Methodenplanung

Birkert, Arndt; Haage, Stefan; Straub, Markus

doi:10.1007/978-3-642-34670-5_8

Cited by 1 publication

(1 citation statement)

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“…The need for faster vehicle development processes has existed for decades now, and with the introduction of higher strength materials in car body engineering, the process forces are increasing. The total cost of tryout accounts for approximately 25% or more of tooling costs and offers significant potential for cost reduction [1]. The elastic behavior of the press and tool plays an increasingly important role in the result of the forming process.…”

Section: Introductionmentioning

confidence: 99%

Methodic Approach for the Simulation-Based, Elastic Die Surface Compensation by Creating a FE-model of the Car Body Press Predicated on Spotting Images via Neural Networks

Göltl,

Birkert,

Harst

et al. 2024

IOP Conf. Ser.: Mater. Sci. Eng.

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In recent years, the push for streamlined vehicle development and the adoption of higher-strength materials to reduce the car body weight has been prominent. However, this shift results in greater elastic deformations in press and tool components due to the elevated yield stress of these materials. The die active face is subject to the compliance of the body press and die, which influences the manufacturability and dimensional accuracy of the sheet metal part. In practice, manual die spotting is required, relying on empirical data and subjective criteria for acceptance. This paper presents a new methodological approach for the development of the car body press’s FE model in structural and forming coupled simulations. To achieve this, a series of measurements quantifies the pressure between tool and sheet metal. This information is fed into a neural network along with the die spotting images as training data. The network evaluates subsequent spotting images, assigning pressure values to distinct tool sections. Based on the die pressure distribution in the process, a virtual press model is created with the help of a newly developed objective function in topology optimization. This model aims to significantly enhance simulation-based die surface compensation, effectively reducing the die spotting process.

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Section: Introductionmentioning

confidence: 99%