Conveyor Line ML Control for Accurate Cargo Spacing and Timing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conveyor lines in logistical systems face challenges in accurately controlling the movement of general cargo, leading to issues like collisions, missed destinations, and inadequate spacing between items, due to suboptimal acceleration and deceleration of conveyor belt sections.
Innovation Solution
A machine learning model is employed to control conveyor lines by using input data from other conveyor lines, including sensor measurements and speed data, to predict arrival times and optimize the acceleration or deceleration of individual conveyor belt sections, ensuring precise timing and spacing of cargo delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional control methods are used for conveyor line portions, then the system structure remains simple, but manufacturing precision and reliability deteriorate due to collisions, missed destinations, and inadequate spacing
Solution Approach 1:
The patent replaces conventional mechanical control systems with a machine learning-based control system. The ML model processes sensor data from multiple conveyor lines and predicts optimal control signals, substituting traditional mechanical feedback mechanisms with intelligent algorithms that learn from historical operating information to achieve precise cargo delivery.
Solution Approach 2:
The patent implements a feedback mechanism where sensor data from conveyor lines (including position, speed, and cargo detection) is continuously fed into the machine learning model. The model uses this feedback to adjust control signals for conveyor line portions, optimizing acceleration and deceleration in real-time to prevent collisions and ensure accurate delivery.
2Productivity
If fast clocking is used to adjust conveyor line speeds, then productivity improves through accurate timing, but device complexity increases due to the need for precise control of multiple conveyor line portions
Solution Approach 1:
The machine learning model serves multiple functions simultaneously: it processes sensor data from multiple conveyor lines, predicts arrival times, optimizes speed control for multiple conveyor line portions, and coordinates their operation. This multi-functionality allows the system to achieve fast clocking and accurate timing without proportionally increasing control system complexity.
Solution Approach 2:
The machine learning model is trained in advance using historical operating information from conveyor lines. This preliminary training enables the model to predict optimal control actions ahead of time, allowing the system to achieve accurate delivery timing without real-time computational delays that would increase control complexity.
3Manufacturing precision
If individual conveyor line portions are accelerated or decelerated independently, then manufacturing precision improves through better spacing control, but loss of time increases due to frequent speed adjustments
Solution Approach 1:
The patent implements dynamic speed control where each conveyor line portion's acceleration and deceleration profiles are continuously optimized by the machine learning model based on real-time cargo positions and destinations. This dynamic adjustment minimizes unnecessary speed changes while maintaining precise spacing, reducing the time lost to acceleration and deceleration cycles.
Data Source
AI summary
A process for controlling a conveyor line for general cargo is provided, the conveyor line including a plurality of consecutive conveyor line portions, each of which is driven by a drive. The drives are controlled by a computing unit using a machine learning model. The machine learning model accomplishes this by getting first input data on the basis of current operating information from at least one further conveyor line that it does not control. The machine learning model has previously been trained using second input data on the basis of operating information of the at least one further conveyor line. The operating information of the at least one further conveyor line in this instance relates to measured values from sensors for detecting general cargo and speeds of conveyor line portions.