scholarly journals Artificial Neural Network Controlled GMAW System: Penetration and Quality Assurance in a Multi-Pass Butt Weld Application

2019 ◽  
Vol 105 (7-8) ◽  
pp. 3369-3385 ◽  
Author(s):  
Sakari Penttilä ◽  
Paul Kah ◽  
Juho Ratava ◽  
Harri Eskelinen
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Making ◽  
Artificial Neural Network ◽  
Machine Vision ◽  
Vision System ◽  
Welding Parameter ◽  
Parameter Control ◽  
Machine Vision System ◽  
Profile Data

Abstract Intelligent welding parameter control is fast becoming a key instrument for attaining quality consistency in automated welding. Recent scientific breakthroughs in intelligent systems have turned the focus of adaptive welding control to artificial intelligence-based welding parameter control. The aim of this study is to combine artificial neural network (ANN) decision-making software and a machine vision system to develop an adaptive artificial intelligence (AI)-based gas metal arc welding (GMAW) parameter control system. The machine vision system uses a laser sensor to scan the upcoming seam and gather seam profile data. Based on further processing of the seam profile data, welding parameters are optimized by the decision-making system. In this work, the developed system is tested in a multivariable welding condition environment and its performance is evaluated. The quality of the welds was consistent and surpassed the required quality level. Additionally, the heat-affected zone (HAZ) was evaluated by microscopy, X-ray, and scanning electron microscope (SEM) imaging. It is concluded that the developed ANN system is suitable for implementation in automated applications, can improve quality consistency and cost efficiency, and reduce required workpiece preparation and handling.

scholarly journals Design and development of a machine vision system using artificial neural network-based algorithm for automated coal characterization

2020 ◽  
Author(s):  
Amit Kumar Gorai ◽  
Simit Raval ◽  
Ashok Kumar Patel ◽  
Snehamoy Chatterjee ◽  
Tarini Gautam
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Machine Vision ◽  
Vision System ◽  
Color Space ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Machine Vision System ◽  
Coal Characterization ◽  
Artificial Neural

Abstract Coal is heterogeneous in nature, and thus the characterization of coal is essential before its use for a specific purpose. Thus, the current study aims to develop a machine vision system for automated coal characterizations. The model was calibrated using 80 image samples that are captured for different coal samples in different angles. All the images were captured in RGB color space and converted into five other color spaces (HSI, CMYK, Lab, xyz, Gray) for feature extraction. The intensity component image of HSI color space was further transformed into four frequency components (discrete cosine transform, discrete wavelet transform, discrete Fourier transform, and Gabor filter) for the texture features extraction. A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development. The datasets of the optimized features were used as an input for the model, and their respective coal characteristics (analyzed in the laboratory) were used as outputs of the model. The R-squared values were found to be 0.89, 0.92, 0.92, and 0.84, respectively, for fixed carbon, ash content, volatile matter, and moisture content. The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression, support vector regression, and radial basis neural network models. The study demonstrates the potential of the machine vision system in automated coal characterization.

scholarly journals Predicting Fruit Yield Using Shallow Neural Networks

2020 ◽  
Author(s):  
Duke M Bulanon ◽  
Trevor Braddock ◽  
Brice Allen ◽  
Joseph Ichiro Bulanon
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Machine Vision ◽  
Precision Agriculture ◽  
Vision System ◽  
Fruit Trees ◽  
Fruit Yield ◽  
Early Prediction ◽  
Machine Vision System ◽  
Yield Monitoring

Precision agriculture is a technology used by farmers to help food sustainability amidst growing population. One of the tools of precision agriculture is yield monitoring, which helps a farmer manage his production. Yield monitoring is usually done during harvest, however it could also be done early in the growing season. Early prediction of yield, specifically for fruit trees, aids the farmer in the marketing of their product and assists in managing production logistics such as labor requirement and storage needs. In this study, a machine vision system is developed to estimate fruit yield early in the season. The machine vision system uses a color camera to capture images of fruit trees during the full bloom period. An image segmentation algorithm based on an artificial neural network was developed to recognize and count the blossoms on the tree. The artificial neural network segmentation algorithm uses color information and position as input. The resulting correlation between the blossom count and the actual number of fruits on the tree shows the potential of this method to be used for early prediction of fruit yield.

