An online intelligent algorithm pipeline for the elimination of springback effect during sheet metal bending

Dilan, Rasim Askin; Balkan, Tuna; Platin, Bülent E.

doi:10.1016/j.proeng.2017.10.1081

“…Naranje et al (2016) estimated the life of deep drawing die with an ANN model. Dilan et al (2017) examined the springback effect of sheet metal bending process through an ANN strategy. Aleyasin (2017) proposed ANFIS algorithm to determine forming limit diagram (FLD) of sheet metals.…”

Section: Introductionmentioning

confidence: 99%

Prediction of life of piercing punches using artificial neural network and adaptive neuro fuzzy inference systems

Salunkhe

¹

,

Rajamani

²

,

Esakki

³

et al. 2019

IJMATEI

0

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Predicting the life of piercing punch is one of the major concerns in the design of compound dies. Finite element analysis is performed to determine the maximum and minimum principal stresses through which fatigue limit of punch is estimated. The factors affecting the life of punch are examined and a mathematical model is established using artificial neural network (ANN) and adaptive neuro fuzzy inference systems (ANFIS). The developed model is utilised to evaluate the life of punch for varied load conditions. Comparative evaluation of ANN and ANFIS results suggested that the later model is superior in predicting the life of punch and it can be effectively utilised in machine tool applications.

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“…Naranje et al (2016) estimated the life of deep drawing die with an ANN model. Dilan et al (2017) examined the springback effect of sheet metal bending process through an ANN strategy. Aleyasin (2017) proposed ANFIS algorithm to determine forming limit diagram (FLD) of sheet metals.…”

Section: Introductionmentioning

confidence: 99%

Prediction of life of piercing punches using artificial neural network and adaptive neuro fuzzy inference systems

Salunkhe

¹

,

Rajamani

²

,

Esakki

³

et al. 2019

IJMATEI

3

0

View full text Add to dashboard Cite

Predicting the life of piercing punch is one of the major concerns in the design of compound dies. Finite element analysis is performed to determine the maximum and minimum principal stresses through which fatigue limit of punch is estimated. The factors affecting the life of punch are examined and a mathematical model is established using artificial neural network (ANN) and adaptive neuro fuzzy inference systems (ANFIS). The developed model is utilised to evaluate the life of punch for varied load conditions. Comparative evaluation of ANN and ANFIS results suggested that the later model is superior in predicting the life of punch and it can be effectively utilised in machine tool applications.

show abstract

Comparison of Artificial Neural Network and Adaptive Neuro Fuzzy Inference Systems for Predicting the Life of Blanking Punch

Salunkhe

¹

,

Rajamani

²

,

Esakki

³

2018

Advanced Structured Materials

1

0

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An online intelligent algorithm pipeline for the elimination of springback effect during sheet metal bending

Cited by 4 publications

References 10 publications

Prediction of life of piercing punches using artificial neural network and adaptive neuro fuzzy inference systems

Prediction of life of piercing punches using artificial neural network and adaptive neuro fuzzy inference systems

Prediction of life of piercing punches using artificial neural network and adaptive neuro fuzzy inference systems

Comparison of Artificial Neural Network and Adaptive Neuro Fuzzy Inference Systems for Predicting the Life of Blanking Punch

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