Optimization of Inconel 718 alloy welds in an activated GTA welding via Taguchi method, gray relational analysis, and a neural network

Lin, Hsuan-Liang

doi:10.1007/s00170-012-4538-9

Cited by 20 publications

(8 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Taguchi method (TM) was used to obtain the optimal solution within the specified control factor level and determine the magnitude of neural network input. The results show that the design of neural network training samples in the feasible solution region constrained by the Taguchi method (TM) can greatly improve the learning efficiency and accuracy of neural network [19].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Tensile Strength and Deformation of Diffusion Bonding Joint for Inconel 718 Using Deep Neural Network

Mei

Lang

et al. 2020

Metals

View full text Add to dashboard Cite

Due to the acceptable high-temperature deformation resistance of Inconel 718, its welding parameters such as bonding temperature and pressure are inevitably higher than those of general metals. As a result of the existing punitive processing environment, it is essential to control the deformation of parts while ensuring the bonding performance. In this research, diffusion bonding experiments based on the Taguchi method (TM) are conducted, and the uniaxial tensile strength and deformation ratio of the experimental joints are measured. According to experimental data, a deep neural network (DNN) was trained to characterize the nonlinear relationship between the diffusion bonding process parameters and the diffusion bonding strength and deformation ratio, where the overall correlation coefficient came out to be 0.99913. The double-factors analysis of bonding temperature–bonding pressure based on the prediction results of the DNN shows that the temperature increment of the diffusion bonding of Inconel 718 significantly increases the deformation ratio of the diffusion bonding joints. Therefore, during the multi-objective optimization of the bonding performance and deformation of components, priority should be given to optimizing the bonding pressure and duration only.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of Tensile Strength and Deformation of Diffusion Bonding Joint for Inconel 718 Using Deep Neural Network

Mei

Lang

et al. 2020

Metals

View full text Add to dashboard Cite

show abstract

“…Haragopal et al 11 applied this method to the design parameter of the Al-65032 alloy in the GMAW process. Some researchers [12][13][14][15][16][17] have used the TM and GRA for their selection of optimum levels of process parameters in welding. All of them used the same method to solve for optimum values; their different welding processes, workpiece materials, and quality characteristics are shown in Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…Acherjee et al 16 employed the GRA-based TM for analysis of the parametric optimization for the laser welding process. Lin 17 used the TM with GRA and a neural network for the optimization of Inconel 718 alloy welds in activated gas tungsten arc welding (GTAW).…”

Section: Introductionmentioning

confidence: 99%

“…In this work, the percentage of the zinc coating balances edge joints of the galvanized steel sheets using the 11 GMAW Al-65032 aluminum Taguchi Weld dilution Tarng et al 12 SAW Mild steel Grey-Taguchi Deposition rate, dilution Datta et al 13 SAW Mild steel Grey-Taguchi Weld bead geometry Pal et al 14 PMIGW Carbon steel Grey-Taguchi Weld bead geometry Sathiya and Abdul Jaleel 15 GMAW 904 L stainless steel Grey-Taguchi Weld bead geometry Acherjee et al 16 LW Thermoplastics Grey-Taguchi Weld strength, weld width Lin 17 GTAW Inconel 718 alloy Grey-Taguchi Weld bead, penetration Our approach MIGPBP Galvanized steel sheets Grey-Taguchi Percent zinc coating balances edge joints (PZBEJ) GMAW: gas metal arc welding; SAW: submerge arc welding; PMIGW: pulse metal inert gas welding; GTAW: gas tungsten arc welding; LW: laser welding; MIGPB: metal inert gas pulse brazing; MIGPBP: metal inert gas pulse brazing process; PZBEJ: percent zinc coating balances edge joints.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Grey–Taguchi method to optimize the percent zinc coating balances edge joints for galvanized steel sheets using metal inert gas pulse brazing process

Songsorn

Sriprateep

Rittidech

2016

Advances in Mechanical Engineering

View full text Add to dashboard Cite

The objective of this work was to optimize the percent zinc coating balances edge joints of galvanized steel sheets using the metal inert gas pulse brazing process. The Taguchi method and grey relational analysis were used to determine the relationship between the metal inert gas pulse brazing process parameters and percent zinc coating balances edge joints. The metal inert gas pulse brazing process parameters used in this study included wire feed speeds, arc voltages, travel speed, peak currents, and pulse frequency. The characteristics of metal inert gas pulse brazing process that were considered to find response were percent zinc coating balances edge joints on the upper edge joint (PZBEJ1), the lower edge joint (PZBEJ2), and the back sides of the edge joint (PZBEJ3). Analysis of variance was performed to determine the impact of an individual process parameter on the quality parameters. The results showed that the optimal parameters in which grey relational grade increases at the highest level were wire feed speeds at 3.25 m/min, arc voltages at 16 V, travel speeds at 0.9 m/min, peak currents at 425 A, and pulse frequency at 35 Hz. These parameters gave a 74.90% higher response value than those of the initial parameters of metal inert gas pulse brazing process.

