1. Core AI Intelligent Technology Principles
(1) 3D Vision AI Recognition System The equipment is equipped with a binocular structured-light 3D camera, paired with the PointNet++ point cloud segmentation deep learning model. After workpieces are loaded, 3D scanning and modeling are completed within milliseconds. The AI automatically distinguishes the metal substrate of molds, vulcanized rubber residues, rust spots, cured release agents and other contaminated areas, and independently divides the boundaries of cleaning zones. There is no need to manually input mold drawings or model numbers in advance.
(2) Adaptive Dynamic Laser Adjustment Logic Based on the thickness of contaminants and the curvature of mold curved surfaces, the AI outputs control instructions in real time to dynamically adjust the laser pulse power, spot size, scanning traveling speed and laser focal length. For fragile positions such as sharp mold corners and thin-wall mirror surfaces, the AI automatically reduces laser energy to avoid ablation and scratches.
(3) Local Edge Computing for Stable Operation The equipment is built with an industrial edge computing unit. All AI recognition and trajectory calculations are processed locally with an inference delay of only 12ms. The entire intelligent cleaning process can operate independently when the device is offline or disconnected from the factory LAN, free from network restrictions, making it suitable for complex workshop production environments.
2. Hardware Architecture of Dual-station Fully Automatic Production Line
The workstation adopts a left-right parallel dual-station design, matched with 6-axis industrial robots for automatic loading and unloading. Station 1 performs laser cleaning of molds, while Station 2 simultaneously unloads finished cleaned workpieces and loads new ones. There is no idle time between the two processes to maximize equipment operation efficiency. AI intelligent safety gratings are installed around the equipment, featuring multi-layer safety mechanisms including automatic shutdown when foreign objects intrude, abnormal high-temperature early warning and laser leakage protection. No workers are required to stay on-site throughout the whole process, realizing unmanned production during night shifts.
3. Core Actual Measurement Data from Factory Deployment
1. Improved cleaning efficiency: It takes an average of 90 minutes to clean a set of tire molds by traditional manual single-station equipment, while the AI intelligent workstation only needs 34 minutes, cutting working hours by 62%.
2. Reduced labor costs: Workers need to debug and inspect more than 12 times per shift with traditional equipment; the intelligent equipment requires zero manual intervention throughout operation, saving labor equivalent to 2 operators per workshop monthly.
3. Higher product yield: Manual cleaning easily causes scratches on mold mirror surfaces. After the deployment of this intelligent system, the defect rate of substrate scratches drops to zero.
4. Wide application scope: It is compatible with more than a dozen industrial mold categories, including household appliance injection molds, automotive rubber sealing part molds and large tire vulcanization molds.
4. Market Deployment Status
As of July 2026, this set of AI laser cleaning workstations has been mass-delivered to more than 20 rubber and plastic, tire manufacturing factories in the Pearl River Delta and Yangtze River Delta, including multiple supporting suppliers of auto parts. The equipment supports connection to the factory MES production management system. The AI automatically records cleaning duration, laser parameters, cleaning quality data of each mold, and generates traceable digital production reports, meeting the quality inspection and digital factory audit requirements of high-end manufacturing industries.
