Precision and Flexible Bending Control Strategy Based on Analytical Models and Data Models

Abstract

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.

A Scoping Review of Pipeline Maintenance Methodologies Based on Industry 4.0

“…The technologies inherent to Industry 4.0 allow several changes with resource optimization, improving the productivity of companies and industries. Among the main characteristics, we can mention the following: (i) Production can be made more flexible to achieve efficient customization; (ii) Devices, machines, and facilities can be monitored remotely or in situ in real-time, thus facilitating the rapid detection of failures and problem-solving; (iii) In hazardous environments or environments that generate a risk for human beings, processes can be virtualized, safeguarding the integrity of human resources; (iv) By placing flexible technologies, these can be adapted to the company's needs, freeing up human personnel to perform other types of tasks; and (v) Intercommunication between devices and operators [57,58]. Within the results shown in the previous section, it can be understood that AI and cobots, as core technologies, are the backbone of predictive maintenance and, in part, preventive maintenance.…”

Enhancing the Forming Accuracy of CFRP through a Global Compensation Method by Introducing an Optimal Profile

“…Currently, the primary methods for mitigating distortion during the manufacturing of composites in an autoclave include the traditional trial-and-error method [6], the optimized design process parameters method [7][8][9][10][11][12], the mold profile compensation method [13][14][15][16][17], the hot sizing process method [18,19], and other control methods [20]. The traditional approach relies on experience and a trial-and-error process.…”