show abstract

“…The research was mostly experimental and only very few attempts were made to develop the mathematical models to depict the relationships between the input and output parameters that can be used for prediction as well as for determining the optimum values of the output parameters. Few researchers [29]- [31] had applied neural networks also for prediction purpose. Furthermore, it is observed that only a few researchers had experimented on plasma spraying of Al 2 O 3 -TiO 2 coatings on stainless steel substrates [32] [33].…”

Section: Introductionmentioning

confidence: 99%

Parameter Optimization of Amalgamated Al2O3-40% TiO2 Atmospheric Plasma Spray Coating on SS304 Substrate Using TLBO Algorithm

Rajesh¹,

Rao²

2016

JSEMAT

View full text Add to dashboard Cite

SS304 is a commercial grade stainless steel which is used for various engineering applications like shafts, guides, jigs, fixtures, etc. Ceramic coating of the wear areas of such parts is a regular practice which significantly enhances the Mean Time Between Failure (MTBF). The final coating quality depends mainly on the coating thickness, surface roughness and hardness which ultimately decides the life. This paper presents an experimental study to effectively optimize the Atmospheric Plasma Spray (APS) process input parameters of Al2O3-40% TiO2 ceramic coatings to get the best quality of coating on commercial SS304 substrate. The experiments are conducted with a threelevel L18 Orthogonal Array (OA) Design of Experiments (DoE). Critical input parameters considered are: spray nozzle distance, substrate rotating speed, current of the arc, carrier gas flow and coating powder flow rate. The surface roughness, coating thickness and hardness are considered as the output parameters. Mathematical models are generated using regression analysis for individual output parameters. The Analytic Hierarchy Process (AHP) method is applied to generate weights for the individual objective functions and a combined objective function is generated. An advanced optimization method, Teaching-Learning-Based Optimization algorithm (TLBO), is applied to the combined objective function to optimize the values of input parameters to get the best output parameters and confirmation tests are conducted based on that. The significant effects of spray parameters on surface roughness, coating thickness and coating hardness are studied in detail.

show abstract

Optimization of Inconel 718 alloy welds in an activated GTA welding via Taguchi method, gray relational analysis, and a neural network

Cited by 20 publications

References 25 publications

Prediction of Tensile Strength and Deformation of Diffusion Bonding Joint for Inconel 718 Using Deep Neural Network

Prediction of Tensile Strength and Deformation of Diffusion Bonding Joint for Inconel 718 Using Deep Neural Network

Grey–Taguchi method to optimize the percent zinc coating balances edge joints for galvanized steel sheets using metal inert gas pulse brazing process

Parameter Optimization of Amalgamated Al<sub>2</sub>O<sub>3</sub>-40% TiO<sub>2</sub> Atmospheric Plasma Spray Coating on SS304 Substrate Using TLBO Algorithm

Contact Info

Product

Resources

About

Optimization of Inconel 718 alloy welds in an activated GTA welding via Taguchi method, gray relational analysis, and a neural network

Cited by 20 publications

References 25 publications

Prediction of Tensile Strength and Deformation of Diffusion Bonding Joint for Inconel 718 Using Deep Neural Network

Prediction of Tensile Strength and Deformation of Diffusion Bonding Joint for Inconel 718 Using Deep Neural Network

Grey–Taguchi method to optimize the percent zinc coating balances edge joints for galvanized steel sheets using metal inert gas pulse brazing process

Parameter Optimization of Amalgamated Al&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt;-40% TiO&lt;sub&gt;2&lt;/sub&gt; Atmospheric Plasma Spray Coating on SS304 Substrate Using TLBO Algorithm

Contact Info

Product

Resources

About

Parameter Optimization of Amalgamated Al<sub>2</sub>O<sub>3</sub>-40% TiO<sub>2</sub> Atmospheric Plasma Spray Coating on SS304 Substrate Using TLBO Algorithm