Optimization of Process Parameters in Laser Welding of Hastelloy C-276 Using Artificial Neural Network and Genetic Algorithm

In this paper, an effort is made to determine the optimized parameters in laser welding of Hastelloy C-276 using Artificial Neural Network (ANN) and Genetic Algorithm (GA). CO2 Laser welding was performed on a sheet of thickness 1.6[Formula: see text]mm based on Taguchi L27 orthogonal array. Laser power, welding speed and shielding gas flow rate were chosen as input parameters and Bead width, depth of Penetration and Microhardness were measured for assessing the weld quality. ANN was applied for modeling the welding process parameters i.e. heat input, welding speed and gas flow rate. Various learning algorithms such as Batch Back Propagation (BBP), Incremental Back Propagation (IBP), Quick Propagation (QP) and Levenberg–Marquardt (LM) were comprehensively tested for estimating the output parameters and a comparison was also made among them, with respect to prediction accuracy. BBP method was found to be the best learning algorithm. Experimental validation test was performed based on the ANN and GA predicted optimized responses and this welding input parameters provided satisfactory weld metal characteristics in terms of penetration depth, bead width and microhardness.

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Development of a Gas Flow Rate Model for Multi-Stage Choke System in HPHT Gas Wells Using Artificial Intelligence

10.2118/205163-ms ◽

2021 ◽

Author(s):

Ahmed Abdullah alghamdi ◽

Nawaf Saud Almutairi ◽

Ali Muslim ◽

Humoud Khaldi ◽

Abdulazeez Abdulraheem

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Artificial Neural Network ◽

Production Rate ◽

Flow Rate ◽

Gas Flow ◽

Rate Measurement ◽

Gas Flow Rate ◽

Gas Wells ◽

Flow Meter

Abstract Objective/Scope Accurate well production rate measurement is critical for reservoir management. The production rate measurement is carried out using surface devices, such as orifice flow meter and venturi flow meter. For large offshore fields development with a high number of wells, the installation and maintenance costs of these flowmeters can be significant. Therefore, an alternative solution needs to be developed. This paper described the successful implementation of Artificial Intelligence in predicting the production rate of big-bore gas wells in an offshore field. Methods, Procedures, Process Successful application of AI depends on capitalizing on a large set of data. Therefore, flowing parameters data were collected for more than 30 gas wells and totaling over 100,000 data points. These wells are producing gas with slight solid production from a high-pressure high-temperature field. In addition, these wells are equipped with a multistage choke that reduces the noise and vibration levels. An Artificial Neural Network is trained on the data using Gradient Descent method as the optimization algorithm. The network takes as an input the upstream and downstream pressure and temperature, and the choke size. The output is the gas rate measured in MMscf/day. Results, Observations, Conclusions The data set was divided into 70% for training the neural network and 30% for validation. Artificial Neural Network (ANN) was used and the developed model compared exceptionally well with the gas rates measured from the calibrated venturi meters. The gas rate estimation was within a 5% error. The model was developed for two types of completions: 7" and 9-5/8" production tubing. One of the challenges was how to estimate the choke wear which plays a major role in the quality of the choke size data. A linear choke wear deterioration is applied in this case, while work in progress is taking place for acquiring acoustic data that can significantly improve the choke wear modeling. Novel/Additive Information The novel approach presented in this paper capitalizes on Al analytics for estimating accurate gas flow rate values. This approach has improved the reservoir data management by providing accurate production rate values which has drastically improved the reservoir simulation. Moreover, the robustness of the AI model has forced us to rethink the conventional design of installing a flow meter for every well. As shown in this paper, the AI model served as an alternative to conventional venturi meters. We believe that the application of AI models to other aspects of production surveillance will lead to a shift into how operators design production facilities.

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Tri-objective constrained optimization of pulsating DC sourced magnetic abrasive finishing process parameters using artificial neural network and genetic algorithm

Materials and Manufacturing Processes ◽

10.1080/10426914.2020.1866196 ◽

2021 ◽

pp. 1-15

Author(s):

Shadab Ahmad ◽

Ranganath M. Singari ◽

R.S. Mishra

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Constrained Optimization ◽

Process Parameters ◽

Magnetic Abrasive Finishing ◽

Finishing Process ◽

Abrasive Finishing ◽

Artificial Neural

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The Effect of Process Parameters on the Microstructure and Mechanical Properties of AW5083 Aluminum Laser Weld Joints

Metals ◽

10.3390/met10111443 ◽

2020 ◽

Vol 10 (11) ◽

pp. 1443 ◽

Cited By ~ 1

Author(s):

Maroš Vyskoč ◽

Miroslav Sahul ◽

Mária Dománková ◽

Peter Jurči ◽

Martin Sahul ◽

...

