2.2.3. Create Membership Function (Initialization)

Membership functions are used in the fuzzification and defuzzification steps of a Fuzzy Logic Systems (FLS), to map the non-fuzzy input values to fuzzy linguistic terms and vice versa. A membership function is used in most cases to quantify a linguistic term. An important characteristic of fuzzy logic is that a numerical value does not have to be fuzzified using only one membership function. In other words, a value can belong to multiple sets at the same time.

As mentioned earlier, five membership functions were selected for each input and output variable namely; very low, low, moderate, high and very high. Figures 6-13 show the definition of the membership function for voltage, current, welding speed and gas flow rate.

Summary results of membership function and membership sets for voltage, current, weld speed, gas flow rate and percentage dilution are presented in Table 2.

For the critical rules constructed for predicting dilution using fuzzy logic, the simplified form of Figure 14 is presented as follows:

Figure 6. Definition of membership function for voltage (very low voltage).

Figure 7. Definition of membership function for voltage (low voltage).

Figure 8. Definition of membership function for current (low current).

Figure 9. Definition of membership function for current (moderate current).

Figure 10. Definition of membership function for welding speed (very low welding speed).

Figure 11. Definition of membership function for welding speed (low welding).

Figure 12. Definition of membership function for gas flow rate (low gas flow rate).

Figure 13. Definition of membership function for gas flow rate (moderate gas flow rate).

Table 2. Summary results of membership function and membership sets.

1) If voltage is low and current is high and welding speed is high and gas flow rate is high, dilution is very low.

2) If voltage is moderate and current is moderate and welding speed is moderate and gas flow rate is very high, dilution is low.

3) If voltage is high and current is high and welding speed is low and gas flow rate is low, dilution is moderate.

4) If voltage is high and current is low and welding speed is high and gas flow rate is high, dilution is high.

5) If voltage is very high and current is moderate and welding speed is moderate and gas flow rate is moderate, dilution is very high.

6) If voltage is low and current is high and welding speed is low and gas flow rate is low, dilution is moderate.

7) If voltage is low and current is low and welding speed is low and gas flow rate is low, dilution is low.

8) If voltage is low and current is low and welding speed is high and gas flow rate is high, dilution is low.

2.3. Construct the Rule Base (Initialization)

In a fuzzy logic system, a rule base is constructed to control the output variable. A fuzzy rule is a simple IF-THEN rule with a condition and a conclusion. Based on the result of Table 2, eight critical rules were constructed to predict the dilution based on fuzzy logic. Figure 14 shows the fuzzy rule editor containing the eight critical rules constructed for this problem.

3. Results and Discussion

3.1. Predicting Dilution Using Fuzzy Logic

Figures 15-22 show the predictions that were made using fuzzy logic systems.

From the result of Figure 15, it was observed that; for a voltage of 20 volt, current of 160 Amp, welding speed 170 mm/min and gas flow rate of 14 L/min, the predicted dilution was 43.10 mm.

From the result of Figure 16, it was observed that; for a voltage of 22 volt,

Figure 14. The critical rules constructed for predicting Dilution using fuzzy logic.

Figure 15. Prediction of Dilution using fuzzy logic.

current of 150 Amp, welding speed 160 mm/min and gas flow rate of 15 L/min, the predicted dilution was 56.90 mm.

From the result of Figure 17, it was observed that; for a voltage of 24 volt, current of 160 Amp, welding speed 150 mm/min and gas flow rate of 12 L/min, the predicted dilution was 70.70 mm.

From the result of Figure 18, it was observed that; for a voltage of 24 volt,

Figure 16. Prediction of Dilution using fuzzy logic.

Figure 17. Prediction of Dilution using fuzzy logic.

current of 140 Amp, welding speed 170 mm/min and gas flow rate of 14 L/min, the predicted dilution was 84.40 mm.

From the result of Figure 19, it was observed that; for a voltage of 26 volt, current of 150 Amp, welding speed 160 mm/min and gas flow rate of 13 L/min, the predicted dilution was 98.20 mm.

Figure 18. Prediction of Dilution using fuzzy logic.

Figure 19. Prediction of Dilution using fuzzy logic.

From the result of Figure 20, it was observed that; for a voltage of 20 volt, current of 160 Amp, welding speed 150 mm/min and gas flow rate of 12 L/min, the predicted dilution was 70.70 mm.

