In a flexible multi-tray manufacturing system for intelligent warehousing, online pallet quality inspection is a core element in ensuring production efficiency and product reliability. By integrating IoT, AI, and multi-sensor technologies, the system enables real-time monitoring and closed-loop quality management throughout the entire pallet manufacturing process.
Online pallet quality inspection requires the construction of a multi-dimensional data acquisition network. A flexible multi-tray manufacturing system for intelligent warehousing typically deploys equipment such as laser rangefinders, visual recognition cameras, pressure sensors, and temperature and humidity monitoring modules on the production line. Laser rangefinders, by emitting a beam and measuring the reflection time, can capture pallet flatness, dimensional accuracy, and stacker crane docking deviations in real time. The visual recognition system uses high-definition cameras to acquire pallet images and combines deep learning algorithms to analyze contour integrity, surface defects, and marking clarity. Pressure sensors are embedded in the pallet's load-bearing area to dynamically monitor load distribution and deformation.
Temperature and humidity sensors，track the environmental adaptability of pallets made of special materials (such as wood and composite materials). This sensor data is aggregated to a central control platform via industrial Ethernet or wireless transmission protocols, forming a digital profile of pallet quality.
Artificial intelligence algorithms are a key support for pallet defect identification. The system employs a convolutional neural network (CNN) to train visual data, enabling accurate identification of defects such as cracks, burrs, and color differences on pallet surfaces, significantly improving accuracy compared to traditional manual visual inspection. For pallet structural strength testing, the system combines a finite element analysis model with real-time pressure data to simulate stress distribution under different load conditions, providing early warnings of potential fracture risks. Furthermore, through transfer learning technology, the system can quickly adapt to the testing needs of pallets of different sizes, reducing model training time.
Multi-sensor data fusion technology enhances the reliability of testing results. The intelligent warehousing flexible multi-tray manufacturing system uses a Kalman filter algorithm to fuse laser ranging and visual positioning data, eliminating measurement errors caused by environmental interference (such as dust and vibration) from a single sensor. For example, in pallet stacking scenarios, the system simultaneously collects vertical height data from the laser rangefinder and spatial coordinate information from the vision system, ensuring that the inter-layer spacing of pallets meets standards through data calibration. For dynamic load testing, the system correlates and analyzes pressure sensor data with motor current signals to comprehensively assess the structural stability of the pallet during high-speed handling.
Real-time early warning and closed-loop control mechanisms improve the fault tolerance of the production process. When the system detects a pallet size deviation exceeding a threshold, it immediately triggers a production line pause command and uses digital twin technology to simulate a corrective plan, guiding operators to adjust mold parameters or cutting paths. For batch quality issues, the system can trace back to specific production batches and equipment stations, combining historical data to pinpoint the root cause of process defects. For example, if a batch of wooden pallets frequently cracks, the system will correlate and analyze parameters such as raw material moisture content, drying temperature, and pressing pressure to optimize the production process.
The collaborative architecture of edge computing and cloud computing ensures inspection efficiency. At the production line end, edge computing nodes perform preliminary screening and preprocessing of sensor data, uploading only key feature values to the cloud, reducing network transmission load. The cloud platform is responsible for storing historical data, training AI models, and generating quality reports, supporting experience sharing and algorithm iteration across multiple production lines. For example, the system can identify regional quality fluctuation patterns by comparing pallet inspection data from different factories, providing a basis for supply chain optimization.
The online inspection function of the intelligent warehousing flexible multi-tray manufacturing system also extends to the warehousing and logistics环节. By installing weight sensors and displacement monitoring devices on warehouse racks, the system can periodically assess the creep characteristics of pallets under long-term static loads and predict their service life. For automated storage and retrieval systems (AS/RS), the system combines AGV handling trajectory data with pallet deformation monitoring results to dynamically adjust storage location strategies, avoiding the placement of heavy goods on aging pallets and reducing safety risks.
The intelligent warehousing flexible multi-tray manufacturing system utilizes IoT sensing, AI analysis, and multimodal data fusion technology to construct a pallet quality control system covering the entire chain of production, inspection, and warehousing. This system not only realizes the transformation from "post-event sampling inspection" to "end-to-end intelligent inspection," but also promotes the development of pallet manufacturing towards intelligence, standardization, and sustainability through data-driven decision optimization.
