Assessment of weld bead geometry in modified shortcircuiting gas metal arc welding process for low alloy steel

Bandhu, Din; Abhishek, Kumar

doi:10.1080/10426914.2021.1906897

Cited by 42 publications

(5 citation statements)

References 86 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The values of R-sq and R-sq(adj) are very close to each other, which indicates the less variability of the predicted responses with respect to the input parameters. The P values in the ANOVA table for linear, square, and interaction of input parameters are found to be less than 0.05 which indicates that the developed regression model is significant [ 31 ]. W 1 (µm) = −16422 + 28.75 P − 15.30 WS − 0.00807 P 2 + 0.01387 WS 2 − 0.00174 P·WS W 2 (µm) = −34885 + 54.1 P − 23.26 WS − 0.01718 P 2 + 0.00709 WS 2 + 0.00676 P·WS H 1 (µm) = 680 − 1.45 P + 2.55 WS + 0.000893 P 2 + 0.000805 WS 2 − 0.002470 P·WS H 2 (µm) = −506 − 1.54 P + 7.04 WS + 0.00162 P 2 + 0.00291 WS 2 − 0.006685 P·WS Y (MPa) = −2537 + 4.44 P − 1.864 WS − 0.001580 P 2 + 0.000715 WS 2 + 0.000951 P·WS …”

Section: Resultsmentioning

confidence: 99%

Fiber Laser Welded Cobalt Super Alloy L605: Optimization of Weldability Characteristics

et al. 2022

View full text Add to dashboard Cite

The present study describes the laser welding of Co-based superalloy L605 (52Co-20Cr-10Ni-15W) equivalent to Haynes-25 or Stellite-25. The influence of laser welding process input parameters such as laser beam power and welding speed on mechanical and metallurgical properties of weld joints were investigated. Epitaxial grain growth and dendritic structures were visible in the weld zone. The phase analysis results indicate the formation of hard phases like CrFeNi, CoC, FeNi, and CFe in the weld zone. These hard phases are responsible for the increase in microhardness up to 321 HV0.1 in the weld zone, which is very close to the microhardness of the parent material. From the tensile strength tests, the ductile failure of welded specimens was confirmed due to the presence of dimples, inter-granular cleavage, and micro voids in the fracture zone. The maximum tensile residual stress along the weld line is 450 MPa, whereas the maximum compressive residual stress across the weld line is 500 MPa. On successful application of Response Surface methodology (RSM), laser power of 1448.5 W and welding speed of 600 mm/min i.e., line energy or heat input equal to 144 J/mm, were found to be optimum values for getting sound weld joint properties. The EBSD analysis reveals the elongated grain growth in the weld pool and very narrow grain growth in the heat-affected zone.

show abstract

Section: Resultsmentioning

confidence: 99%

Fiber Laser Welded Cobalt Super Alloy L605: Optimization of Weldability Characteristics

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Subsequently, tacking was performed among both workpieces at a spacing of 03 mm ( Figure 3 b). These workpieces were then fastened for executing welding operation by means of the “Continuum 500” welding machine at obtained optimal settings [ 30 , 31 , 32 ]. The particulars of the welding settings are presented in Table 2 .…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, few scholars have optimized the welding operation prior to deploying it in practical uses, yet simply limited endeavors have been undertaken to utilize the RMD technique for low alloy steel. The scholars of this study [ 30 , 31 , 32 ] conducted a number of investigations with a viewpoint of determining the ideal welding conditions for low alloy steel concerning the bead geometry. Current (A) = 100 A, voltage (V) = 13 V, and gas flow rate (GFR) = 21 L/min were the optimal values.…”

Section: Introductionmentioning

confidence: 99%

Effect of Metal-Cored Filler Wire on Surface Morphology and Micro-Hardness of Regulated Metal Deposition Welded ASTM A387-Gr.11-Cl.2 Steel Plates

et al. 2022

Self Cite

View full text Add to dashboard Cite

Environmental and human-friendly welding is the need of the hour. In this context, this study explores the application of the regulated metal deposition (RMD) technique for ASTM A387-Gr.11-Cl.2 steel plates. To examine the effect of metal-cored filler wire (MCFW), MEGAFIL 237 M was employed during regulated metal deposition (RMD) welding of 6 mm thick ASTM A387-Gr.11-Cl.2 steel plates. The welding was carried out at an optimized current (A) of 100 A, voltage (V) of 13 V, and gas flow rate (GFR) of 21 L/min. Thereafter, the as-welded plates were examined for morphological changes using optical microscopy. Additionally, the micro-hardness of the as-welded plates was measured to make corroboration with the obtained surface morphologies. In addition to this, the as-welded plates were subjected to heat treatment followed by surface morphology and micro-hardness examination. A comparison was made between the as-welded and heat-treated plates for their obtained surface morphologies and microhardness values. During this, it was observed that the weld zone of as-welded plates has a dendritic surface morphology which is very common in fusion-based welding. Similarly, the weld zone of heat-treated plates has a finer and erratic arrangement of martensite. Moreover, the obtained surface morphologies in the weld zone of as-welded and heat-treated plates have been justified by their respective hardness values of 1588.6 HV and 227.3 HV.

show abstract

“…The ANFIS employs two techniques; gradient descend and least-squares method in updating parameters (hybrid learning method). In ne tuning the premise parameters, the gradient descend that de ne membership functions is employed, while the least -squares method is applied in identifying other parameters useful in de ning the coe cients of the output equations [32][33]. To begin the ANFIS modeling process, the data set (input-output data pairs) from the experimental study is dividing it into training and testing data sets.…”

Section: Development Of Anfis For the Weld Quality Predictionmentioning

confidence: 99%

Prediction of optimal mild steel weld parameters using the Adaptive Neuro-Fuzzy Inference System (ANFIS) technique

Lofinmakin,

Sada,

Emovon

et al. 2024

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Welding is one of the major operations in many industries as it provides a durable means of joining metals and ensuring that diverse equipments are created to meet the growing needs of the manufacturing industries. To enhance the production of these diverse equipments, studies are continually been performed to identify improved means of obtaining reliable joints. This study applies the Adaptive Neuro Fuzzy Inference System (ANFIS) technique, in improving the predictability of the optimal weld characteristics for a mild steel welded joints, with focus on tensile strength and hardness as responses.From the study, the variation in tensile strength and hardness as a result of the process parameters effects is illustrated, and it reveals the optimal tensile strength and hardness is obtained at the combined input parameters. 170Amp, 20volts, 24l/min, 2.2mm for the tensile strength and 220Amp, 20volts, 20l/min, 2.4mm for the hardness.

show abstract

Assessment of weld bead geometry in modified shortcircuiting gas metal arc welding process for low alloy steel

Cited by 42 publications

References 86 publications

Fiber Laser Welded Cobalt Super Alloy L605: Optimization of Weldability Characteristics

Fiber Laser Welded Cobalt Super Alloy L605: Optimization of Weldability Characteristics

Effect of Metal-Cored Filler Wire on Surface Morphology and Micro-Hardness of Regulated Metal Deposition Welded ASTM A387-Gr.11-Cl.2 Steel Plates

Prediction of optimal mild steel weld parameters using the Adaptive Neuro-Fuzzy Inference System (ANFIS) technique

Contact Info

Product

Resources

About