METHODS FOR IMPROVING MACHINING ACCURACY IN THE MANUFACTURING OF ROTATIONAL PARTS ON RDB MACHINES

Abstract

The manufacturing industry increasingly demands higher precision in rotational component production, particularly for critical applications in aerospace, automotive, and precision engineering. This comprehensive review examines multiple strategies for enhancing machining accuracy in Rotary Draw Bending (RDB) processes, focusing on both conventional and advanced approaches. Through systematic analysis of recent research and technological developments, we identify and evaluate several effective methods: implementing localized thermal fields to control material behavior and reduce springback, applying push assistant loading (PAL) conditions to manage material flow and minimize defects, developing accurate mathematical models for process optimization, utilizing data-driven machine learning approaches for predictive accuracy, and optimizing tooling systems and parameters. The findings demonstrate that a combined approach integrating thermal, mechanical, and data-driven strategies can significantly improve dimensional accuracy, reduce springback, and minimize defects such as wrinkling and cross-sectional deformation in RDB processes. This research provides valuable insights for manufacturers seeking to enhance precision in rotational part production while maintaining efficiency and cost-effectiveness in industrial applications.