**6. Conclusion**

108 Recent Trends in Processing and Degradation of Aluminium Alloys

**% error SD in error % error SD in error** 

**Output Training Testing** 

Major strain 0.007 0.23 5.23 3.51 Minor strain 0.067 0.92 2.79 0.87 Failure location 0.071 0.76 *6.52* 1.82 Minimum thickness 0.003 0.03 4.28 2.64 Strain path slope 0.052 0.42 3.91 0.36

Table 4. Validation of prediction by ANN for tensile simulation with necking induced

and in thicker material side, while thinner material shows maximum draw-in.

a) Test sample-1 b) Test sample-2

deep drawing test simulation of aluminium alloy TWB

Fig. 21. Comparison of draw-in profile between ANN prediction and FE simulation for two

The deep drawing simulation data was used to train ANN to predict global TWB deep drawing behaviour viz., maximum weld line movement, draw depth, maximum punch force, draw-in profile for the chosen range of thickness and strength combinations, weld properties, orientation, and location. Two intermediate level data were taken for testing and validating the results as shown in Table 5. Fig. 21 presents the comparison between ANN and simulation results of draw-in profile of deep drawn cup. At different TWB conditions, the draw-in profile predicted by ANN model is well matched with the simulation results. All output parameters are predicted within acceptable error limits, except maximum weld line movement. Average error in this case is approximately 15% which is unacceptable. This possibly can be improved by using different strain-hardening laws and yield theories more suitable for aluminium alloy base materials. It is observed from Fig. 21a that the draw-in profiles are un-symmetric in shape. Minimum draw-in is seen along the angular weld region

failure

This chapter presented some studies on tensile and deep drawing behaviour of aluminium tailor-welded blanks. A finite element based numerical simulation method is used to understand the behaviour. The presence of thickness, strength heterogeneities and weld region deteriorates the formability of aluminium welded blanks in most of the cases. Designing TWB for a typical application will be successful only by knowing the appropriate thickness, strength combinations, weld line location and profile, number of welds, weld orientation and weld zone properties. Predicting these TWB parameters in advance will be helpful in determining the formability of TWB part in comparison to that of un-welded base materials. In order to fulfil this requirement, one has to perform lot of simulation trials separately for each of the cases which is time consuming and resource intensive. Automotive sheet forming designers will be greatly benefited if an 'expert system' is available for TWBs that can deliver its forming behaviour for varied weld and blank conditions. A artificial neural network based expert system is described which is being developed by the authors. The expert system is envisaged to be expanded with industrial applications also. For example, a sheet forming engineer who wants to develop expert system for some industrial TWB sheet part can just make it as part of existing system framework in the same line of thought, without introducing new rules and conditions. The relations between TWB inputs and outputs are non-linear in nature and hence it is complex to explicitly state rules for making expert system. But these complex relationships can be captured by artificial neural networks. The expert system proposed is a continuous learning system as the field problems solved by the system can also become a part of training sample. Though the expert system can not reason out the decisions/results unlike rule based systems, one can interpret the results by comparing the outputs of two different input conditions quantitatively with minimum knowledge in TWB forming behaviour.
