| Place of Origin: | CHINA |
| Model Number: | Smart Checkweigher Systems with AI-Driven Quality Control in Manufacturing |
| Minimum Order Quantity: | 1 set |
|---|---|
| Prix: | Négociable |
| Packaging Details: | Caron box |
| Delivery Time: | 10-30days |
| Supply Ability: | 2000 set per month |
| Produit: | Systèmes intelligents de poids à billes avec contrôle de la qualité basé sur l'IA dans la fabricatio | Detection Capability: | Underweight/Overweight, missing components, packaging defects |
|---|---|---|---|
| Taux de faux refus: | <0,5% (auto-optimisation via un apprentissage continu) | Tolérance à l'humidité: | 10% - 95% (non condensé) |
| Résistance aux vibrations: | <0,5 m / s² (AI compense les perturbations mineures) | Protocoles de communication: | Ethernet, Wi-Fi, RS-485, Bluetooth (pour la configuration mobile) |
| Certifications: | FDA 21 CFR Part 11, UE Directive des poids et mesures (MID), GMP, ISO 9001/13485 | Exigences de données de formation: | 500 à 5 000 échantillons pour l'étalonnage du modèle initial |
| Apprendre à s'adapter: | Les poids / paramètres de mise à jour automatiquement basés sur de nouvelles données | Predictive Maintenance: | Alerts for belt wear, motor faults, or calibration drift |
| Mettre en évidence: | AI-driven smart checkweigher systems,manufacturing checkweigher with quality control,Mettler Toledo load cell checkweigher |
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| Attribute | Value |
|---|---|
| Detection Capability | Underweight/Overweight, missing components, packaging defects |
| False Rejection Rate | <0.5% (self-optimizing via continuous learning) |
| Rejection Mechanism | Pneumatic pusher, diverter arm, or drop gate |
| Humidity Tolerance | 10% – 95% (non-condensing) |
| Vibration Resistance | <0.5 m/s² (AI compensates for minor disturbances) |
| Communication Protocols | Ethernet, Wi-Fi, RS-485, Bluetooth (for mobile config) |
| Certifications | FDA 21 CFR Part 11, EU Weights & Measures Directive (MID), GMP, ISO 9001/13485 |
| Training Data Requirements | 500–5,000 samples for initial model calibration |
| Adaptive Learning | Auto-updates weights/parameters based on new data |
| Predictive Maintenance | Alerts for belt wear, motor faults, or calibration drift |
| Parameter | Specification |
|---|---|
| Weighing Range | 0.1g - 50kg (customizable based on application) |
| Accuracy | ±0.1g to ±1g (depends on speed & product type) |
| Maximum Throughput | 100 - 600 items/minute (adjustable for line speed) |
| Minimum Product Length | 10mm - 500mm (configurable belt width) |
| AI Model | Deep Learning (CNN/RNN) for anomaly detection |
| Detection Capability | Underweight/Overweight, missing components, packaging defects |
| False Rejection Rate | <0.5% (self-optimizing via continuous learning) |
| Data Logging | CSV, SQL, Cloud (AWS IoT, Azure) |
| Integration | REST API, OPC UA, MQTT, PLC (Siemens, Allen-Bradley) |
| Power Supply | 110V/220V AC, 50/60Hz |
| IP Rating | IP65 (dust/water-resistant) or IP69K (food/pharma-grade) |
| Operating Temperature | 0°C to 45°C (optional cooling/heating for extremes) |
| Belt Width | 100mm - 600mm (standard/custom) |
| HMI (Human-Machine Interface) | 7"-15" touchscreen with real-time analytics dashboard |
| Safety Features | Emergency stop, overload protection, guard rails |
| Rejection Mechanism | Pneumatic pusher, diverter arm, or drop gate |
| Conveyor Type | Stainless steel (304/316), modular belt or chain-driven |
Personne à contacter: Mrs. Shirley
Téléphone: +86-15851932889
Télécopieur: 86-519-68781609