Conveyor Line ML Control for Precise Cargo Spacing
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Solution Overview
Problem
Conventional conveyor line control systems face challenges in accurately positioning general cargo due to poor acceleration and deceleration control, leading to collisions, missed destinations, and inefficient transport times, especially in complex logistical systems with varying conveyor belt speeds and friction issues.
Innovation Solution
A machine learning model is employed to control conveyor line portions by receiving input data on the proportional occupancy of each section, allowing for individual acceleration or deceleration of each conveyor line portion, using sensors such as light barriers, 2D/3D cameras, or RFID systems, to achieve precise control and maintain defined distances between items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional control systems are used for conveyor lines, then the system structure is simple, but the positioning precision deteriorates leading to collisions and missed destinations
Solution Approach 1:
The patent replaces conventional mechanical control systems with a machine learning-based control system. The ML model processes sensor data and generates control signals for conveyor belt drives, substituting traditional mechanical control mechanisms with an intelligent system that achieves superior positioning precision while preventing collisions and missed destinations.
Solution Approach 2:
The patent implements a feedback mechanism where sensors continuously monitor the positions of items on the conveyor line and feed this information back to the machine learning model. The model uses this real-time feedback to adjust control signals dynamically, optimizing acceleration and deceleration profiles to maintain precise positioning and prevent collisions.
2Measurement precision
If fast clocking is used to set destination accurately, then the arrival precision improves, but the acceleration and deceleration control deteriorates causing collisions and instability
Solution Approach 1:
The patent applies dynamic control by using a machine learning model that continuously adapts acceleration and deceleration profiles based on real-time sensor feedback. Instead of fixed control parameters, the system dynamically adjusts control signals to optimize both arrival precision and transport stability, preventing collisions while maintaining fast clocking operation.
Solution Approach 2:
The patent changes control parameters dynamically by having the machine learning model adjust acceleration and deceleration values based on current system state. The model processes sensor data and generates optimized control parameters for each conveyor belt drive, enabling precise destination arrival while maintaining transport stability through adaptive parameter adjustment.
3Measurement precision
If multiple sensors per conveyor line portion are used, then the detection accuracy improves, but the device complexity and cost increase
Solution Approach 1:
The patent implements multi-functionality by using a single sensor per conveyor line portion that performs multiple detection functions. The sensor simultaneously detects the presence, position, and movement state of items, eliminating the need for multiple specialized sensors while maintaining high detection accuracy through sophisticated signal processing by the machine learning model.
Solution Approach 2:
The patent applies self-service by enabling each sensor to provide comprehensive position information through intelligent signal processing. The machine learning model processes sensor outputs to extract multiple pieces of information (presence, position, movement state) from single sensor readings, allowing the sensor system to serve multiple detection purposes independently without additional hardware.
4Productivity
If conventional control is used, then the system is easy to operate, but the transport time increases due to poor acceleration and deceleration control
Solution Approach 1:
The patent replaces simple mechanical control with an intelligent machine learning-based control system that optimizes acceleration and deceleration profiles. The ML model processes sensor data and generates optimized control signals that reduce transport time while maintaining ease of operation through automated control, eliminating the need for manual intervention.
Solution Approach 2:
The patent applies preliminary action by having the machine learning model predict optimal acceleration and deceleration profiles in advance based on sensor feedback. The system proactively adjusts control signals to optimize transport time before items reach critical positions, improving productivity while maintaining simple operation through automated predictive control.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution enables accurate and efficient control of conveyor lines, reducing collisions and transport time, while eliminating the need for expensive additional sensors, and allowing for high-speed operation with millisecond timing precision.
Implementation Method 1
The sensors are light barriers
Implementation Method 2
RFID receivers in combination with RFID tags on the general cargo
Data Source
AI summary
A process for controlling a conveyor line for general cargo, the conveyor line including a plurality of consecutive conveyor line portions , each of which is driven by a drive. One or more sensors for detecting general cargo are located on at least some of the conveyor line portions. The drives are controlled by means of a computing unit using a machine learning model. The machine learning model accomplishes this by repeatedly receiving input data including a vector of a fixed length, each vector element being associated with a section of the conveyor line and indicating a current proportional occupancy of the respective section by an item of general cargo. Each conveyor line portion is split into a plurality of the sections of identical size. An apparatus or a system for data processing, a computer program, a computer-readable data carrier and a data carrier signal is also provided.