Container Receptacle Alignment Using Neural Network Image Rotation
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Solution Overview
Problem
Existing container treatment machines require precise operator input and expertise to align containers correctly, which is inefficient and prone to errors, especially when dealing with variations in container features such as embossments and wear over time.
Innovation Solution
A container treatment machine equipped with a neural network that processes images of containers to determine the necessary rotation to achieve the target attitude, allowing for automatic alignment with minimal operator input, using Deep Neural Networks or Convolutional Neural Networks for pattern recognition and learning from previous alignment attempts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a camera-based rotation method is used to align containers by detecting product features, then the container can be aligned to the target attitude, but the process requires precise operator input and expertise, reducing ease of operation
Solution Approach 1:
The system performs self-alignment by automatically detecting container features and calculating the necessary rotation without requiring operator expertise. The control unit autonomously processes camera images, identifies alignment features, and determines the rotation angle, making the system self-sufficient in the alignment task.
Solution Approach 2:
The patent replaces manual operator judgment and mechanical alignment methods with an automated optical-mechanical system. A camera captures container images, a control unit processes the data to determine alignment features and rotation angles, and an actuator executes the rotation, substituting human expertise with an automated sensing-control-actuation system.
2Reliability
If complete container examination is performed for each alignment, then reliable alignment is achieved, but the process efficiency is limited, reducing productivity
Solution Approach 1:
The system performs preliminary detection of alignment features using a camera before the actual alignment operation. By capturing and analyzing container images in advance, the control unit can determine the necessary rotation angle beforehand, enabling rapid and reliable alignment without time-consuming manual inspection during the alignment process itself.
Solution Approach 2:
The system uses camera-based feedback to continuously monitor container position and alignment features. The control unit processes this visual feedback to automatically adjust the container orientation, creating a closed-loop control system that achieves reliable alignment efficiently without requiring complete manual examination of each container.
3Adaptability or versatility
If the system adapts to variations in container features over time, then alignment reliability is maintained, but the device complexity increases
Solution Approach 1:
The system dynamically adapts to different container types and feature variations by continuously analyzing camera images and adjusting the alignment algorithm parameters. The control unit can recognize different alignment features (such as seams, labels, or geometric features) and automatically adjust the detection and rotation calculations to suit the specific container being processed, providing adaptability without requiring manual reconfiguration.
Data Source
AI summary
A container treatment machine comprises a treatment unit, for the treatment of containers, and container receptacles, in which containers can be received such that they can rotate about an axis, the container treatment machine comprising a camera, for capturing an image of a container transported upstream of the treatment unit in a container receptacle, and an alignment module, the alignment module being designed to rotate a container into a target position by actuating the container receptacle. The alignment module comprises a neural network, which, by processing the image of a container transported upstream of the treatment unit in a container receptacle, can determine a necessary rotation of the container from the current position of same to the target position, and the alignment module can control the rotation of the container receptacle on the basis of the determined rotation.