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CN-121972541-A - Bending control method for metal plate processing

CN121972541ACN 121972541 ACN121972541 ACN 121972541ACN-121972541-A

Abstract

The invention relates to the technical field of metal plate processing, in particular to a bending control method for metal plate processing, which is realized based on integrated equipment, wherein the integrated equipment comprises a tool withdrawal assembly, a feeding rotary pressing arm device and a bent shaft connecting rod bending structure, an AI prediction module and a wireless communication module are integrated through a control platform to form closed-loop control logic, and the method comprises the following steps of S1, system initialization and data acquisition, connection of a cloud database through the wireless communication module, historical bending data downloading and plate initial parameter acquisition by utilizing a sensor, and S2, AI auxiliary feeding and compaction control, and fine adjustment of feeding position and compaction force according to rebound compensation output by the AI prediction module. The precision is improved, the rebound compensation controls the bending angle deviation within +/-0.2 degrees (the traditional method is more than +/-1 degree), and the rejection rate is reduced by 30%.

Inventors

  • SHI ZHIREN
  • WANG XIANDE

Assignees

  • 安徽礌鸿智能装备有限公司

Dates

Publication Date
20260505
Application Date
20260302

Claims (9)

  1. 1. A bending control method for metal plate processing is characterized by being realized based on integrated equipment, wherein the integrated equipment comprises a tool withdrawal assembly, a feeding rotary pressing arm device and a crankshaft connecting rod bending structure, and the method integrates an AI prediction module and a wireless communication module through a control platform to form closed-loop control logic and comprises the following steps: S1, initializing a system and collecting data, connecting a cloud database through a wireless communication module, downloading historical bending data, and collecting initial parameters of a plate by using a sensor; Step S2, AI assists feeding and compaction control, fine tuning feeding position and compaction force according to rebound compensation quantity outputted by AI prediction module; s3, intelligent bending and rebound compensation control is carried out, a bending target angle is adjusted based on an AI prediction result, and a crankshaft connecting rod bending mechanism is driven to apply force; s4, retracting and self-adapting adjusting, adjusting the length of the pressing cutter according to a prediction result, and monitoring the state of the equipment through wireless communication; And S5, closed-loop optimization and remote maintenance, updating an AI model by using bent data, and supporting remote diagnosis and parameter adjustment.
  2. 2. The bending control method for metal plate processing according to claim 1, wherein the AI prediction module adopts a deep learning model, wherein model inputs comprise plate material, thickness, bending angle and historical rebound data, model outputs are rebound compensation amounts, and the deep learning model is an LSTM or CNN framework and supports online learning.
  3. 3. The bending control method for metal plate processing according to claim 1, wherein the wireless communication module realizes data transmission through an IoT platform and encrypts data by adopting an MQTT protocol, and the wireless communication module is integrated in sensors of a calibration table of a feeding rotary press arm device and a crankshaft connecting rod bending mechanism and uploads angle, displacement and pressure data in real time.
  4. 4. The bending control method for sheet metal processing as set forth in claim 1, wherein the AI assisting feeding and pressing control in step S2 specifically includes: the feeding device adjusts the position according to the feeding offset predicted by AI, and the dislocation placement is realized through a main pressing arm deflection mechanism; the compression mechanism drives the pressure head through a third cylinder, and the AI module dynamically adjusts the compression force according to the real-time pressure data; in the feeding process, the wireless communication module synchronizes the calibration data to the cloud platform.
  5. 5. The method for controlling bending for metal sheet processing as set forth in claim 1, wherein the intelligent bending and springback compensation control in step S3 specifically includes: The bending mechanism receives an AI instruction, and adjusts the bending target angle to be the sum of the original angle and the rebound compensation amount; The crankshaft connecting rod driving mechanism drives the bending fixing seat to float up and down through the coordinated operation of the driving motors from one to four; After bending, the sensor measures the actual angle, and the rebound error data is used for model optimization.
  6. 6. The bending control method for sheet metal processing as set forth in claim 1, wherein the retracting and adaptive adjustment in step S4 specifically includes: The cutter retracting assembly drives the cutter pressing mechanism to displace through the first cylinder and the second cylinder, and the cutter pressing length is adjusted based on the rebound trend predicted by AI; the inflation control of the self-made micro air cylinder I and the self-made micro air cylinder II is synchronous through the wireless module, so that the quick disassembly or expansion of the pressing cutter main body is realized.
  7. 7. The bending control method for sheet metal processing as set forth in claim 1, wherein the closed-loop optimization and remote maintenance in step S5 specifically includes: After each batch of processing, the AI model is retrained based on new data, and the local model is updated through a wireless network; the cloud platform provides visual reports and operators remotely debug the device parameters through a wireless interface.
  8. 8. A bending control method for metal sheet processing as defined in claim 1, wherein the method is suitable for metal sheet bending processing, the sheet size ranges from 10cm to 50cm, and bending angle deviation is controlled within +/-0.2 degrees.
  9. 9. A control system for implementing the bending control method for sheet metal processing as claimed in any one of claims 1 to 8, characterized in that the control system comprises: The AI prediction module is deployed on the control platform and is used for rebound prediction; The wireless communication module is used for data transmission and remote monitoring; The sensor group is integrated in feeding, bending and tool retracting equipment and is used for collecting real-time data; the actuator group comprises an air cylinder and a driving motor and is used for driving each component to act.