From the result of Figure 21, it was observed that; for a voltage of 20 volt,

Figure 20. Prediction of Dilution using fuzzy logic.

Figure 21. Prediction of Dilution using fuzzy logic.

Figure 22. Prediction of Dilution using fuzzy logic.

current of 140 Amp, welding speed 150 mm/min and gas flow rate of 12 L/min, the predicted dilution was 56.90 mm.

From the result of Figure 22, it was observed that; for a voltage of 20 volt, current of 140 Amp, welding speed 170 mm/min and gas flow rate of 14 L/min, the predicted dilution was 56.90 mm.

3.2. Discussion of Results

The surface plot which shows the relationship between the input and the output variable is presented in Figure 23 & Figure 24.

Result of Figure 23 and Figure 24 shows the dependence of the input variables on the output variable (dilution (%)). It shows clearly that any change in the input variable will result in a significant change in the output variable.

The randomized selected performance evaluation of fuzzy logic and experimental means in predicting dilution are shown in Table 3.

Learning parameters on the accuracy of weld predictions were studied along the variations of the input parameters for fuzzy logic and it was able to produce a model capable of predicting percentage weld dilution beyond the range of the given parameters. The randomized selected results obtained from fuzzy logic for minimized percentage weld dilution are presented in Table 3 in relation to the actual experiment which indicates that result of Fuzzy logic were very close to results of the actual or observed experiment. Table 3 also shows that it is possible to predict the percentage weld dilution with only the process parameter, we see that for a randomized serial number (R/N) of 7 in Table 3, having a voltage of 26 volts, current of 150 amps, weld speed of 160 mm/min, and gas flow rate of

Figure 23. Influence of gas flow rate and voltage on dilution (%).

Figure 24. Influence of welding speed and gas flow rate on dilution (%).

Table 3. Shows the result of using fuzzy logic to predict the dilution.

13 L/min, gave a percentage dilution of 98.20 using the fuzzy logic tool, which was very close to our experimental result. It provides enough room for process parameter simulation for optimum response, which would also save cost and material wastage that result from try and error experimentation.

This research work has thrived in developing an optimization and prediction of welds of extremely high quality of TIG welding process using fuzzy logic through which the effects of their various process parameters and their interactions were determined and predictions made on expected quality of the weld at known process parameters.

4. Conclusion

A novel concept of an intelligent model has been developed to predict welding process parameters (current, voltage, welding speed and gas flow rate) and bead parameters (%D) for improved quality welds using fuzzy logic. The results of this study will help reduce the cost of expensive analytical methods employed during welding operation and it will help fabrication industries to maximize the quality of their products with minimal stress and save them time normally applied to do a trial and error work pre to welding.

Cite this paper

Nweze, S. and Achebo, J. (2019) The Use of Fuzzy Logic in Predicting Percentage (%) Dilution of Weld during Tig Welding Process.*Materials Sciences and Applications*, **10**, 406-422. doi: 10.4236/msa.2019.105030.

Nweze, S. and Achebo, J. (2019) The Use of Fuzzy Logic in Predicting Percentage (%) Dilution of Weld during Tig Welding Process.

References

[1] Mittal, S.P. (2014) Optimization of Weld Bead Geometry in Gas Metal Arc Welding Process Using RSM and Fmincon. International Research Journal of Mechanical Engineering, 2, 114-123.

http://www.internationalscholarsjournals.org/

[2] Sathe, S.S. and Harne, M.S. (2013) Optimization of Process Parameters in Tig Welding of Dissimilar Metals by Using Activated Flux Powder. International Journal of Science and Research (IJSR), 4, 2319-7064.

[3] Sudhakaran, R., Vel-Murugan, V. and Sivasa Kthivel, P.S. (2012) Effect of Process Parameters on Depth of Penetration in Gas Tungsten Arc Welded (GTAW) 202 Grade Stainless Steel Plates Using Response Surface Methodology. The International Journal of Advanced Manufacturing Technology, 9, 64-79.

https://doi.org/10.1007/s00170-012-4117-0

[4] Joshi, J., Thakkar, M. and Vora, S. (2012) Parametric Optimization of Metal Inert Gas Welding and Tungsten Inert Gas Welding by Using Analysis of Variance and Grey Relational Analysis. International Journal of Science and Research (IJSR), 3, 2319-7064.

[5] Hussain, A.K., Lateef, A., Javed, M. and Pramesh, T. (2010) Influence of Welding Speed on Tensile Strength of Welded Joint in TIG Welding Process. International Journal of Applied Engineering Research, 1, 518-527.

