scholarly journals DESIGN OF AUTOMATION SYSTEM FOR CERAMIC SURFACE QUALITY CONTROL USING ARTIFICIAL NEURAL NETWORK AT BALAI BESAR KERAMIK

2019 ◽  
Author(s):  
Puspita Ayu Lestari ◽  
Haris Rachmat ◽  
Mohd. Rasidi Ibrahim ◽  
Denny Sukma Eka Atmaja
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Quality Control ◽  
Surface Quality ◽  
Automation System ◽  
Ceramic Surface ◽  
Artificial Neural ◽  
Surface Quality Control

Ann Modelling for the Analysis of the Green Moulding Sands Properties

2013 ◽  
Vol 58 (3) ◽  
pp. 961-963 ◽  
Author(s):  
J. Jakubski ◽  
P. Malinowski ◽  
St.M. Dobosz ◽  
G. Major-Gabrýs
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Quality Control ◽  
Artificial Neural Networks ◽  
Moisture Content ◽  
Environmental Humidity ◽  
Sand Temperature ◽  
Artificial Neural ◽  
Moulding Sand

Abstract Application of modern technological solutions, as well as the economic and ecological solutions, is for foundries one of the main aspects of the competitiveness on the market for castings. IT solutions can significantly support technological processes. This article presents neural networks with different structures that have been used to determine the moisture content of the moulding sand based on the moulding sand selected properties research results. Neural networks were built using Matlab software. Moulding sand properties chosen for quality control processes were selected based on wide previous results. For the proposed moulding sand properties, neural networks can be a useful tool for predicting moisture content. The structure of artificial neural network do not have a significant influence on the obtained results. In subsequent studies on the use of neural networks as an application to support the green moulding sand rebonding process, it must be determined how factors such as environmental humidity and moulding sand temperature will affect the accuracy of data obtained with the use of artificial neural networks.

scholarly journals Artificial neuronal networks for optical inspection in PCB quality control

2017 ◽  
Vol 11 (2) ◽  
pp. 169-178
Author(s):  
Katherin Rodríguez Cadena ◽  
Frank Nixon Giraldo Ramos
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Artificial Neural Network ◽  
Quality Control ◽  
Research Work ◽  
Problem Definition ◽  
Circuit Board ◽  
Theoretical Concepts ◽  
Optical Inspection ◽  
Artificial Neural

This paper is the result of the research work on the application of an artificial neural network algorithm applied in decision making in the process of AIO (Automatic Optical Inspection) for quality control from an electronic prototyping company, generating models for the assurance of Quality in the PCB (Printed Circuit Board) product, covering the fields of decision making, quality management, production processes, neural computer systems and artificial vision among others. It is intended to develop an algorithm of artificial neural networks that provides an approach to human recognition and perception when performing a quality inspection of the final product, based on image analysis and recognition. It is presented the theoretical concepts explored and the results obtained. Initially a problem definition was made to model, then the data processing was performed, the artificial neural network model was selected to be applied, then the relevant adjustments made to the model to finally obtain a simulation and validation of the same

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