Simulation and Prediction of Springback in Sheet Metal Bending Process Based on Embedded Control System.

Area of Science:

• Manufacturing Engineering
• Automation and Control Systems
• Materials Science

Background:

• The increasing demand for flexible, efficient, and adaptable production in sheet metal bending necessitates advanced automation solutions.
• Existing systems face challenges in precisely controlling bending processes, particularly in managing springback errors.
• The need for integrated control systems capable of real-time adjustments is paramount for small-batch, multi-varietal manufacturing.

Purpose of the Study:

• To design and develop an embedded soft Programmable Logic Controller (PLC) for enhancing sheet metal bending automation.
• To improve the precision and reliability of CNC bending machinery through advanced control strategies.
• To mitigate and compensate for springback errors in the sheet metal bending process.

Main Methods:

• Development of an embedded soft PLC using the Codesys platform and ARM Cortex-A55 architecture.
• Implementation of the EtherCAT communication protocol for seamless integration with servo-driven CNC bending machines.
• Construction and refinement of a finite element simulation model with Arbitrary Lagrangian-Eulerian (ALE) adaptive grid technology.
• Deployment of an enhanced Whale Optimization Algorithm-Backpropagation (WOA-BP) model, incorporating Latin hypercube sampling and neural networks, for springback error prediction and compensation.

Main Results:

• The developed embedded soft PLC effectively controls fully electric servo-driven CNC bending machinery.
• The finite element simulation model, enhanced with ALE technology, significantly improved simulation precision for springback.
• The WOA-BP model successfully predicted and counteracted springback errors, achieving high accuracy.
• Experimental results demonstrated that the final forming angle deviation was constrained within 0.3 degrees.

Conclusions:

• The proposed embedded soft PLC and advanced error compensation methodology provide a technically feasible and effective solution for sheet metal bending automation.
• This approach significantly enhances the reliability and precision of the bending system, meeting the demands for adaptable production.
• The study contributes to the advancement of automated manufacturing in the sheet metal industry through intelligent control and simulation.