Modelling and Prediction of Stainless Steel Clad Bead Geometry Deposited by GMAW Using Regression and Artificial Neural Network Models

Sreeraj, P.; Kannan, T.

doi:10.1155/2012/237379

Cited by 29 publications

(9 citation statements)

References 18 publications

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“…Exclusive features like higher strength to weight ratio, corrosion resistance, remarkable fatigue and endurance limit are offered by their use [1]. When a stainless steel is cladded to a low carbon steel the resultant properties include high strength and high corrosion resistance [2]. The growing use of clad composites has attracted the focus of researchers in these days.…”

Section: Introductionmentioning

confidence: 99%

Abrasive Waterjet Cutting of Clad Composite for Achieving Minimal Cut Quality Difference Between Constituent Layers

Ishfaq

Ahmad

Mufti

et al. 2019

Metals

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Clad composites have emerged as a suitable choice to augment the industrial needs due to having a combination of different properties. The accurate cutting is challenging due to the heterogeneous nature of the composite. Conventionally, thermal cutting (plasma/gas) techniques are commonly employed which provide poor cut quality, deeper heat affected zones and demand additional finishing operations. Therefore, this research evaluates the potential of abrasive water jet cutting (AWJC) as a proficient substitute for the cutting of stainless-clad-steel composite in terms of surface quality. However, it is difficult to produce a similar level of surface finish at both the layers because the constituent layers have different mechanical properties. The effect and significance of four important AWJC parameters on cut quality are examined through statistical analyses. Optical and scanning electron microscopic analyses are further provided as evidence of the reported results. Optimal settings are also developed using a weighted signal-to-noise ratio technique which can provide minimal roughness at each layer. Moreover, using the optimal settings, a similar level of surface finish has been achieved for both the layers with a difference of just 0.03 µm between the constituent layers.

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Section: Introductionmentioning

confidence: 99%

Abrasive Waterjet Cutting of Clad Composite for Achieving Minimal Cut Quality Difference Between Constituent Layers

Ishfaq

Ahmad

Mufti

et al. 2019

Metals

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show abstract

“…The acceptable clad bead geometry depends on arc voltage, welding current, gas flow rate, wire feed rate, welding speed, torch angle, tip-to-nozzle distance, etc. 7) . The relationships between input process parameters and bead parameters were investigated with the help of various mathematical models 8,9) .…”

Section: Introductionmentioning

confidence: 99%

Anticorrosion Performance of FCAW Cladding with Regard to the Influence of Heat Input

Saha

Mondal

Hazra

et al. 2018

Journal of Welding and Joining

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Heat input plays a significant role in flux cored arc welding (FCAW) for producing quality welding. Weld cladding improves corrosion resistance and other mechanical properties. Selection of appropriate heat input is required to get optimum quality of the cladding. In this experiment, FCAW cladding of duplex stainless steel is done on low alloy steel under CO 2 gas shield. Three sets of heat input are chosen for experimentation in which each level of heat input comprised of three sets of current and voltage in such a way that the heat input level remained nearly constant. The output parameters like corrosion rate is measured and microstructures are evaluated. The relationship between heat input and corrosion rate is evaluated by means of polynomial regression analysis and the results matched with proposed model with small variations. The results showed that corrosion rate increases with increase in heat input within the experimental domain. Minimum corrosion rate is obtained in the present investigation at 0.49 kJ/mm heat input with 170A weld current, 27V weld voltage and 7.5 mm/s weld torch travel speed. For a particular heat input, corrosion rate is found to vary with different weld current. Microstructure of duplex stainless steel cladding shows presence of austenite phase, and ferrite phase. For an increase in heat input, the microstructure of clad part sacrifices more amount of ferrite phase that exhibits more amount of corrosion.

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“…His method can be used to predict the penetration, leg length, and e ective throat thickness for a given set of weld parameters and alternating shielding gas frequencies. Hamidinejad et al [10] used the back propagation ANN model for the resistance spot welding of galvanized interstitial free steel sheets, and Sreeraj and Kannan [16] estimated various input process parameters, such as welding current, welding speed, gun angle, contact tip-towork distance, and pinch, to get optimum dilution in stainless steel cladding of low carbon structural steel plates using gas metal arc welding.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Mechanical Properties of Welded S355j2+n Steel via the Artificial Neural Network

2016

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A new estimation study on material features for welding processes is reported. The method is based on the Arti cial Neural Network (ANN) for estimation of material features after the gas-metal arc welding process. Since welding is a very common process in many engineering areas, this method would certainly assist technicians and engineers in estimating material features related to the welding parameters before any welding operation. In the proposed method, the input parameters of welding are de ned as various shielding gas mixtures of Ar, O2 and CO2. As the resulting feature, an estimation is made on the mechanical properties, such as tensile strength, impact test, elongation and weld metal hardness, following ANN. The controller is trained with the scaled conjugate gradient method. It is proven that some estimated values are consistent with the experimental data, whereas some others have relatively higher errors. Thus, this method can be used to estimate, especially, the yield strength and elongation values when the shielding gas proportions are ascertained before the welding. Thus, the method helps to ascertain the welding gas selection in a very short time for engineers, and assists in decreasing welding costs.

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Modelling and Prediction of Stainless Steel Clad Bead Geometry Deposited by GMAW Using Regression and Artificial Neural Network Models

Cited by 29 publications

References 18 publications

Abrasive Waterjet Cutting of Clad Composite for Achieving Minimal Cut Quality Difference Between Constituent Layers

Abrasive Waterjet Cutting of Clad Composite for Achieving Minimal Cut Quality Difference Between Constituent Layers

Anticorrosion Performance of FCAW Cladding with Regard to the Influence of Heat Input

Estimation of Mechanical Properties of Welded S355j2+n Steel via the Artificial Neural Network

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