This case study makes use of surrogate models as a replacement of costly computer simulations which is not very uncommon for the conventional Finite Element Analysis (FEA) based solvers. Model discretization and meshing is also a very critical and time consuming step for FEA. In cases of parametric studies which require a lot of iterations to create model mesh, morphing is a technique which is very helpful to adjust these design parameters and can be combined with automatic mesh generation tools. This also ensures model the model robustness and better convergence. In the related research, authors have developed surrogate model assisted optimization algorithms for single and multi- objective optimization. One of the algorithms is utilized in this case study on the actual engineering problem to generate the Pareto-front. These results show that the developed algorithms resulted into 4% improvement in NHV value as compared to reference surrogate assisted multi-objective optimization problems with improved and equally spaced Pareto-front. A post optimal study was performed on the final Pareto-front solutions to explore the hidden knowledge and derive the simple design rules for the designers. These derived relations once derived; act as a knowledge base for the designers for making informed decisions to get optimal designs without solving the actual problems for all future studies of similar nature.