Application of intelligent manufacturing in hose clamp production: full process innovation from automated production line to digital twin

Keywords: intelligent manufacturing of hose clamps, automated production lines, digital twins, quality traceability

abstract

Traditional clamp production relies on manual operation, which has problems such as low efficiency (single piece processing time>20 seconds) and high defect rate (>5%). This article takes a certain clamp factory as an example to analyze its intelligent transformation path.

3.1 Automated Stamping and Forming

Multi station progressive die: Integrated processing of material dropping, punching, forming, and cutting is achieved through a 24 station progressive die, increasing material utilization from 70% to 90% and reducing single piece processing time to 5 seconds.

Servo press: Adopting a 1500 ton servo press, the stamping speed reaches 400 times/minute, the positioning accuracy is ± 0.01mm, and it is suitable for clamp strips with a thickness of 0.3-5mm.

3.2 Robot Welding and Assembly

Electron beam welding process: ProBeam 6kW electron beam welding machine is used, with a welding speed of 15m/min and a weld depth of 8mm, suitable for deep penetration welding of titanium alloy clamps.

Collaborative robot assembly: The Fanuc CRX-10iA robot is used to automatically load and tighten screws and nuts, with a torque accuracy of ± 1.5%. The single shift production capacity has been increased from 1000 pieces to 5000 pieces.

3.3 Online Testing and Quality Traceability

3D machine vision inspection: Deploy Keyence IV-5000 series cameras to detect clamp tooth defects (such as burrs and cracks) with an accuracy rate of 99.9% and a detection speed of 300 pieces/minute.

Blockchain quality traceability: Through the Hyperledger Fabric platform, the production batch, testing data, and logistics information of each clamp are recorded to achieve full lifecycle traceability.

3.4 Digital Twin and Predictive Maintenance

Virtual debugging and optimization: Using Siemens NX MCD to establish a digital twin model of the clamp production line, shortening the production line debugging cycle by 40%.

AI fault diagnosis: Deploy LSTM neural network model to analyze equipment vibration, temperature and other data, and warn equipment faults 96 hours in advance.

conclusion

Intelligent manufacturing has shifted the production of hose clamps from "experience driven" to "data-driven", achieving a dual improvement in quality and efficiency through the integration of automation, digitization, and intelligence.