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So certain design modifications may be required to make the part aesthetically pleasing. Human element is still missing in both these technologies.The output design in some cases requires post-processing to meet the manufacturing feasibilities & reduce machining cost.For Generative Design, it is even higher and mostly done through cloud computing. For Topology Optimization, the computational power required is high taking from several hours to days depending upon geometry size, number of elements and number of runs, if done on the local system.It requires a trained designer to put correct boundary conditions and ensure all possible failure modes are part of the input conditions.PREREQUISITES & PRECAUTIONS FOR TOPOLOGY OPTIMIZATION AND GENERATIVE DESIGN: If that happens, alternate approach could be user can set target weight with number of runs and convergence criteria to complete the process quicker. However, the time taken for these criteria is unpredictable and may take too long to complete. A general method can be to define maximum allowable stress or minimum factor of safety along with convergence criteria. Another option is to select convergence criteria, wherein the optimization process will complete if the change in weight between two sequential simulation iteration is less than convergence criteria. The trade-off here is results may not be most optimal. Number of Runs & Convergence: You can set the number of runs if you want the optimization process to complete in a stipulated time.If target weight specified for Topology Optimization is too low, study will most likely fail.

It is not an ideal option in situations where you want to maximize reduction in weight. The optimization process will stop once the target weight is achieved. Target Weight: If you already have a set target weight for your product, you can specify in this step.