CHAPTER 8 ANFIS MODELING OF FLANK WEAR 8.1 AISI M2 HSS TOOLS

Transcription

1 CHAPTER 8 ANFIS MODELING OF FLANK WEAR 8.1 AISI M2 HSS TOOLS Control surface as shown in Figs gives the interdependency of input, and output parameters guided by the various rules in the given universe of discourse. These rules were implemented in MATLAB environment using Sugeno type of fuzzy inference system in fuzzy logic toolbox. Control surface given in Fig. 8.1 shows the inter dependency of flank wear on soaking temperature and cutting speed, Fig. 8.2 shows inter dependency of flank wear on soaking temperature and cutting time and Fig. 8.3 shows inter dependency of flank wear on cutting speed and cutting time. Results predicted from this ANFIS model have been compared with the experimental results for its validation. The tool flank wear predictions of the developed ANFIS model as a function of the experimentally determined values are shown in Fig Note that the comparison was made using values only from the test data set, which was not introduced to the ANFIS during the training process. In order to assess the accuracy of ANFIS predictions, graphic is provided with a straight line indicating perfect prediction. As seen in Fig. 8.4, the ANFIS predictions for the flank wear result in a mean relative error of 2.47% and correlation coefficient of with the experimental data. Also, the p value of 0.96 (Table 8.1) calculated by performing ANOVA indicate that the difference reported in experimental and modeled flank wear values is not statistically significant. 196

2 Fig. 8.1 Control surface of fuzzy model showing inter dependency of flank wear on soaking temperature and cutting speed. Fig. 8.2 Control surface of fuzzy model showing inter dependency of flank wear on soaking temperature and cutting time. 197

3 Fig. 8.3 Control surface of fuzzy model showing inter dependency of flank wear on cutting speed and cutting time. Fig. 8.4 The ANFIS predictions for the tool flank wear vs. experimental values. 198

4 Table 8.1 Results of ANOVA applied to experimental and modeled flank wear values. Sum of Degree of Mean F Significance squares freedom square value Between group 7.812E E Within groups E 02 Total TUNGSTEN CARBIDE TOOLS Control surface as shown in Figs gives the interdependency of input, and output parameters guided by the various rules in the given universe of discourse. These rules were implemented in MATLAB environment using Sugeno type of fuzzy inference system in fuzzy logic toolbox. Control surface given in Fig. 8.5 shows the inter dependency of flank wear on soaking temperature and cutting speed, Fig. 8.6 shows inter dependency of flank wear on soaking temperature and cutting time and Fig. 8.7 shows inter dependency of flank wear on cutting speed and cutting time. Results predicted from this fuzzy model of turning have been compared with the experimental results for its validation. The tool flank wear predictions of the developed ANFIS model as a function of the experimentally determined values are shown in Fig Note that the comparison was made using values only from the test data set, which was not introduced to the ANFIS during the training process. In order to assess the accuracy of ANFIS predictions, graphic is provided with a straight line indicating perfect prediction. As seen in Fig. 8.8, the ANFIS predictions for the flank wear result in a mean relative error of 1.24% and correlation coefficient of with the experimental data. Also, the p value of (Table 8.2) calculated by performing ANOVA indicate that the difference reported in experimental and modeled flank wear values is not statistically significant. 199

5 Fig. 8.5 Control surface of fuzzy model showing inter dependency of flank wear on soaking temperature and cutting speed. Fig. 8.6 Control surface of fuzzy model showing inter dependency of flank wear on soaking temperature and cutting time. 200

6 Fig. 8.7 Control surface of fuzzy model showing inter dependency of flank wear on cutting speed and cutting time. Fig. 8.8 The ANFIS predictions for the tool flank wear vs. experimental values. 201

7 Table 8.2 Results of ANOVA applied to experimental and modeled flank wear values. Sum of Degree of Mean F Significance squares freedom square value Between group 1.186E E Within groups E 02 Total These results demonstrate that the ANFIS predicts flank wear excellently for both types of tools, although the tests for acquiring data were performed in a broad range of turning conditions. Considering the prediction performances reported in Fig. 8.4 and 8.8, one can conclude that ANFIS has a great ability to learn from inputoutput patterns and predict the output variables of the system. The results demonstrate that the ANFIS can be successfully applied to predict tool flank wear. The developed ANFIS model can also be used for investigating the effects of the input parameters on the performance parameters of the system. During the course of the turning experiments, it was found that some turning experiments yielded results in contradiction with the rules proposed by the model. This mainly suggests that the turning environment is different from the place at which these rules were obtained. Therefore, to be able to use these rules effectively, the model needs calibration for each particular turning place. This calibration can be done by performing a small number of experiments in the new turning environment. The rules are held constant but the membership functions adjusted to yield better predictions for a new turning environment. It is evident that the rules of the model are absolute, while membership function definitions will vary from one turning place to another. Given the general improvements in quality control, differences among various turning environments will continue to decline. Therefore, membership function fine-tuning should be easy. To help in the fine-tuning process, the relative fuzzy rules can be used, thus providing a relative sense of direction, and helping in identifying ways to adjust the membership functions (e.g. shift them left or right). Hence, calibrating the model to match a given turning environment may require only a handful of short experiments. 202