Forming Quality Control of an AA5182-O Aluminum Alloy Engine Hood Inner Panel

Abstract

The poor formability of aluminum alloys at room temperature easily leads to quality defects such as ruptures, wrinkles and excessive springback after forming, which severely restricts their wide application in the automotive industry. In this article, a combination of springback compensation and process parameter optimization is proposed to improve the forming quality of aluminum alloy auto body panels. First, the springback compensation for the tooling setting was conducted using the global shape modeling (GSM) function in ThinkDesign to ensure the desired dimensional quality of the hood. Then, the process parameter optimization was conducted based on the combination of a back-propagation (BP) neural network and genetic algorithm (GA) method to improve formability. After springback compensation and process parameter optimization, the obtained product could satisfy the matching requirements well. A case study of an AA5182-O aluminum alloy engine hood inner panel is presented. The experiments demonstrate that the combination of the springback compensation and the optimization scheme based on the BP neural network and GA can effectively improve the product’s forming quality.