Keyword(s):

Mechanical Properties ◽

Tensile Strength ◽

Process Parameters ◽

Flow Rate ◽

Weld Joint ◽

Gas Flow ◽

Filler Wire ◽

Shielding Gas ◽

Gas Flow Rate ◽

Weld Joints

In this article, the effect of process parameters on the microstructure and mechanical properties of AW5083 aluminum alloy weld joints welded by a disk laser were studied. Butt welds were produced using 5087 (AlMg4.5MnZr) filler wire, with a diameter of 1.2 mm, and were protected from the ambient atmosphere by a mixture of argon and 30 vol.% of helium (Aluline He30). The widest weld joint (4.69 mm) and the highest tensile strength (309 MPa) were observed when a 30 L/min shielding gas flow rate was used. Conversely, the narrowest weld joint (4.15 mm) and the lowest tensile strength (160 MPa) were found when no shielding gas was used. The lowest average microhardness (55.4 HV0.1) was recorded when a 30 L/min shielding gas flow rate was used. The highest average microhardness (63.9 HV0.1) was observed when no shielding gas was used. In addition to the intermetallic compounds, β-Al3Mg2 and γ-Al12Mg17, in the inter-dendritic areas of the fusion zone (FZ), Al49Mg32, which has an irregular shape, was recorded. The application of the filler wire, which contains zirconium, resulted in grain refinement in the fusion zone. The protected weld joint was characterized by a ductile fracture in the base material (BM). A brittle fracture of the unshielded weld joint was caused by the presence of Al2O3 particles. The research results show that we achieved the optimal welding parameters, because no cracks and pores were present in the shielded weld metal (WM).

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A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters

Materials & Design (1980-2015) ◽

10.1016/j.matdes.2011.01.058 ◽

2011 ◽

Vol 32 (6) ◽

pp. 3457-3464 ◽

Cited By ~ 76

Author(s):

Fei Yin ◽

Huajie Mao ◽

Lin Hua

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Injection Molding ◽

Process Parameters ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Injection Molding Process ◽

Molding Process

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Modeling of Weld Lap-Shear Strength for Laser Transmission Welding of Thermoplastic Using Artificial Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.445.454 ◽

2012 ◽

Vol 445 ◽

pp. 454-459 ◽

Cited By ~ 5

Author(s):

M.R. Nakhaei ◽

N.B. Mostafa Arab ◽

F. Kordestani

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Shear Strength ◽

Laser Welding ◽

Process Parameters ◽

Welding Speed ◽

Laser Power ◽

Laser Transmission Welding ◽

Lap Shear Strength ◽

Lap Shear

Laser welding of plastic materials has a wide range of applications in the packaging, medical, electronics and automobile industries provided it can predict high quality welds compared with other joining methods. Laser welding process parameters can affect the quality of welds. In this paper, Artificial Neural Network (ANN) is used to model the effects of laser power, welding speed, clamp pressure and stand-off distance on weld lap-shear strength in laser transmission welding (LTW) of acrylic (polymathy methacrylate). A set of experimental data on diode laser weld lap-shear strengths was used to train and test the ANN from which the neurons relations were gradually extracted to develop a model. The developed ANN model can be used for the analysis and prediction of the complex relationships between the above mentioned process parameters and weld lap-shear strength. The results indicated that increase in laser power and clamp pressure increases the weld lap-shear strength whereas welding speed and stand off distance had a decreasing affect on shear strength at high value.

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Application of Taguchi technique to optimize the gma welding parameters and study of fracture mode characterization of AISI 304H welded joints

International Review of Applied Sciences and Engineering ◽

10.1556/1848.2018.9.1.2 ◽

2018 ◽

Vol 9 (1) ◽

pp. 9-16

Author(s):

S. A. Rizvi

Keyword(s):

Tensile Strength ◽

Ultimate Tensile Strength ◽

Process Parameters ◽

Flow Rate ◽

Fracture Mode ◽

Welded Joints ◽

Gas Flow ◽

Welding Parameters ◽

Taguchi Technique ◽

Gas Flow Rate

This research article is focusing on the optimization of different welding process parameters which affect the weldability of stainless steel (AISI) 304H, Taguchi technique was used to optimize the welding parameters and the fracture mode characterization was studied. A number of experiments have been conducted. L9 orthogonal array (OA) (3×3) was applied. Analysis of variance ( ANOVA) and signal to noise ratio (SNR) was applied to determine the effect of different welding parameters such as welding current, wire feed speed and gas flow rate on mechanical, microstructure properties of SS304H. Ultimate tensile strength (UTS), toughness, microhardness (VHN), and mode of fracture was examined to determine weldability of AISI 304H and it was observed from results that welding voltage has major impact whereas gas flow rate has minor impact on ultimate tensile strength of the welded joints. Optimum process parameters were found to be 23 V, 350 IPM travel speed of wire and 15 l/min gas flow rate for tensile strength and mode of fracture was ductile fracture for tensile test specimen.

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