Neural Network Container Alignment for Precise Receptacle Rotation
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Solution Overview
Problem
Existing container alignment methods require precise user input and are inefficient in adapting to variations in container features, leading to potential misalignment and reduced process efficiency.
Innovation Solution
A container treatment machine equipped with a neural network that processes container images to determine the necessary rotation to align containers accurately, using features like molded seams or embossings, and adjusts the container holder to achieve the target position, with the ability to learn and adapt over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional alignment methods using camera capture and rotation are used, then container alignment can be achieved, but the process requires precise user input and considerable operator experience, significantly impairing efficiency when target position specification is incorrect
Solution Approach 1:
The system performs self-alignment by automatically detecting container features and calculating the necessary rotation without requiring operator input for target position specification. The neural network processes container images and autonomously determines alignment parameters, eliminating the need for operator experience in specifying target positions.
Solution Approach 2:
The patent replaces the manual mechanical alignment process with an automated vision-based system. Instead of relying on operators to visually assess and manually adjust container positions, the system uses image capture, neural network processing, and automated rotation control to achieve precise alignment.
2Reliability
If traditional alignment methods examine each container individually are used, then alignment can be achieved, but the efficiency of the process can only be increased up to a limit determined by operator skills
Solution Approach 1:
The system performs preliminary examination and learning from previously processed containers to establish alignment patterns and features. By pre-processing container images and learning from the series, the system builds a knowledge base that accelerates subsequent alignment operations while maintaining high reliability.
Solution Approach 2:
The system implements feedback by continuously learning from examined containers and using this information to improve future alignment decisions. The neural network processes each container's features and uses this information to refine its understanding of container variations, thereby improving both reliability and speed over time.
3Measurement precision
If precise user specification of target position is required, then alignment accuracy can be maintained, but the method is not suitable for drawing conclusions for future containers from already examined containers
Solution Approach 1:
The system performs preliminary examination of container series to identify common features and patterns before processing individual containers. By pre-analyzing the container series and establishing baseline alignment criteria, the system maintains accuracy while enabling learning and adaptation across the series.
Solution Approach 2:
The system uses feedback from examining each container to continuously refine its understanding of the container series. The neural network learns from the features and variations observed in already examined containers and applies this knowledge to improve the alignment of future containers in the series, maintaining precision while enabling adaptability.
Data Source
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AI summary
The invention relates to a container treatment machine for the treatment of containers such as bottles, cans or the like. The 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, characterised in that 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.