Optimal Design Of H-Shaped Forging

Original Articlepreform Optimal Design Of H Shaped Forging Based On Bi

Preform design is one of the most important aspects in forging process design. An automatic method of preform optimal design based on the bi-directional evolutionary structural optimization is presented in this paper. The complete filling of die cavity with less flash and uniform deformation of material are the goals of the preform optimal design. A new criterion of element addition/ deletion is proposed.

The new criterion is associated with objective functions. The technique of element information tracking and transferring is introduced and the variables can be transmitted from the final mesh of the forging to the background grid that represents the preform. The method of boundary smooth fitting of the preform using the B-Spline curve is given. The preform optimal design for an H-shaped forging is carried out to demonstrate the effectiveness of the proposed method. A near net-shape forming is achieved meanwhile the uniform deformation is obtained.

Paper For Above instruction

The design and manufacturing of complex forging components, such as H-shaped forgings, require meticulous preform optimization to ensure the quality and efficiency of the forging process. The process encompasses achieving full die cavity filling, minimizing flash, and ensuring uniform deformation of the material throughout the forging. In recent years, computational optimization methods, particularly bi-directional evolutionary structural optimization (BESO), have gained prominence for their ability to automate and refine preform design, thereby reducing reliance on empirical knowledge and trial-and-error procedures.

This paper proposes an innovative approach to the preform optimal design of H-shaped forgings based on BESO. The methodology integrates a novel element addition/deletion criterion linked directly to the objective functions governing the forging process. The core objective functions aim to maximize material utilization by minimizing flash and to promote uniform deformation of the material, which directly influences the microstructural uniformity and mechanical properties of the final forging.

The process begins with an initial mesh representing the initial billet, which is subjected to finite element analysis (FEA) to evaluate how well the current preform shape meets the established goals. The element information—such as strain, contact status, and deformation—is tracked and transferred across iterations using a specialized interpolation method. This ensures that relevant data accurately reflect the evolving geometry and stress distribution within the forging. Based on the analysis results, the new element addition/deletion criteria determine where material should be retained or removed, guiding the evolution of the preform shape toward optimality.

A significant innovation in this approach is employing boundary smooth fitting techniques, specifically B-Spline curves, to refine the shape of the final preform. This step ensures the transition regions of the preform are smooth and manufacturable, essential for practical forging applications. The B-Spline fitting process takes the shape of the evolved mesh's boundary and constructs a smooth curve that can be readily used to manufacture the preform and die components.

The practical implementation involves iterative cycles of FEA, shape updating based on the element addition/deletion criterion, and boundary smoothing. For illustration, the method is applied to an H-shaped forging, a typical complex geometry common in various industrial applications. The results show that the optimized preform closely approximates the final shape with minimal flash, promoting near net-shape forming. Moreover, the deformation uniformity across the forging is significantly improved, leading to higher quality final products with consistent mechanical properties.

The advantages of this method include enhanced material utilization, reduced formation of excess flash, improved deformation uniformity, and fewer iterations needed for convergence compared to traditional sensitivity-based optimization approaches. Its automation capability makes it attractive for industrial adoption, especially in complex forging operations where manual preform design is time-consuming and less reliable.

Fundamentally, this research underscores the effectiveness of integrating BESO with shape optimization techniques in forging preform design. By establishing a direct link between the objective functions and the element modification criteria, the method achieves a more logical and efficient optimization process. Future research could explore extending this approach using advanced machine learning algorithms for even faster convergence or applying it to other forging geometries and materials.

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