In the wave of manufacturing transformation and upgrading, the folding machine as the core equipment of sheet metal processing, its automation level directly affects production efficiency and product quality. Traditional equipment relies on mechanical cam or simple PLC control, which has the problems of low positioning accuracy, slow response speed and complex debugging. Through the integration high-performance PLCs and multi-axis servo control systems, precise control of the equipment's motion trajectory, dynamic adjustment of process parameters and real-time collection of production data can be realized, laying the foundation for intelligent manufacturing.
I. System Architecture Design: Layered Control of Hardware-Software Synergy
1.1 Collaborative logic of a three-tier architecture
The three-layer structure of edge computing node + PLC + servo driver is adopted, and the division of labor among each layer is clear:
Edge layer: Deployment of an industrial PC or smart gateway to run a Python/C + -developed preprocessing algorithms to filter sensor data, extract features, and detect anomalies. For example, a moving average filter algorithm can be used to eliminate noise interference from temperature sensors, or a threshold based approach can determine whether oil pressure exceeds the safe limit.
Control layer: PLC acts as core controller, performing logic control and motion planning. The Siemens S7-1200, for example, has a motion control module that manages six servo axes simultaneously and supports PROFINET bus communication for microsecond-level synchronous control.
Execution Layer: The servo driver receives PLC command and drives motor to complete precise movement. For example, a certain brand's servo system with a 23-bit encoder resolution, combined with feedforward compensation algorithms, can limit positioning errors to ±0.01 mmWave.
1.2 Key Indicators for Hardware Selection
PLC performance: Supports high-speed counting (≥200kHz), pulse output (≥1MHz), and floating-point arithmetic to meet complex motion control requirements.
Servo System: Select drivers that support full closed-loop control with a high-resolution encoder (≥17 bits) to ensure compensation for mechanical transmission errors.
Communication Interface: Prioritize real-time Ethernet protocols such as PROFINET and EtherCAT are prioritized for multi-axis synchronization control and low latency data transmission.
ii. Servo System Integration: from cabling to Parameter Optimization
2.1 Hardware Connection Specifications
In the case of a folding machine, servo system integration requires the following steps:
Power Wiring: Connect the U/V/W terminals of the servo driver to the motor to ensure correct phase sequence and avoid reverse rotation.
Encoder Feedback: The motor encoder is connected to the driver by a differential signal line, grounding the shielding end to suppress interference.
Control signal: PLC to drive outputs pulse (Y0) and direction signals (Y1), connecting enabled signal (SON) and alarm reset signal (RES).
Safety Grounding: All equipment should be on the same ground, power and signal lines should be laid separately and kept ≥ 30cm apart to avoid coupling interference.
2.2 Parameter Configuration Essentials
The performance of the servo system depends on the optimization of parameters. Key parameters include:
Electronic Gear Ratio: calculated according to mechanical transmission ratio. For example, if the motor rotates in a complete circle corresponding to the 10mm motion of the roller and the encoder has a resolution of 4000 pulses per rotation, the electron gear ratio is set to 1:4 (molecular 1, denominator 4) so that for every 4000 pulses sent by PLC, the roller moves 10mm.
Gain adjustment: Optimize position loop (P23) and speed loop (P24) gain through automatic adjustment. For systems with a load inertia ratio of 5: 1, the position loop gain can be set to 50Hz and the speed loop gain to 200Hz after automatic tuning to eliminate mechanical resonance.
Filter Parameters: set speed feedforward (P15) and acceleration feedforward (P16) coefficients to compensate for mechanical inertia. For example, setting P15 to 0.8 reduces tracking errors by 80%.
III. PLC Program Development: Integration Ladder Diagrams and Advanced Instructions
3.1 Basic Control Logic
In the case of positioning mode, PLC programs need to perform the following functions:
Servo enabled: Control driver's SON signal through output point Y2. Examples of programmes:
Positioning Control: Use the DRVI instruction for relative positioning. Program example
Status Monitoring: Read the driver's alarm signal (X1) and positioning completion flag (M8029). Program example:
3.2 Implementation of Advanced Functions
Multi-Axis Synchronization: Synchronization of spindle to spindle is achieved through the PROFINET bus, and the spindle sends synchronized signals from the spindle to the spindle, following the movement from the spindle to the gear ratio. For example, by setting the ratio of electronic gears on the spindle (X-axis) and from the spindle (Y axis) to 1:1, a 45-degree edge folding can be achieved.
Dynamic adjustment of process parameters: PLC calculates servo speed and acceleration according to preset algorithms by input of material thickness and roller pressure on touch screen. For example, for every 1mm increase in material thickness, the servo speed decreases by 10%.
Fault Diagnosis and recovery: Record servo alarm codes (such as overload and overpressure), display the cause of the fault through HMI, and provide a one-button reset function.
IV. INTRODUCTION Debugging and optimization: from single step to Full Process Verification
4.1 Hardware Debugging Steps
Start Inspection: Make sure the driver doesn't have an alarm (display "00"), the PLC's RUN light is on, and the motor isn't making any unusual noise.
Jog Test: Force PLC to output pulses (such as PLSY K1000 K100 Y0) to see if the motor rotates in the desired direction and speed.
Encoder Feedback Verification: Driver validation of actual location to match number of pulses sent by PLC with error ≤ ≤ 0.1%.
4.2 Software Debugging Techniques
One-step operation: Trigger positioning instructions in PLC monitoring mode, observe pulse output, D8140 change (current pulse count), and whether the M8029 (completion flag) is set.
Variable Monitoring: Real-time monitoring of servo system parameters such as actual speed (r0021), torque (r0031), and adjustment of gain parameters to eliminate overload.
Online Debugging: Performs a multi-segment positioning programs to measure the roller's movement distance with a dial indicator and compare it to a calculation based on command pulses. Accuracy should ≤ 0.02mm.
V. Application Case: Upgrading Practice of an Automotive Component Production Line
An enterprise's folding machine originally used mechanical cam control, faced with the following problems:
Product replacement requires manual adjustment of the cam, each replacement takes 2 hours.
The margin angle error ± 0.5??, and the product qualification rate only 85%.
Real-time production data could not be collected and equipment utilization statistics relied on manual methods.
The following improvements have been achieved through the integration of PLC and servo systems:
Flexible Production: product parameters can be input through HMI, PLC automatically calculate the servo trajectory, changeover time reduced to 5 minutes.
Accuracy Improvement: The error of hem angle decreased to ±0.1° and the pass rate increased to 99.2%.
Data drive operation: the servo current, temperature and other data are collected, and equipment failure prediction was realized by edge computing, which reduces maintenance cost by 30%.
VI. INTRODUCTION Future Prospects: Artificial intelligence and Digital Twins merge in depth
With the development of Industry 4.0, the integration of PLC and servo systems will lead to intelligent development:
AI-Optimized Control: Machine learning algorithms can analyze historical data and automatically adjust servo gain parameters based on different material characteristics.
Digital Twins: Virtual models of devices can be constructed, programs can be debugged in virtual environments, and downtime can be reduced.
5G + Edge Computing: Leverages 5G low latency for remote monitoring and collaborative manufacturing to support cross-plant resource scheduling.
The automation upgrade of the folding machine is not just a hardware upgrade, but also a revolution of control concepts. Through the deep fusion of PLC and servo systems, enterprises can realize transparency, flexibility and intelligence of production process, which provides the key support for the transition to intelligent manufacturing.
