Abstract
The production of hybrid components involves a long process chain, which leads to high investment costs even before machining. To increase process safety and process quality during finishing, it is necessary to provide information about the semi-finished parts geometry for the machining process and to identify defect components at an early stage. This paper presents an investigation to predict variations in dimension and cavities inside the material during cross-wedge rolling of shafts based on measured tool pressure. First, the process is investigated with respect to the variation in diameter for three roll gaps and two materials. Subsequently, features are generated from the hydraulic pressures of the tools and multi-linear regression models are developed in order to determine the resulting diameters of the shaft shoulder. These models show better prediction accuracy than models based on meta-data about set roll gap and formed material. The features are additionally used to successfully monitor the process with regard to the Mannesmann effect. Finally, a sensor concept for a new cross-wedge rolling machine to improve the prediction of the workpiece geometry and a new approach for monitoring machining processes of workpieces with dimensional variations are presented for upcoming studies.
Highlights
The mass reduction of components is one of the most effective methods of reducing CO2 emissions and fuel consumption in the mobility sector [1]
This paper focuses on the signal-based prediction of workpiece quality of cross-wedge rolling (CWR) components to provide additional information for the machining process
For the shafts formed with a roll gap of 29 mm, the Mannesmann effect in CWR process can be determined by monitoring the hydraulic pressures
Summary
The mass reduction of components is one of the most effective methods of reducing CO2 emissions and fuel consumption in the mobility sector [1]. Based on the process data of the previous production steps, it is possible to predict, for example, the geometry of the semi-finished part before machining Using this information, the influences of the variation of the geometry can be reduced during process monitoring and defective components can be removed. This paper focuses on the signal-based prediction of workpiece quality of CWR components to provide additional information for the machining process For this purpose, two materials are formed with three different roll gaps. Features are generated from the measured hydraulic pressures, which are directly in correlation with the forming forces These as well as the meta-data about set roll gap and selected material are used to investigate a model-based prediction of the workpiece geometry. The monitoring can be individually adapted to the workpiece to achieve higher process safety
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