Plant Identification Based on Artificial Intelligence

2011 ◽  
Vol 255-260 ◽  
pp. 2286-2290 ◽  
Author(s):  
Yang Ying Gan ◽  
Chun Sheng Hou ◽  
Ting Zhou ◽  
Shu Fa Xu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Expert System ◽  
Machine Vision ◽  
Species Identification ◽  
Plant Identification ◽  
Artificial Intelligent ◽  
Botanical Research ◽  
Identification Tool

Species identification plays an important role in botanical research, but traditional identification tool, which mainly depends on reference books or identification keys, is often recognized as a difficult and frustrating task, especially for novices. In recent decades, many efforts have been made by taxonomists and programmers to ease the difficulty of species identification by developing a range of tools that increasingly involved the use of computers. In this paper, new advances of plant identification based on three main artificial intelligent technologies: expert system, artificial neural network, and machine vision are briefly introduced. Several trends of plant identification tools for non-expert users are also proposed in the last part.

Applying Optoelectronic Devices Fusion in Machine Vision

2020 ◽  
pp. 184-213
Author(s):  
Wendy Flores-Fuentes ◽  
Moises Rivas-Lopez ◽  
Daniel Hernandez-Balbuena ◽  
Oleg Sergiyenko ◽  
Julio C. Rodríguez-Quiñonez ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Vision ◽  
Robot Navigation ◽  
Vision System ◽  
Optoelectronic Devices ◽  
Structural Monitoring ◽  
Machine Vision System ◽  
Wide Range ◽  
Spatial Coordinates ◽  
Electronic Hardware

Machine vision is supported and enhanced by optoelectronic devices, the output from a machine vision system is information about the content of the optoelectronic signal, it is the process whereby a machine, usually a digital computer and/or electronic hardware automatically processes an optoelectronic signal and reports what it means. Machine vision methods to provide spatial coordinates measurement has developed in a wide range of technologies for multiples fields of applications such as robot navigation, medical scanning, and structural monitoring. Each technology with specified properties that could be categorized as advantage and disadvantage according its utility to the application purpose. This chapter presents the application of optoelectronic devices fusion as the base for those systems with non-lineal behavior supported by artificial intelligence techniques, which require the use of information from various sensors for pattern recognition to produce an enhanced output.

Identification of Peanut Pods with Three or More Kernels by Machine Vision and Neural Network

2014 ◽  
Vol 10 (1) ◽  
pp. 97-102
Author(s):  
Jason Wang ◽  
Wade W. Yang ◽  
Lloyd T. Walker ◽  
Taha Rababah
Keyword(s):  
Neural Network ◽  
Machine Vision ◽  
Vision System ◽  
Major Axis ◽  
Machine Vision System ◽  
Discrimination Accuracy ◽  
Projected Area ◽  
Niche Marketing ◽  
Axis Length ◽  
Physical Features

Abstract Separation of unshelled peanuts containing three or more kernels and then niche marketing them can potentially increase the value of unshelled peanuts and thus the profit of peanut producers or processors. Effective identification of peanut pods with three or more kernels is a critical step prior to separation. In this study, a machine vision system was teamed up with neural network technique to discriminate unshelled peanuts into two groups: one with three or more kernels and the other with two or less kernels. A set of physical features including the number of bumps, projected area, length and perimeter, etc., were extracted from the images taken and used to train an artificial neural network for discriminating the peanuts. It was found that among all the selected features, the length, the major axis length and perimeter have the best correlation with the number of kernels (correlation coefficient r = 0.87–0.88); the area and convex area have good correlation (r = 0.85); the eccentricity, number of bumps, and the compactness have relatively lower correction (r = 0.77–0.80); the solidity and the minor axis length have the least correlation to the number of kernels (r = −0.415–0.26). The best discrimination accuracy obtained for peanut pods with three or more kernels was 92.5% for the conditions used in this study.

Applying Optoelectronic Devices Fusion in Machine Vision

2017 ◽  
pp. 1-37
Author(s):  
Wendy Flores-Fuentes ◽  
Moises Rivas-Lopez ◽  
Daniel Hernandez-Balbuena ◽  
Oleg Sergiyenko ◽  
Julio C. Rodríguez-Quiñonez ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Vision ◽  
Robot Navigation ◽  
Vision System ◽  
Optoelectronic Devices ◽  
Structural Monitoring ◽  
Machine Vision System ◽  
Wide Range ◽  
Spatial Coordinates ◽  
Electronic Hardware

Machine vision is supported and enhanced by optoelectronic devices, the output from a machine vision system is information about the content of the optoelectronic signal, it is the process whereby a machine, usually a digital computer and/or electronic hardware automatically processes an optoelectronic signal and reports what it means. Machine vision methods to provide spatial coordinates measurement has developed in a wide range of technologies for multiples fields of applications such as robot navigation, medical scanning, and structural monitoring. Each technology with specified properties that could be categorized as advantage and disadvantage according its utility to the application purpose. This chapter presents the application of optoelectronic devices fusion as the base for those systems with non-lineal behavior supported by artificial intelligence techniques, which require the use of information from various sensors for pattern recognition to produce an enhanced output.

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