Algorithms based on neural network for segmentation of defects on metal sheet images

2021 ◽  
Author(s):  
Sergei Repin ◽  
Alexander Kupriyanov
Keyword(s):  
Neural Network ◽  
Metal Sheet

scholarly journals A Multi-State Model for Reliability Analysis of Metal Sheet Manufacturing Process Using Artificial Neural Network Technique

2020 ◽  
Vol 28 (4) ◽  
Author(s):  
Anil Chandra ◽  
Surbhi Gupta ◽  
Chandra Kant Jaggi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reliability Analysis ◽  
Manufacturing Process ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Metal Sheet ◽  
Time Period ◽  
Artificial Neural ◽  
Over Time

A manufacturing system is governed by its various processes upon which its efficiency is dependent. Since failure results in considerable losses, many manufacturing systems have certain redundancies for some processes. These redundancies cause the system to work under different efficiency states called multi-state elements. In this paper, various processes of metal sheet manufacturing unit have been categorized as subsystems to determine the multi-state probabilities of its different efficiency states. Artificial Neural Network Technique (ANN) has been used to estimate the change in these multi-state probabilities over time. The ANN has also been used to estimate variation in upstate and downstate probabilities of the system for a particular-time period. The results have been used to determine variation in profit over time for the system.

Parameter selection by an artificial neural network for a laser bending process

2002 ◽  
Vol 216 (11) ◽  
pp. 1517-1520 ◽  
Author(s):  
G Casalino ◽  
A D Ludovico
Keyword(s):  
Neural Network ◽  
Model Simulation ◽  
Numerical Models ◽  
Back Propagation ◽  
Metal Sheet ◽  
Analytical Models ◽  
Processing Unit ◽  
Feed Forward Neural Network ◽  
Laser Bending ◽  
Central Processing

Based on thermally induced plastic deformations produced by laser irradiation, metal sheet laser bending can be a valid alternative to dies for rapid prototyping and manufacturing. Some numerical models have been built in order to improve the understanding and prediction of mechanisms. Drawbacks entailed with those models have been found. Finite element model simulation has proved to be time and CPU (central processing unit) memory consuming. The analytical models have been cumbersome and unsatisfactory. Nowadays, it is possible to build a neural network model for process modelling directly from data collected during the experiments. In this paper a feed-forward neural network with a back-propagation learning function has been designed and its performances have been evaluated for metal sheet laser bending. This technique has proved to be effective and efficient, providing the process parameters that are necessary to achieve a desired bending angle.

Improved knowledge-based neural network (KBNN) model for predicting spring-back angles in metal sheet bending

2014 ◽  
Vol 05 (02) ◽  
pp. 1350026 ◽  
Author(s):  
Tinh Quoc Bui ◽  
Anh Viet Tran ◽  
Abid Ali Shah
Keyword(s):  
Neural Network ◽  
Empirical Model ◽  
Metal Sheet ◽  
Spring Back ◽  
Element Analysis ◽  
Knowledge Based ◽  
Surface Method ◽  
Sheet Bending ◽  
Artificial Neural Network Ann ◽  
The Relationship

We develop an efficiently improved knowledge-based neural network (KBNN) associated with optimization algorithms and finite element analysis (FEA) to accurately predict spring-back angles in metal sheet bending. The well-known V and U prevalent processes of bending are considered. The KBNN predictive results are based on the empirical model and artificial neural network (ANN) modeling. The empirical model is constructed from the FEA results using response surface method, while the multilayer perceptron is employed to create the ANN. The trained KBNN can accurately model the relationship between the spring-back angles and process parameters. The obtained results are validated against other existing methods showing a high accuracy.

Neural Network Approach for Prediction of Wrinkling Limit in Square Metal Sheet Under Diagonal Tension

1997 ◽  
Vol 25 (1) ◽  
pp. 74 ◽  
Author(s):  
DR Petersen ◽  
RE Link ◽  
S Di ◽  
PF Thomson
Keyword(s):  
Neural Network ◽  
Metal Sheet ◽  
Network Approach ◽  
Neural Network Approach

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Three-Dimensional Cognitive Mapping With a Neural Network

2000 ◽  
Author(s):  
Horatiu Voicu ◽  
Nestor Schmajuk
Keyword(s):  
Neural Network ◽  
Cognitive Mapping ◽  
Three Dimensional
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