[6] Lin, J.L. and Lin, C.L. (2005) Optimization of the EDM Process Based on the Orthogonal Array with Fuzzy Logic and Grey Relational Analysis Method. International Journal of Advanced Manufacturing Technology, 19, 271-277.

https://doi.org/10.1007/s001700200034

[7] Edwin Raja Dhas, J. and Kumanan, S. (2007) ANFIS for Prediction of Weld Bead Width in a Submerged Arc Welding Process. Journal OF Scientific & Industrial Research, 66, 335-338.

[8] Achebo, J. and Odinikuku, W. (2015) Optimization of Gas Metal Arc Welding Process Parameters Using Standard Deviation (SDV) and Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA). Journal of Minerals and Materials Characterization and Engineering , 3, 298-308.

https://doi.org/10.4236/jmmce.2015.34032

[9] Esme, U., Bayramoglu, M., Kazancoglu, Y. and Ozgun, S. (2009) Optimization of Weld Bead Geometry in TIG Welding Process Using Grey Relation Analysis and Taguchi Method. Materials and Technology, 43, 143-149.

[10] Kim, I.S., Son, J.S., Kim, H.H., Kim, I.J. and Kang, B.Y. (2006) A Study on Fuzzy Logic Theory to Predict the Process Parameters in GMA Welding Process. Materials Science Forum, 505-507, 541-546.

https://doi.org/10.4028/www.scientific.net/MSF.505-507.541

[1] Mittal, S.P. (2014) Optimization of Weld Bead Geometry in Gas Metal Arc Welding Process Using RSM and Fmincon. International Research Journal of Mechanical Engineering, 2, 114-123.

http://www.internationalscholarsjournals.org/

[2] Sathe, S.S. and Harne, M.S. (2013) Optimization of Process Parameters in Tig Welding of Dissimilar Metals by Using Activated Flux Powder. International Journal of Science and Research (IJSR), 4, 2319-7064.

[3] Sudhakaran, R., Vel-Murugan, V. and Sivasa Kthivel, P.S. (2012) Effect of Process Parameters on Depth of Penetration in Gas Tungsten Arc Welded (GTAW) 202 Grade Stainless Steel Plates Using Response Surface Methodology. The International Journal of Advanced Manufacturing Technology, 9, 64-79.

https://doi.org/10.1007/s00170-012-4117-0

[4] Joshi, J., Thakkar, M. and Vora, S. (2012) Parametric Optimization of Metal Inert Gas Welding and Tungsten Inert Gas Welding by Using Analysis of Variance and Grey Relational Analysis. International Journal of Science and Research (IJSR), 3, 2319-7064.

[5] Hussain, A.K., Lateef, A., Javed, M. and Pramesh, T. (2010) Influence of Welding Speed on Tensile Strength of Welded Joint in TIG Welding Process. International Journal of Applied Engineering Research, 1, 518-527.

[6] Lin, J.L. and Lin, C.L. (2005) Optimization of the EDM Process Based on the Orthogonal Array with Fuzzy Logic and Grey Relational Analysis Method. International Journal of Advanced Manufacturing Technology, 19, 271-277.

https://doi.org/10.1007/s001700200034

[7] Edwin Raja Dhas, J. and Kumanan, S. (2007) ANFIS for Prediction of Weld Bead Width in a Submerged Arc Welding Process. Journal OF Scientific & Industrial Research, 66, 335-338.

[8] Achebo, J. and Odinikuku, W. (2015) Optimization of Gas Metal Arc Welding Process Parameters Using Standard Deviation (SDV) and Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA). Journal of Minerals and Materials Characterization and Engineering , 3, 298-308.

https://doi.org/10.4236/jmmce.2015.34032

[9] Esme, U., Bayramoglu, M., Kazancoglu, Y. and Ozgun, S. (2009) Optimization of Weld Bead Geometry in TIG Welding Process Using Grey Relation Analysis and Taguchi Method. Materials and Technology, 43, 143-149.

[10] Kim, I.S., Son, J.S., Kim, H.H., Kim, I.J. and Kang, B.Y. (2006) A Study on Fuzzy Logic Theory to Predict the Process Parameters in GMA Welding Process. Materials Science Forum, 505-507, 541-546.

https://doi.org/10.4028/www.scientific.net/MSF.505-507.541