## 3 Results and Discussions
### Microstructural Observation  
The surfaces of cold forging backward extrusion samples of AISI 1010, AISI 1018, and AISI 1045 were prepared with standard metallographic techniques. Then, surfaces were etched with NITTAL solution and the microstructure was examined with a scanning electron microscope (SEM). The consequence of cold forging was viewed on the surface, in transverse section. The grain structure of the forged component was analyzed closely with two magnification levels and reported in Figure 24.  
Upon cold forging backward extrusion, the initial coarse-grained microstructure characterized by uniform dispersion with globules layout (because of annealing treatment) were transformed into fine grains elongated in the direction normal to the forging and can be attributed to the severe plastic deformation. The microstructure of the transverse section of the forged component of AISI 1010, AISI 1018, and AISI 1045 from Figure 24, shows the traces of refined grain that are closely packed with no signs of cracks. In contrast, mechanical integrity and uniformity are preserved along grain boundaries; clearly confirming the non-separation of grains and distortion through plastic deformation in the applied strain direction. The microstructural examinations reveal the mechanical soundness of the components produced from AISI 1010, AISI1018, and AISI 1045 and the aptness of the identified geometrical attributes.  
The micro-hardness values were obtained for the transverse section of the forged specimen of AISI 1010, AISI 1018, and AISI 1045 by a [PERSON]'s computerized micro-hardness tester. Figure 25 indicates the average micro-hardness measured on the samples of the billets as machined, annealed, and as a forged condition of AISI 1010, AISI1018, and AISI 1045 steel. It is apparent from the plot that the Vicker's value was 267 for AISI 1018, comparatively more than AISI 1045 and AISI 1010 for the forged samples. The AISI 1018 steel being low-medium carbon steel was observed to be highly responsive to the cold forming process, as evidenced by the Vickers hardness, and substantiated the steel's ability to be implemented in the production of automotive components featured with good process-induced mechanical strength.  
From the above analysis of forming behavior for different grades of steel in cold forging, it is evident that the ANN has the potential to capture a non-linearity with admirable generalization with reasonable precision. The parameters selected for ANN training and the validation were acquired by a number of simulations and found to be proper for modeling the identified geometrical attributes. In addition, DE optimization results disclosed that for every geometrical attribute, the optimal state of forming responses is different for every grade of steel. There exist several optimal solutions since the association among the  
Figure 23: Comparison of DE optimal and experimental values for effective strain rate.  
planned forming behavior and the selected variables was highly complex and non-linear. The results of DE optimization presented here represent one such possible optimal solution obtained through the designed fitness function.  
Figure 24: Microstructure of AISI steels. (**a1,a2**) AISI 1010; (**b1,b2**) AISI 1018; (**c1,c2**) AISI 1045.