Analysis of conical slump shape reconstructed from stereovision images for yield stress prediction

2021 ◽  
Vol 150 ◽  
pp. 106601
Author(s):  
Hong Li ◽  
Aixiang Wu ◽  
Haiyong Cheng
Keyword(s):  
Yield Stress ◽  
Stress Prediction

An unsupervised clustering approach for yield stress prediction during flat rolling

2012 ◽  
Vol 45 (23) ◽  
pp. 50-55 ◽  
Author(s):  
Francesco A. Cuzzola ◽  
Claudio Aurora ◽  
Daniele Sclauzero
Keyword(s):  
Yield Stress ◽  
Unsupervised Clustering ◽  
Flat Rolling ◽  
Stress Prediction ◽  
Clustering Approach

A Model for Prediction of Yield Stress of Heat Treated Al-7Si-Mg Cast Alloys

2016 ◽  
Vol 850 ◽  
pp. 409-418
Author(s):  
Qing Yan Xu ◽  
Rui Chen ◽  
Yu Feng Shi ◽  
Bai Cheng Liu
Keyword(s):  
Yield Stress ◽  
Cast Alloy ◽  
Precipitation Kinetic ◽  
Physical Metallurgy ◽  
Precipitated Phase ◽  
Heat Treated ◽  
Physically Based ◽  
Stress Prediction ◽  
Cast Alloys ◽  
Cast Microstructure

In the present investigation, a physically based numerical model was developed to predict the yield stress of Al-7Si-Mg cast alloy during processing. It covered the integrated unit step models of the physical metallurgy of solidification, solid-state of homogenization, and structural hardening of precipitation. The as-cast microstructure of Al-7Si-Mg alloy was calculated based on the cellular automaton method and the evolution of the precipitated phase during aging process was achieved by a precipitation kinetic model involved nucleation, growth and coarsening. The yield stress prediction was achieved by a strengthening model including the effects of as-cast microstructure, solution strengthening and precipitate hardening. The predictions of this model were verified by comparing with experimental measured yield stress which shows that this model is successfully applied to predict the yield stress evolution of Al-7Si-Mg cast alloy.

A novel PCA-DC-Bagging algorithm on yield stress prediction of RAFM steel

Computing ◽  
2019 ◽  
Vol 102 (1) ◽  
pp. 19-42
Author(s):  
Sifan Long ◽  
Ming Zhao ◽  
Jieqiong Song
Keyword(s):  
Yield Stress ◽  
Stress Prediction ◽  
Rafm Steel

Rancang Bangun Sistem Pakar Prediksi Stres Belajar Dengan Neural Network Algoritma Backpropagation

2016 ◽  
Vol 7 (2) ◽  
pp. 105-112
Author(s):  
Adhi Kusnadi ◽  
Idul Putra
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Stress Level ◽  
Network Structure ◽  
Human Being ◽  
Accuracy Rate ◽  
Backpropagation Algorithm ◽  
Stress Prediction ◽  
Index Terms ◽  
Hidden Nodes

Stress will definitely be experienced by every human being and the level of stress experienced by each individual is different. Stress experienced by students certainly will disturb their study if it is not handled quickly and appropriately. Therefore we have created an expert system using a neural network backpropagation algorithm to help counselors to predict the stress level of students. The network structure of the experiment consists of 26 input nodes, 5 hidden nodes, and 2 the output nodes, learning rate of 0.1, momentum of 0.1, and epoch of 5000, with a 100% accuracy rate. Index Terms - Stress on study, expert system, neural network, Stress Prediction

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