Forming of various customized bending parts, small batches, as well as numerous types of materials is a new challenges for Industry 4.0, the current control strategies can not meet the precision and flexibility requirement, expected control strategy of bending processes need to not only resist unknown interferences of process condition and models, but also produce various new parts automatically and efficiently. In this paper, a precision and flexible bending control strategy based on analytical models and data models is proposed to build adaptive bending systems. New analytical prediction models for loading and unloading are established and suitable for various materials, a sequential identification strategy is proposed to search nominal properties using the four sub-optimization models. A data-based feedback model is established to prevent over-bending and eliminate online deviation. Above models are merged into a precision and flexible control strategy. The system firstly uses sub-optimization models to search the nominal point which is near to target point, secondly the system further uses feedback model to eliminate residual error between the nominal point and target point. Compared with four kinds sheet metals, the allowable ranges for variables are determined for a good convergence. The target bending angles were set to 20°, 40°, and 60°. Forty parts were tracked for each kind material, the adaptive bending system converged after one iteration, and exhibited better performances.
Forming of various customized bending parts, small batches, as well as numerous types of materials is a new challenges for Industry 4.0, the current control strategies can not meet the precision and flexibility requirement, expected control strategy of bending processes need to not only resist unknown interferences of process condition and models, but also produce various new parts automatically and efficiently. In this paper, a precision and flexible bending control strategy based on analytical models and data models is proposed to build adaptive bending systems. New analytical prediction models for loading and unloading are established and suitable for various materials, a sequential identification strategy is proposed to search nominal properties using the four sub-optimization models. A data-based feedback model is established to prevent over-bending and eliminate online deviation. Above models are merged into a precision and flexible control strategy. The system firstly uses sub-optimization models to search the nominal point which is near to target point, secondly the system further uses feedback model to eliminate residual error between the nominal point and target point. Compared with four kinds sheet metals, the allowable ranges for variables are determined for a good convergence. The target bending angles were set to 20°, 40°, and 60°. Forty parts were tracked for each kind material, the adaptive bending system converged after one iteration, and exhibited better performances.
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