Optimization of Machining Parameters for Improving Accuracy of Dimension and Shape of Bent Part in Rotary Draw Bending

Abstract:

This study dealt with the rotary draw bending method most used for tube bending and investigates how applied bending such as normal bending, using mandrels or pressing with booster have an effect on machining accuracy, focusing on dimensional defects due to springback and flat deformation to the transverse plane. The study used particle swarm optimization (PSO) algorithms to investigate the optimal machining conditions for improving the accuracy of dimension and shape of a bent part. The following findings were obtained: The springback during applied machining using a mandrel, or using a mandrel and booster together, is almost the same as during normal processing; The flattening near the center of the bend in applied processing using a mandrel, or a mandrel and booster together, decreases more than with normal processing at mandrel protrusion L ≥ 4 mm, and the maximum can be suppressed to approximately 0.15%; When the sum of the springback and the flattening is taken as the objective function and the minimum value is obtained, the optimal solution is around L = 7 mm.

• Periodical: Key Engineering Materials (Volume 904)
• Pages: 480-484
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.904.480
• Online since: November 2021
• Authors: Xia Zhu*, Sheng Lin Mu, Hiroshi Kurosu, Hiromichi Toyota, Ken Uwagawa
• Keywords: Flatness, Particle Swarm Optimization, Pipe, Rotary Draw Bending, Springback

References

[1] F. Kikuchi, K. Ooyama and K. Tashiro, Application of hydroforming to automobile parts, J. of JSTP, 47-550 (2006) 1064-1068.

DOI: 10.9773/sosei.47.1064

[2] J. Kimura, Basics and application examples of bulge processing, Press technology, Press Technology, Nikkan Kogyo Shimbun, 27-21 (1989) 81-86.

[3] A. Aidu, Pipe bending and springback, Press technology, Press Technology, Nikkan Kogyo Shimbun, 6-4 (1968) 33-36.

[4] K. Kaida, M. Hirano, T. Fujii and M. Yoshida, Aluminum Extrusion Bend Processing Technologies, Kobe Steel Engineering Reports, 47-2 (1997) 17-22.

[5] N.V. Gerge and G. Panda, A Particle-Swarm-Optimization-Based Decentralized Nonlinear Active Noise Control System，IEEE Trans．on Instrumentation and Measurement， 61-12 (2012) 3378-3386.

DOI: 10.1109/tim.2012.2205492

[6] K. Ishaque and Z. Salam, A Deterministic Particle Swarm Optimization Maximum Power Point Tracker for Photovoltaic System Under Partial Shading Condition, IEEE Trans. on Industrial Electronics, 60-8 (2013) 3195-3206.

DOI: 10.1109/tie.2012.2200223

[7] S. Das, A. Abraham, and A. Konar, Particle Swarm Optimization and Differential Evolution algorithms: Technical analysis, applications and hybridization perspectives. Studies in Computational Intelligence, 116:1–38, (2008).

DOI: 10.1007/978-3-540-78297-1_1