughness is found in the range of –30.87 to 20.23 and the overall coefficient of performance of ANN modeling is 0.947 which is quite acceptable.},
 type={Article},
 type={Artykuł /Article},
 title={Application of Artificial Neural Network: A Case of Single PointIncremental Forming (SPIF) of Cu67Zn33 Alloy},
 title={Application of Artificial Neural Network: A Case of Single PointIncremental Forming (SPIF) of Cu67Zn33 Alloy},
 URL={http://ochroma.man.poznan.pl/Content/119510/PDF/art02.pdf},
 doi={10.24425/mper.2021.136868},
 keywords={SPIF, input variable, artificial neural network, surface roughness., SPIF, input variable, artificial neural networks, surface roughness},
}