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Since 1997, General Motors Body Manufacturing Engineering - Die Engineering Services (BME-DES) has been working jointly with our software vendor to develop and implement a parallel version of stamping simulation software for mass production analysis applications. The evolution of this technology and the insight gained through the implementation of DMP/MPP technology as well as performance benchmarks are discussed in this publication.

In this paper, the factors that affect the accuracy of springback prediction are discussed. Springback predictions of aluminum and high strength steel panels are compared with measurement data. The effect of springback can be reduced or eliminated through process control and die face compensation. The first method involves finding the root causes of springback and eliminating them through process modification. The second method is a direct way to eliminate the springback effect. For large springback with twisting, an incremental compensation is required and the final deviation can be controlled by setting tight convergent tolerance. Stamping production environment can introduce many variables which deviate from engineering condition. The paper shows that material property change within the same grade will cause significant springback variation. This means that the process control is one of the key factors that we have to pay attention to solve springback issue.

As a virtual manufacturing press line, line die forming simulation provides a full range math-based engineering tool for stamping die developments of automotive structure and closure panels. Much beyond draw-die-only formability analysis that has been widely used in stamping simulation community during the last decade, the line die formability analysis allows incorporating more manufacturing requirements and resolving more potential failures before die construction and press tryout. Representing the most difficult level in formability analysis, conducting line die formability analysis of automotive body side outers exemplifies the greatest technological challenge to stamping CAE community. This paper discusses some critical issues in line die analysis of the body side outers, describes technical challenges in applications, and finally demonstrates the impact of line die forming simulation on the die development.

In automotive body manufacturing, there are two processes are often applied, Nominal Build and Functional Build. The Nominal Build process requires all individual stamping components meet their nominal dimensions with specified tolerances. While, the Functional Build process emphasizes more on the tolerances of the entire assembly as opposed to those of the individual stamped parts. The common goal of both processes is to build the body assemblies that meet the specified tolerances. Although there is strict tolerance specified for individual stamping parts the finished stampings frequently are released to assembly process with certain levels of dimensioning deviations, or they are within the specified tolerances but require heavy clamping during assembly. It is of high interest to predict the dimensional deviations in the stamping sub-assembly or body-in-white assembly process.

A common occurrence in computer aided design is the need to make changes to an existing CAD model to compensate for shape changes which occur during a manufacturing process. For instance, finite element analysis of die forming or die tryout results may indicate that a stamped panel springs back after the press line operation so that the final shape is different from nominal shape. Springback may be corrected by redesigning the die face so that the stamped panel springs back to the nominal shape. When done manually, this redesign process is often time consuming and expensive. This article presents a computer program, FESHAPE, that reshapes the CAD or finite element mesh models automatically. The method is based on the technique of volume morphing pioneered by Sederberg and Parry [Sederberg 1986] and refined in [Sarraga 2004]. Volume morphing reshapes regions of surfaces or meshes by reshaping volumes containing those regions.

Surface distortions are frequently introduced into the Class “A” surfaces during various sheet metal forming operations such as drawing, trimming and flanging. The origins of those surface distortions have not been well understood. The scope of this research is to investigate the distortion that occurs in draw operation and to find effective and practical corrective methods. Five geometric parameters are first identified to represent a typical depression feature in automobile outer panels. Experimental dies are then designed to reflect various combinations of these five geometric parameters with the assistance of numerical simulations to ensure that the dies can make parts free of major defects like splits and wrinkles. Surface distortions are observed in our stamping experiments and various techniques are used to measure and record the distortions for further mathematical analysis.

Draw beads are commonly used in stamping operations to control sheet metal flow. The amount of sheet metal flow, or called draw-in amount, is an important index that controls the formability and quality of sheet metal parts. Numerical simulation of sheet metal forming processes is widely adopted. The draw-in map obtained through simulation serves as a guideline for successful die tryout and robust stamping. This study addresses some issues of using draw bead in simulations such as the calculation of draw bead holding and pulling forces, the thinning effect of real draw bead on draw-in amount, the adjustment of math based draw-in amount, the feasibility of using draw bead geometry in simulation, and the effect of draw bead location and configuration on sidewall curl.

The CAE analysis revealed that a stamping component required extremely high yield strength steel in order to pass the safety requirements. However, springback with this component could cause assembly distortions. The formability prediction was compared with the actual nominal die stamping for splits and wrinkles. The predicted free state springback was compared to the measurements. A compensated die was engineered based on the springback dimensions of the free state stamping. Morphing the negative residual surface created the dimensional compensation. Then a new compensated die face was created. A CAE prediction was used to confirm and adjust the compensated die using iterations of predictions and corrections.

A demonstration of the preform anneal process was conducted to form a one-piece aluminum door inner. In preform annealing, the aluminum panel is partially formed, annealed at 350°C to eliminate the cold work (strain hardening) from the first step, and then formed to the final shape using the same die. This process has the ability to form more complex parts than conventional aluminum stamping. Preform annealing uses non-age hardenable aluminum alloys of the 5xxx series and is suitable for a wide range of interior body panels. A rear door inner panel for a mid-size sports utility vehicle (SUV) was used in this study. This door inner was successfully created in one piece out of AA5182-O sheet with only slight design modifications to the original steel product geometry. The design of the door inner panel was conducted based on finite element analysis and predictions were verified with physical parts using thickness measurements and mechanical testing.

The analysis of localized necking is strongly dependent on the yield function. Numerous yield criteria have been advanced to characterize the plastic deformation of sheet materials. Among them Hill's 1948 and the fourth form of 1979 yield criteria are the most commonly used yield criterion. A new and user-friendly yield criterion was proposed by Hill in 1993, which uses five independent and easily-obtainable material parameters. The present investigation compares these three yield criteria in forming limit predictions based on the M-K approach. The M-K analysis based on Hill's 1993 yield criterion yields forming limit predictions for aluminum in good agreement with experimental data. All three yield criteria are found to provide acceptable predictions for aluminum killed steel.