Prediction of Temperature Distribution during Drilling Process Using Artificial Neural Network

Drilling Stuck Pipe Prediction in Iranian Oil Fields: An Artificial Neural Network Approach

Prediction of the changes in physicochemical properties of key lime juice during IR thermal processing by artificial neural networks

Thermal processing of the key lime juice leads to the inactivation of pectin methylesterase (PME) and the degradation of ascorbic acid (AA). These changes affect directly the cloud stability and color of the juice. In this study, an artificial neural network (ANN) model was applied for designing and developing an intelligent system for prediction of the thermal processing effects on the physico...

Prediction of forging force and barreling behavior in isothermal hot forging of AlCuMgPb aluminum alloy using artificial neural network

In the present investigation, an artificial neural network (ANN) model is developed to predict the isothermal hot forging behavior of AlCuMgPb aluminum alloy. The inputs of the ANN are deformation temperature, frictional factor, ram velocity and displacement whereas the forging force, barreling parameter and final shape are considered as the output variable. The developed feed-forward back-prop...

Prediction of polyvinyl alcohol (PVOH) properties synthesized at various conditions by artificial neural networks technique

In this research samples of PVOH were synthesized at various reaction conditions (temperature, time, and amount of catalyst). First at 25˚C and 45˚C and constant catalyst weight samples of PVOH were prepared with different degree of hydrolysis at various times. For investigation of the effects of temperature, at times 20 and 40 min and constant weight of catalyst PVOH was prepared at various te...

Prediction of the Liquid Vapor Pressure Using the Artificial Neural Network-Group Contribution Method

In this paper, vapor pressure for pure compounds is estimated using the Artificial Neural Networks and a simple Group Contribution Method (ANN–GCM). For model comprehensiveness, materials were chosen from various families. Most of materials are from 12 families. Vapor pressure data of 100 compounds is used to train, validate and test the ANN-GCM model. Va...

Prediction of Mechanical Properties of TWIP Steels using Artificial Neural Network Modeling

In recent years, great attention has been paid to the development of high manganese austenitic TWIP steels exhibiting high tensile strength and exceptional total elongation. Due to low stacking fault energy (SFE), cross slip becomes more difficult in these steels and mechanical twinning is then the favored deformation mode besides dislocation gliding. Chemical composition along with processing ...