Modeling and optimization of A-GTAW process using back propagation neural network and heuristic algorithms

2021 ◽  
pp. 104531
Author(s):  
Masoud Azadi Moghaddam ◽  
Farhad Kolahan
Keyword(s):  
Neural Network ◽  
Heuristic Algorithms ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Modeling And Optimization ◽  
Gtaw Process

scholarly journals Optimization of Enhanced TIG Welding Process Using Artificial Neural Network and Heuristic Algorithms

2021 ◽  
Author(s):  
Masoud Azadi Moghaddam ◽  
Farhad Kolahan
Keyword(s):  
Neural Network ◽  
Aspect Ratio ◽  
Heuristic Algorithms ◽  
Welding Process ◽  
Welding Current ◽  
Weld Bead ◽  
Depth Of Penetration ◽  
Bead Width ◽  
Modeling And Optimization ◽  
Gtaw Process

Abstract Using conventional gas tungsten arc welding (C-GTAW) process includes some demerits, shallow penetration has been considered as the most important ones. Recently, in order to cope with the mentioned disadvantage (low penetration), using a paste like coating of activating flux during welding process known as activated GTAW (AGTAW) has been proposed. In this paper, effect of A-GTAW process input adjusting parameters including welding speed (S), welding current (C) and percentage of activating fluxes (TiO2 and SiO2) combination (F) on weld bead width (WBW), depth of penetration (DOP), and consequently aspect ratio (ASR) (the most important quality characteristics) in welding of AISI316L parts have been studied. Box-behnken design (BBD) of experiments has been used to prepare the required experimental matrix for modeling and optimization objectives. Back propagation neural network (BPNN), architecture (hidden layers number and their corresponding neurons/nodes) of which has been determined using heuristic algorithm employed to model the process outputs, the most fitted ones have been optimized using simulated annealing (SA), and particle swarm optimization (PSO) algorithms in order to obtain the desired aspect ratio, maximum depth of penetration, and minimum weld bead width. Finally, confirmation experimental tests have been carried out to evaluate the performance of the proposed method. Due to the obtained results, the suggested method for modeling and optimization of A-GTAW process is quite efficient (with less than 4% error).

scholarly journals Using a back propagation neutral network based modeling and heuristic algorithms based optimization technique in activated gas tungsten arc welding process

2021 ◽  
Author(s):  
Masoud Azadi Moghaddam ◽  
Farhad Kolahan
Keyword(s):  
Arc Welding ◽  
Heuristic Algorithms ◽  
Back Propagation ◽  
Gas Tungsten Arc Welding ◽  
Back Propagation Algorithm ◽  
Modeling And Optimization ◽  
Gas Tungsten Arc ◽  
Gtaw Process ◽  
Gas Tungsten ◽  
Bpnn Model

Abstract In this study, a modeling method based on an artificial neural networks model combined with a back propagation algorithm (BPNN) and an optimization procedure based on heuristic algorithms (particle swarm optimization (PSO) and simulated annealing (SA) algorithms) have been proposed for modeling and optimization of activated gas tungsten arc welding (A-GTAW) process in order to tackle the poor penetration drawback occurs during GTAW process. in this study effect of the most important process adjusting variables including welding current (C), welding speed (S)) and percentage of activating fluxes (TiO2 and SiO2) combination (F) on the most important quality characteristics (weld bead width (WBW), depth of penetration (DOP), and consequently aspect ratio (ASR)) in welding of AISI316L austenite stainless steel parts have been investigated. Box-behnken and central composite designs (BBD and CCD) based on response surface methodology (RSM) in design of experiments (DOE) method have been employed to gather the required data for modeling and optimization purposes. Then, BPNN has been used to determine the relations between A-GTAW process input variables and output responses. To determine the proper BPNN model architecture (the proper hidden layers’ number and their corresponding neurons/nodes in each layer) PSO algorithm has been used. Next, PSO and SA algorithms have been used to optimize the proposed BPNN model in such a way that desired AR, minimum WBW, and maximum DOP achieved. Finally, confirmation experimental tests have been conducted to evaluate the proposed procedure performance. Based on the results, the proposed method is efficient in modeling and optimization (less than 7% error) of A-GTAW process.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

2019 ◽  
Vol 13 ◽  
Author(s):  
Shikha Bhardwaj ◽  
Gitanjali Pandove ◽  
Pawan Kumar Dahiya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Hybrid System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Retrieval Accuracy ◽  
Color Descriptor ◽  
Benchmark Datasets ◽  
Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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