ISSN:
1433-3015
Keywords:
Keywords: Artificial neural network; Springback; Vee air bending
Source:
Springer Online Journal Archives 1860-2000
Topics:
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
Notes:
Springback is a serious problem in the air vee bending process because of its inconsistency. An on-line tool to control spring-back is more reliable than an analytical model which might not be able to control the stroke of the machine in real-time. Therefore, one might resort to adaptive control or use an artificial neural network (ANN) trainer, either using experimental data or analytical predictions (or both), and use it for real-time control of the machine tool. The inconsistency in springback is then reduced to within acceptable limits. Adaptive control would need several strokes to complete the job, but it is envisaged that the job could be completed in a single stroke with the ANN. The present paper discusses the development of an ANN which can be used to train and later to predict the springback, as well as the punch travel, to achieve the desired angle in a single stroke in an air vee bending process.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/s001700050169
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