Description

Bending control method for metal plate processing Technical Field The invention relates to the technical field of metal plate processing, in particular to a bending control method for metal plate processing. Background The bending processing of the plate belongs to a key process in sheet metal forming, and relates to a plurality of steps of feeding, compacting, bending, retracting and the like. In the prior art, bending equipment is generally controlled in a discrete mode, and all components independently operate and lack cooperativity. Bending rebound is a core problem affecting molding accuracy, resulting in deviation of actual bending angle from target angle (often exceeding ±1°). Traditional control methods such as PID adjustment or empirical compensation have limited precision and cannot be suitable for small-batch production of multiple varieties. In addition, equipment monitoring relies on a local system, and fault diagnosis and maintenance are required to be operated on site, so that the efficiency is low. Disclosure of Invention The invention aims to provide a bending control method, which predicts bending resilience in real time through an AI prediction module, compensates and adjusts bending parameters in advance and combines wireless communication to realize remote monitoring and cooperative control. The method solves the problems of low rebound control precision, isolated operation of equipment and the like in the prior art. In order to achieve the purpose, the invention provides a bending control method for metal sheet processing, which is realized based on integrated equipment, wherein the integrated equipment comprises a tool withdrawal assembly, a feeding rotary pressing arm device and a bent shaft connecting rod bending structure, and the method integrates an AI prediction module and a wireless communication module through a control platform to form closed-loop control logic, and comprises the following steps: S1, initializing a system and collecting data, connecting a cloud database through a wireless communication module, downloading historical bending data, and collecting initial parameters of a plate by using a sensor; Step S2, AI assists feeding and compaction control, fine tuning feeding position and compaction force according to rebound compensation quantity outputted by AI prediction module; s3, intelligent bending and rebound compensation control is carried out, a bending target angle is adjusted based on an AI prediction result, and a crankshaft connecting rod bending mechanism is driven to apply force; s4, retracting and self-adapting adjusting, adjusting the length of the pressing cutter according to a prediction result, and monitoring the state of the equipment through wireless communication; And S5, closed-loop optimization and remote maintenance, updating an AI model by using bent data, and supporting remote diagnosis and parameter adjustment. Preferably, the AI prediction module adopts a deep learning model, wherein the model input comprises plate material, thickness, bending angle and historical rebound data, the model output is rebound compensation quantity, and the deep learning model is of an LSTM or CNN architecture and supports online learning. Preferably, the wireless communication module realizes data transmission through an internet of things (IoT) platform, encrypts data by adopting an MQTT protocol, and is integrated in a calibration table of a feeding rotary pressing arm device and sensors of a crankshaft connecting rod bending mechanism to upload angle, displacement and pressure data in real time. Preferably, the AI-assisted feeding and compacting control in step S2 specifically includes: the feeding device adjusts the position according to the feeding offset predicted by AI, and the dislocation placement is realized through a main pressing arm deflection mechanism; the compression mechanism drives the pressure head through a third cylinder, and the AI module dynamically adjusts the compression force according to the real-time pressure data; in the feeding process, the wireless communication module synchronizes the calibration data to the cloud platform. Preferably, in step S3, the intelligent bending and rebound compensation control specifically includes: The bending mechanism receives an AI instruction, and adjusts the bending target angle to be the sum of the original angle and the rebound compensation amount; The crankshaft connecting rod driving mechanism drives the bending fixing seat to float up and down through the coordinated operation of the driving motors from one to four; After bending, the sensor measures the actual angle, and the rebound error data is used for model optimization. Preferably, the retracting and adaptive adjusting in step S4 specifically includes: The cutter retracting assembly drives the cutter pressing mechanism to displace through the first cylinder and the second cylinder, and the cutter pressing length is adjusted based on the