Volume 8, 2021
|Number of page(s)||10|
|Published online||02 March 2021|
Optimisation of micro W-bending process parameters using I-optimal design-based response surface methodology
School of Mechanical Engineering, Sichuan University, Chengdu, PR China
2 Centre for Precision Manufacturing, Department of Design, Manufacturing and Engineering Management, The University of Strathclyde, Glasgow, UK
3 Malaysian Institute of Aviation Technology, Universiti Kuala Lumpur, Dengkil, Malaysia
* e-mail: email@example.com
Accepted: 3 February 2021
There is an increasingly recognised requirement for high dimensional accuracy in micro-bent parts. Springback has an important influence on dimensional accuracy and it is significantly influenced by various process parameters. In order to optimise process parameters and improve dimensional accuracy, an approach to quantify the influence of these parameters is proposed in this study. Experiments were conducted on a micro W-bending process by using an I-optimal design method, breaking through the limitations of the traditional methods of design of experiment (DOE). The mathematical model was established by response surface methodology (RSM). Statistical analysis indicated that the developed model was adequate to describe the relationship between process parameters and springback. It was also revealed that the foil thickness was the most significant parameter affecting the springback. Moreover, the foil thickness and grain size not only affected the dimensional accuracy, but also had noteworthy influence on the springback behaviour in the micro W-bending process. By applying the proposed model, the optimum process parameters to minimize springback and improve the dimensional accuracy were obtained. It is evident from this study that the I-optimal design-based RSM is a promising method for parameter optimisation and dimensional accuracy improvement in the micro-bending process.
Key words: Micro-forming / micro-bending / springback / response surface methodology / I-optimal design / optimisation
© X. Liu et al., Published by EDP Sciences 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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