Session: 04-23-01: Process Development, Characterization, and Optimization for Additive, Subtractive, and Hybrid Manufacturing

Paper Number: 164573

Designing Optimized Preforms Using Computational Techniques for Additive Manufacturing-Assisted Forging 

Forging is a metal forming process used to manufacture high-strength components commonly utilized in aerospace, automotive, and heavy industries. The process is heavily reliant on experimental knowledge. Achieving defect-free products relies on the optimal design of preforms, which is crucial for preventing defects such as underfill, surface cracks, and cold shuts, and ensuring the appropriate strain for recrystallization. Meeting these stringent quality standards requires careful consideration of several factors, including design optimization, which can help minimize dependence on conventional trial-and-error approaches, thereby cutting costs and improving efficiency. Since forging often involves multiple stages with several preforms to achieve the final product, the process can be time-consuming and expensive.

Additive manufacturing (AM)-assisted forging presents a promising solution by minimizing the number of preform stages and enabling the fabrication of complex geometries tailored for forging.  However, the lack of advanced simulation tools for preform optimization remains a challenge. Standardized testing and predictive simulation models are needed to ensure product reliability and assess the technological and economic feasibility of AM in metal forming. One approach is the use of splines which allows for a flexible modeling of preform shapes. This study emphasizes the role of computational modelling and forming simulation tools in predicting microstructure evolution and strain distribution, correlating these results with experimental data to enhance the mechanical properties of additively manufactured preforms. The initial simulation test was designed using traditional technique to forge a disk shape using a circular ingot. To take advantage of AM designed preform to facilitate preform shape generation, MATLAB and geometrical modeling tools are used to develop and model several preform shapes.  As a case study, an axisymmetric disk-shaped preform, closely resembling the final geometry, was modeled with several preform geometries manually developed using cubic spline interpolation in MATLAB by adjusting control points in the X- and Y-directions. The initial preform shape was determined through cross-sectional partition in the X-direction, ensuring strain levels post-forging remained within the 0.3–0.5 true strain range necessary for recrystallization of IN718. To achieve this, the preform height was increased by approximately 0.35–0.65 times the final height. In this work, the Finite Element Analysis (FEA) software Simufact-Forming is used to model the forging process of Inconel 718 and Stainless Steel 316L. The study explores the effects of forging parameters, such as stroke length, ram velocity, friction, and workpiece temperature, on stress and strain distribution within the final part. The simulations incorporated key forging parameters: die temperature, workpiece temperature, ambient temperature, and friction coefficient. The hammer forging process was modeled with an energy input of 500,000 J using a 5.0-ton hammer at 0.7% efficiency. Heat transfer considerations included convection coefficient to the environment, radiation coefficient, and convective heat transfer coefficient to the workpiece. Automatic remeshing was applied based on a strain threshold of 0.4 to ensure accurate stress and strain distribution predictions. The FEA simulations are conducted on multiple preform shapes, helping to identify the optimal preform shape and forging conditions based on stress and strain distributions.

The effect of preform shape on forging parameters like load, energy, die contact time was analyzed by comparing a traditional forging simulation, which begins with an ingot as the initial preform, to an intermediate preform shape generated in MATLAB using cubic splines, which can be manufactured using AM. Through these simulations, the effect of different preform shapes on the distributions of field variables, such as strain and temperature distribution, in the forged parts gives a better understanding of local changes that occur. This research demonstrates the potential of integrating advanced computational techniques, such as MATLAB, topological modeling, with FEA simulations to optimize preform designs. This integration not only enhances forging efficiency but also contributes to the design of dependable and economically viable AM preforms, facilitating the adoption of AM technologies in the metal forming industry. These efforts will help improve product quality and drive the technological and economic feasibility of AM in metal forming.

Presenting Author: Vignesh Asam Wright state university

Presenting Author Biography: Vignesh Asam is a Ph.D. student in Mechanical Engineering at Wright State University, specializing in metal forming, additive manufacturing, and finite element analysis. His research focuses on developing predictive models for the forging of additively manufactured IN718 and SS316L alloys using MATLAB-based shape optimization and Simufact-Forming simulations.

He is a Graduate Research Assistant at Wright State University and a Materials Engineer Intern at Metallurgical Solutions Inc., where he conducts materials characterization and assisting failure analysis. Vignesh holds an M.S. in Mechanical Engineering from Wright State University and a B.S. in Mechanical Engineering from Lovely Professional University. He actively participates in ASME student chapters and materials research initiatives, contributing to advancements in computational techniques for metal forming.

When he’s not immersed in forging simulations, Vignesh enjoys mentoring students in engineering labs, engaging in technical discussions, and exploring innovations in additive manufacturing.

Authors:

Vignesh Asam Wright state university
Showmik Ahsan wright state university
Raghu Srinivasan Wright state university
Henry D Young Wright State University
Mian Ahsan Wright State University