Optimizing SAW Parameters to Reduce Distortion in Welded Steel Plates

Experimental Setup

The experiments were conducted on mild steel plates of size 150 x 100 x 12 mm plate as par the design matrix (Table 3.2). A 45o V- grove was made on each plate so as to make butt joint. Each one of the cleaned plate was welded employing an electrode positive polarity. Weld beads were deposited in the V- groove using 3.2 mm diameter mild steel wire. A constant potential transformer rectifier type power source with a current capacity of 600A at 60% duty cycle and an open circuit voltage of 12-48 volts was used.

The plates were cleaned chemically and mechanically to remove oxide layer and any other source of hydrogen, before welding. Weld bead were deposited using a mechanized Submerged Arc Welding (SAW) machine to ensure the reproducibility of the data. This also eliminated the effects of welder’s skill on the result.

Model Development and Optimization

The complete sets of eight trials were repeated twice to determine the ‘variance of optimization parameters’ and ‘variance of adequacy’ for the model.

Get quality help now

Dr. Karlyna PhD

Verified writer

Proficient in: Science

4.7 (235)

“ Amazing writer! I am really satisfied with her work. An excellent price as well. ”

+84 relevant experts are online

Hire writer

The experiments were performed in a random order to avoid any systematic error. The wok limits of the parameters are selected as: The regression coefficients of the selected model are calculated using equation given below.

Regression Model for Distortion

The regression model for predicting distortion (D) in the welded plates was developed using the least squares method, resulting in the following equation:

D= 7.97 + 0.267F+ 0.022V+ 0.209R – 0.260S+ 0.16(FV+RS) + 0.44(FR+VS) + 0.095 (FS+VR)

where F, V, R, and S represent the welding parameters, and their interactions are considered to understand their combined effects on distortion.

Calculation of the variance of adequacy:

= 2(0.026+0.031+0.086+0.011+0.028+0.047+0.004+0.119) /3

= (2 x 0.352) / 3

= 0.234

Calculation of variance of reproducibility:

=2(0.029+0.063+0.046+0.065+0.0007+0.014+0.012+0.029)/ 8

= 2(0.258) /8

= 0.064

Calculation of F- ratio:

= 0.234/0.064.

= 3.65

Insignificant coefficients found using Student’s t-test can be dropped along with response with which they are associated, without affecting much of the accuracy of the model. As per this test, a coefficient is significant if its absolute value is greater than the confidence interval for a desired level of confidence (say 95%)

‘t’ from table at (8,0.05) = 2.306

Statistical Analysis

Variance and Adequacy Testing

The model's adequacy was assessed through an F-test, comparing the variance of optimization parameters and the variance of adequacy. The calculations revealed:

• Variance of Adequacy: 0.2340.234
• Variance of Reproducibility: 0.0640.064
• F-ratio: 3.653.65, compared to an F-table value of 4.074.07

This indicated the model's statistical significance and adequacy for predicting distortion.

Significance Testing

The significance of the model coefficients was tested using Student's t-test, identifying several coefficients as insignificant. This led to the refinement of the model by removing the parameters associated with these coefficients, resulting in a simplified and more accurate final model:

D=7.97+0.267F+0.209R−0.260S+0.440(FR+VS)

Model Validation

The validity of the refined model was confirmed through a scatter diagram comparing estimated values against observed values, with a correlation coefficient (r) of 0.900.90. This high level of correlation underscores the model's effectiveness in predicting angular distortion in SAW processes.

Conclusion

This study highlights the importance of precise control and optimization of welding parameters to minimize distortion in welded structures. By applying a systematic experimental design and sophisticated statistical analysis, the research successfully develops a reliable model for predicting and thereby reducing distortion in SAW applications. The findings not only contribute to the advancement of welding technology but also provide a practical guide for improving the quality and performance of welded joints in the manufacturing industry.