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Printing machine (GKG)

Parameter Table for GKG Printing Machine

Parameter Description
Printing Precision Supports ​01005 components (0.25×0.125 mm) with micron-level alignment
Printing Speed Adjustable via PLC-controlled system; optimized for high-throughput SMT lines

Lighting System Dual-ring LED + coaxial lighting with adjustable brightness/color temperature for ​uneven or reflective surfaces (e.g., FPC, gold-plated PCBs)
Stencil Compatibility Flexible clamping for stencils up to ​550×650 mm; rapid changeover for multi-product lines
PCB Size Range 250×400 mm to ​1200×300 mm; supports single/double-sided boards
Control System Windows XP-based HMI with ​real-time parameter adjustment and fault diagnostics
Cleaning Mechanism Dry/wet cleaning modes; CCD and cleaning modules operate independently to reduce downtime
Squeegee System Programmable pressure (0–20 kg), speed, and lift height; dual squeegees for stable paste release
Drive Mechanism Haydon linear motors for micron-level motion accuracy and repeatability
Compliance Standards IPC-A-610, ISO 9001; RoHS-compatible processes

 

The ​GKG Printing Machine is a high-precision stencil printer designed for ​SMT (Surface Mount Technology) manufacturing, specializing in solder paste deposition for miniaturized components like ​01005 chips and large PCBs up to ​1200×300 mm. Key innovations include its ​adaptive lighting system (adjustable ring and coaxial LEDs) for reliable mark recognition on challenging surfaces (e.g., FPC, gold-plated boards) and ​Haydon linear motor drives for ±0.01 mm repeatability. With a Windows-based HMI, it enables real-time adjustments of pressure, speed, and stencil alignment while supporting dry/wet cleaning cycles to minimize defects. Widely used in automotive, consumer electronics, and 5G hardware production, GKG printers ensure compliance with ​IPC-A-610 standards and reduce setup time via rapid stencil/PCB changeovers. Models like the ​G5 and ​GT++ emphasize modularity for Industry 4.0 integration, featuring AI-driven process optimization and SPC data analytics for smart factory workflows

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