Multi-variable measurements and optimization of GMAW parameters for API-X42 steel alloy using a hybrid BPNN–PSO approach

Moghaddam, Masoud Azadi; Golmezergi, R.; Kolahan, Farhad

doi:10.1016/j.measurement.2016.05.049

Cited by 46 publications

(21 citation statements)

References 23 publications

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“…There is a plenty of different heuristic algorithms (including Tabu search, GA, SA, PSO, Ant and Bee colony, and etc.) among which SA and Tabu search algorithms based on their merits are employed extensively for optimization of different problems and processes [13][14][15][16][17][18]. Easy to program and converge fast are the significant advantages of using TS algorithm.…”

Section: Designing the Solution Methodsmentioning

confidence: 99%

“…In Eq. (14), the difference between the current and new answers of the target function has been shown by ΔC . The index k is the number of repetitions (iteration) and the control parameter c k called temperature.…”

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

“…The unique feature of TS is escaping the local optimizations and the legitimate nature of this search engine can be considered as one of the most important benefits. In problems (14) P r (Solution_current k+1 = Solution_neighbour k ) = exp − ΔC c k of optimization, especially in small ones and the final stages of searching for big ones, these two features play a major role in improving the quality of the final answer. Nevertheless, in big problems, maintaining the definite nature of an algorithm that results from the evaluation of all neighborhoods of the current answer, will slow down the progression and extend the computing time.…”

Section: Tabu-sa Integrated Algorithmmentioning

confidence: 99%

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Multi-criteria optimization of multi product assembly line using hybrid Tabu-SA algorithm

Tavakoli

2020

SN Appl. Sci.

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In this paper, a multi-criteria optimization problem has been addressed including minimizing three important objectives: (a) total amount of fines for untimely production of orders; (b) total carrier rails' time including the preparation times of the stations; and (c) total cost of feeding outside the work station plan. In order to consider the relative importance of the stated goals, all costs/fines have been weighed. The proposed problem is one the nondeterministic polynomial complex problems, for which no comprehensive solution has been presented. Hence, to find the optimal answer in a reasonable time, Tabu search (TS) and simulated annealing (SA) algorithms have been used. Furthermore, to obtain the merits of the stated algorithms, a combination of both has been employed (Tabu-SA algorithm). To illustrate the performance of the proposed method, two numerical examples have been solved. The computational results indicate the high speed of integrated search algorithm and the superiority of the quality of its solutions. Based on the results, the proposed hybrid Tabu-SA algorithm has a remarkable advantage in comparison with TS and SA algorithms in terms of convergence rate. The total cost has been reduced at 24% using the proposed hybrid Tabu-SA algorithm. Besides, the computing time was improved at 29% and 46% using the proposed procedure respectively.Keywords Multi-criteria optimization · Multi modal assembly lines (MMAL) · Tabu search (TS) algorithm · Simulated annealing (SA) algorithm * Ahmad Tavakoli, tavakoli-a@um.ac.ir |

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Section: Designing the Solution Methodsmentioning

confidence: 99%

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

Section: Tabu-sa Integrated Algorithmmentioning

confidence: 99%

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Multi-criteria optimization of multi product assembly line using hybrid Tabu-SA algorithm

Tavakoli

2020

SN Appl. Sci.

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“…Previous studies have reported the development of ANN systems capable of predicting the weld quality outcome [1,10,19,[26][27][28][29][30][31][32][33]. In these studies, ANNbased prediction has been studied considering bead width, bead height, and achieved depth of penetration of the weld.…”

Section: Introductionmentioning

confidence: 99%

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

Penttilä

Kah

Ratava

et al. 2019

Int J Adv Manuf Technol

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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 intelligencebased 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.

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“…Yang et al [15] showed that BPNNs have a good performance in solving non-linear problems by comparing with other methods in terms of absolute distance as a similarity measure. Moghaddam et al [16] showed that BPNNs were good at solving non-linear and multi-input parameter problems. The methods of BPNNs were also introduced in the field of wind turbines.…”

mentioning

confidence: 99%

GA-BP Neural Network-Based Strain Prediction in Full-Scale Static Testing of Wind Turbine Blades

Liu

Wang³

et al. 2019

Energies

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This paper proposes a strain prediction method for wind turbine blades using genetic algorithm back propagation neural networks (GA-BPNNs) with applied loads, loading positions, and displacement as inputs, and the study can be used to provide more data for the wind turbine blades’ health assessment and life prediction. Among all parameters to be tested in full-scale static testing of wind turbine blades, strain is very important. The correlation between the blade strain and the applied loads, loading position, displacement, etc., is non-linear, and the number of input variables is too much, thus the calculation and prediction of the blade strain are very complex and difficult. Moreover, the number of measuring points on the blade is limited, so the full-scale blade static test cannot usually provide enough data and information for the improvement of the blade design. As a result of these concerns, this paper studies strain prediction methods for full-scale blade static testing by introducing GA-BPNN. The accuracy and usability of the GA-BPNN prediction model was verified by the comparison with BPNN model and the FEA results. The results show that BPNN can be effectively used to predict the strain of unmeasured points of wind turbine blades.

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Multi-variable measurements and optimization of GMAW parameters for API-X42 steel alloy using a hybrid BPNN–PSO approach

Cited by 46 publications

References 23 publications

Multi-criteria optimization of multi product assembly line using hybrid Tabu-SA algorithm

Multi-criteria optimization of multi product assembly line using hybrid Tabu-SA algorithm

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

GA-BP Neural Network-Based Strain Prediction in Full-Scale Static Testing of Wind Turbine Blades

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