Optimization of Preform Design in Tadeusz Rut Forging of Heavy Crankshafts

Author(s):  
Min Churl Song ◽  
Chester J. VanTyne ◽  
Jin Rae Cho ◽  
Young Hoon Moon

Tadeusz Rut (TR) forging is a widely used forging method to create heavy, solid crankshafts for marine or power-generating engines. The preform of a TR forging is forged into a crank throw by simultaneously applying both a vertical and a horizontal deformation. It is necessary to optimize the preform design, since a conventional analytical design for the preform gives various choices for the geometric variables. The purpose of the current study is to optimize the preform design in TR forging for heavy crankshafts in order to improve the dimensional accuracy of a forged shape using a limited material volume. A finite element (FE) model for TR forging was developed and validated by comparing with experimental results. Parametric FE analyses were used to evaluate the effects of the geometric variables of the preform on the final dimensions of the forged product. The geometric variables of the preform were optimized by a response-surface method (RSM) to obtain the results of parametric FE analyses. The volume allocation between the pin and the web of the preform is the dominant factor that affects the desirability of the final forged shape. A multi-objective optimization is employed to consider the mutually exclusive changes of local machining allowances of the final forged product. Optimization using a response-surface method is a useful tool to reach the large and uniform machining allowances that are required for the preform necessary for a TR forging.

Author(s):  
Amr Ahmed Shaaban ◽  
Omar Mahmoud Shehata

Recently, studies have focused on optimization as a method to reach the finest conditions for metal forming processes. This study tests various optimization techniques to determine the optimum conditions for single point incremental forming (SPIF). SPIF is a die-less forming process that depends on moving a tool along a path designed for a specific feature. As it involves various parameters, optimization based on experimental studies would be costly, hence a finite element model (FE-model) for the SPIF process is developed and validated through experimental results. In the second phase, statistical analyses based on the response surface method (RSM) are conducted. The optimum conditions are determined using the desirability optimization method, in addition to two metaheuristic optimization algorithms, namely genetic algorithm (GA) and particle swarm optimization (PSO). The results of all optimization techniques are compared to each other and a confirmation test using the FE-model is subsequently performed.


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