Asphalt compaction quality control using Artificial Neural Network

2010 ◽  
Author(s):  
Fares Beainy ◽  
Sesh Commuri ◽  
Musharraf Zaman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Quality Control ◽  
Artificial Neural

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

scholarly journals Real-Time Quality Control of Heat Sealed Bottles Using Thermal Images and Artificial Neural Network

2021 ◽  
Vol 7 (2) ◽  
pp. 24
Author(s):  
Samuel Cruz ◽  
António Paulino ◽  
Joao Duraes ◽  
Mateus Mendes
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Quality Control ◽  
Industrial Plants ◽  
Non Invasive ◽  
Processing Plants ◽  
Inspection Process ◽  
Conveyor Belts ◽  
Artificial Neural

Quality control of heat sealed bottles is very important to minimize waste and in some cases protect people’s health. The present paper describes a case study where an automated non invasive and non destructive quality control system was designed to assess the quality of the seals of bottles containing pesticide. In this case study, the integrity of the seals is evaluated using an artificial neural network based on images of the seals processed with computer vision techniques. Because the seals are not directly visible from the bottle exterior, the images are infrared pictures obtained using a thermal camera. The method is non invasive, automated, and can be applied to common conveyor belts currently used in industrial plants. The results show that the inspection process is effective in identifying defective seals with a precision of 98.6% and a recall of 100% and because it is automated it can be scaled up to large bottle processing plants.

scholarly journals Real-Time Quality Control and Infilling of Precipitation Data Using Neural Networks

10.29007/t5k7 ◽  
2018 ◽  
Author(s):  
Mohammadreza Moslemi ◽  
Darko Joksimovic
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Quality Control ◽  
Real Time ◽  
Network Model ◽  
Neural Network Model ◽  
Superior Performance ◽  
Precipitation Data ◽  
Artificial Neural

Due to advancements in instrumentation and communication technologies, monitoring of water infrastructure is experiencing a significant growth worldwide and water managers are increasingly deploying monitoring equipment for decision-making purposes. Hydrological events and relevant datasets including rainfall data are of a complex nature and are potentially susceptible to errors from various sources. Hence, it is essential to develop efficient methods for the quality control of the acquired data. The present work introduces an artificial neural network-based approach for real-time quality control and infilling of rain gauge data. Available rainfall measurements from neighboring rain gauges are employed to train and develop the neural network model. Trained artificial neural network model was able to validate up to about 97% of the data using 95% confidence intervals. This finding suggests that artificial neural networks can be successfully implemented for erroneous data identification/correction and reconstruction of missing data points. Given its short processing time and reportedly superior performance to traditional quality control strategies, neural network methodology can be deployed as an efficient tool for the processing and control of large sets of timeseries with complex natures including precipitation data.

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