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.

Identifying the yield strength of materials quickly and accurately is the key to realizing defect prediction and digital process control on the production line. This paper focuses on identifying the material yield strength based on bending deformation, analyzing the influence of different die fillets, punch fillets, and die spans on the curve shapes, determining the reasonable dimensions of the device, and developing them. Two methods for rapidly extracting the yield load are proposed—the window vector method (WV) and the fitting residual method (FR)—and compared with the double secant line method (CWA) and the one tenth thickness method (t/10). Because there is no direct correspondence between the yield load and the material performance parameters, the relevant equations were fitted using the experimental data. The linear correlation between load and yield strength determined by these four methods was close to 0.99. Finally, four kinds of sheets with high, medium and low yield strength were tested and compared with the observed results. The result shows that when the yield strength is small, the average error and the relevant model dispersion will increase. As the yield strength increases, the biases increase gradually. The prediction errors based on the t/10, WV, and FR methods were all below 4%.

The online derivation of material yield is important for consistency control of product quality. Compare with other deformation, bending deformation is more suitable for online identification. The transition portion of bending force stroke curve is related to the yield strength of material, but for a wide set of materials with different thickness and properties, its longer transition curve shows higher identification scattering. In this paper, the yield characteristics strengthening method and the new anti-dispersion identification method are studied to reduce the scattering. It is found that the yield characteristics are comprehensively affected by several factors. The higher yield strength, together with thinner thickness, can weaken the yield characteristics. The thicker material with wider die span can strengthen the yield characteristics. Window vector method (WV) and standardized fitting residual method (SFR) are proposed. Through the numerical study found that the yield load obtained by WV method shows a most accurate correlation with yield strength. A novel piecewise correlation equation is proposed and added a thickness variable to eliminate partial dispersion from varied thickness. Furthermore, this paper uses the experimental data to extract the generalization error of new correlation model fitted by yield loads from WV method, maximum error is below 12%. This method can improve the accuracy and efficiency of online ranking materials.

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 built 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.