Plastic forming is one of enabling and fundamental technologies in advanced manufacturing chains. Design optimization is a critical way to improve the performance of the forming system, exploit the advantages of high productivity, high product quality, low production cost and short time to market and develop precise, accurate, green, and intelligent (smart) plastic forming technology. However, plastic forming is quite complicated, relating to multi-physics field coupling, multi-factor influence, multi-defect constraint, and triple nonlinear, etc., and the design optimization for plastic forming involves multi-objective, multi-parameter, multi-constraint, nonlinear, high-dimensionality, non-continuity, time-varying, and uncertainty, etc. Therefore, how to achieve accurate and efficient design optimization of products, equipment, tools/dies, and processing as well as materials characterization has always been the research frontier and focus in the field of engineering and manufacturing. In recent years, with the rapid development of computing science, data science and internet of things (IoT), the theories and technologies of design optimization have attracted more and more attention, and developed rapidly in forming process. Accordingly, this paper first introduced the framework of design optimization for plastic forming. Then, focusing on the key problems of design optimization, such as numerical model and optimization algorithm, this paper summarized the research progress on the development and application of the theories and technologies about design optimization in forming process, including deterministic and uncertain optimization. Moreover, the applicability of various modeling methods and optimization algorithms was elaborated in solving the design optimization problems of plastic forming. Finally, considering the development trends of forming technology, this paper discusses some challenges of design optimization that may need to be solved and faced in forming process. 相似文献
The paper presented topology optimization of 2D and 3D Nanofluid-Cooled Heat Sink (NCHS). The flow and heat transfer problem in the NCHS was treated as a single-phase nanofluid based convective heat transfer model. The temperature-dependent fluid properties were taken into account in the model due to the strong temperature-dependent features of nanofluids. An average temperature minimum problem was studied subject to the fluid area and energy dissipation constraints by using the density method. In the method, the design variable is updated according to the gradient information obtained by an adjoint based sensitivity analysis process. The effects of the energy dissipation constraint, temperature-dependent fluid properties and nanofluid characteristics on optimal configurations of NCHS were numerically investigated with following conclusions. Firstly, branched flow channels in the optimal configuration increased with the rise of the allowed energy dissipation. Secondly, temperature-dependent fluid properties were significant for obtaining the appropriate optimal results with best cooling performance. Thirdly, heat transfer performances of optimal configurations were enhanced by reducing the nanoparticle diameter or increasing the nanoparticle volume fraction. Fourthly, the optimal configuration for nanofluid had better cooling performance than that for its base fluid. 相似文献