Business Scenario
Application Scenarios
Clinker outbound loading process
The customer loads clinker onto trucks via belt conveyors. The project uses a LiDAR-based truck volume measurement system to automatically measure the truck bucket space, combined with vehicle recognition and discharge control, achieving precise, managed clinker loading.
Project Background
The customer is a cement manufacturer. Its clinker loading area is equipped with 6 belt conveyors handling large daily clinker transport volumes, with a single belt capable of shipping up to 13,300 tonnes per day. Before the project, clinker loading relied mainly on manual operation: staff controlled the discharge process based on vehicle conditions and experience to meet the loading needs of different trucks. As production scale grew, the traditional manual loading approach increasingly faced efficiency and management challenges. The customer wanted to use automation to optimize the loading process, improve loading accuracy and reduce the pressure of staff attendance.
Customer Challenge
Core Customer Problem
1. Manual operation is prone to loading errors
Traditional loading relies on worker experience; differences between operators may cause over-loading and material overflow, leading to material waste and site contamination.
2. Long-term staff attendance increases management cost
Even during low-outbound periods, staff must still be on site; during continuous peak-season production, shift staffing increases management pressure.
3. Truck size differences affect loading control
Truck bucket sizes vary, and experience alone cannot accurately determine each truck's actual loading space, so digital measurement is needed to obtain vehicle data.
Solutions
Implementation Plan
Neuvition deployed a LiDAR-based truck volume measurement system for the customer's clinker loading site. The system integrates:
·LiDAR 3D scanning;
·license plate recognition system;
·intelligent camera equipment;
·encoder positioning system;
enabling vehicle recognition, 3D bucket data collection, volume calculation and auxiliary discharge control.
System workflow:
1. Vehicle recognition
When a truck enters the detection area, the license plate recognition system automatically captures vehicle information and triggers the measurement process.
2. 3D scanning measurement
The LiDAR scans the truck bucket, obtains 3D spatial data and calculates bucket dimensions and loadable volume.
3. Loading quantity calculation
The system generates loading data from the measurement results as the basis for subsequent loading control.
4. Precise loading
When the truck enters the loading area, the system uses positioning information to assist discharge position control for more precise clinker loading.
System Integration
The system is linked with the on-site loading process, forming a closed data loop from vehicle recognition and data collection to loading control.
The system involves:
vehicle recognition module;
LiDAR measurement module;
data processing system;
discharge control system.
Case Value
Case Highlights
1. LiDAR 3D measurement for precise loading
The system obtains 3D bucket data via LiDAR for automated volume measurement, achieving up to ±2% measurement accuracy and more accurate loading control than manual experience.
2. Recognition optimized for on-site vehicle conditions
Some trucks do not open their canvas covers, which affects normal measurement. To address this, the system adds a cover-open recognition feature that reminds the driver to open the cover when detected, improving measurement reliability.
3. Flexible loading quantity adjustment
For trucks on long-distance transport, the system supports adjusting the loading quantity per transport requirements to avoid overloading that affects transport safety.
4. Unmanned automatic loading
Through automated measurement and intelligent control, drivers can complete the loading process autonomously without dedicated on-site staff, reducing manual management pressure.
Project Results
1. Higher loading automation
The project shifted the customer from manual experience-based operation to data-driven, automated loading, reducing manual involvement and improving production continuity.
2. Lower staffing management pressure
After go-live, the customer reduced fixed-staff headcount by 1 and gained more flexible staffing.
3. Better loading accuracy, less material waste
Vehicle volume measurement and data calculation effectively reduce under-loading and material overflow caused by manual judgment.
4. Digital management of loading data
Vehicle information and loading data can be queried via the backend system, providing data support for production management and outbound statistics.
Quantifiable Results
Customer Concerns
Customer Concerns
1. How to reduce staff attendance and achieve automatic loading
2. How to avoid loading errors caused by manual judgment
3. How to adapt to different truck sizes for precise loading
4. How to handle special cases such as truck cover occlusion on site
5. How to digitalize loading process management