Views: 0 Author: Site Editor Publish Time: 2025-08-19 Origin: Site
In the highly automated world of hosiery production, solenoid valves serve as the unsung nervous system that ensures seamless operations. These electromechanical workhorses enable precise fluid and air control across critical sock manufacturing stages – from knitting and dyeing to finishing and packaging.
Modern computerized sock-knitting machines (e.g., Santoni, Lonati) rely on solenoid valves for:
Stitch Formation Control: Rapid air-jet actuation (20–50 ms response time) guides yarn carriers with micron-level accuracy.
Needle Bed Adjustment: Pneumatic cylinders regulated by 3/2-way valves synchronize needle movements for complex patterns.
Yarn Break Detection: Instant air shutoff via normally closed valves upon thread snap prevents waste.

In jet dyeing machines, corrosion-resistant stainless steel solenoid valves (e.g., ASCO 210 series) manage:
Chemical Dosing: Proportional valves automate caustic soda/hydrogen peroxide injection at ±1.5% concentration tolerance.
Temperature Control: Steam mixing valves maintain dyebath temperatures at 85°C±2°C for color consistency.
Effluent Management: Automated wastewater discharge complies with ZDHC wastewater guidelines.

Steam Finishing Tunnels: Direct-acting valves regulate steam pressure at 2.5–3 bar for anti-shrink treatment.
Automated Inspection Systems: Pneumatic ejectors (actuated by 5/3-way valves) remove defective socks at 120 pairs/minute.
Moisture Management: Humidity-controlled valves in drying ovens prevent over-drying (maintains 8–12% moisture content).
Vacuum Forming: High-flow valves create suction for sock positioning on cardboard inserts.
Bagging Machines: Sequential valve timing enables gas-flush packaging (N₂ or CO₂) for odor prevention.
Carton Erecting: Pneumatic arms controlled by valve manifolds handle 200 boxes/hour.
Miniaturization: 10mm compact valves for robotic sock sorting arms
Hygienic Design: Clean-in-Place (CIP) compatible valves for antimicrobial socks
AI Integration: Valve performance data training ML algorithms for defect prediction