Vision-Guided Bottle and Cap Feeding to Prevent Filling Line Jams
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Solution Overview
Problem
Conventional bottling and capping processes suffer from inefficiencies due to misalignment of bottles and caps, leading to frequent jams, operational downtime, increased maintenance, and reduced throughput, with mechanical systems requiring manual intervention and accelerated wear.
Innovation Solution
A system utilizing 3D vision inspection and robotic arms with AI-based object detection and pose estimation to align and place bottles and caps accurately, ensuring upright orientation for filling and vertical orientation for capping, minimizing downtime and enhancing automation reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If mechanical feeders and rotating drums are used to align and feed bottles and caps, then automation is achieved, but misalignment occurs frequently leading to jams and stoppages
Solution Approach 1:
The patent replaces traditional mechanical feeders and rotating drums with a vision-guided robotic system. 3D cameras capture images of bottles and caps, AI algorithms determine their positions and orientations, and robotic arms precisely pick and place them. This substitution of mechanical systems with intelligent automation eliminates the misalignment and jamming issues inherent in conventional mechanical feeding mechanisms.
2Productivity
If mechanical feeders are used to align containers, then feeding automation is achieved, but alignment precision is insufficient causing jams
Solution Approach 1:
The system replaces mechanical alignment mechanisms with a vision-based detection and robotic positioning system. 3D cameras capture precise spatial information, AI algorithms process this data to determine exact positions and orientations, and robotic arms execute precise placement. This achieves both high feeding speed and high alignment precision simultaneously.
Solution Approach 2:
The vision system creates a digital copy or representation of the physical bottle and cap positions and orientations through 3D imaging. This digital model is then processed by AI to plan the precise robotic movements needed for accurate alignment and placement, enabling high precision without complex mechanical alignment mechanisms.
3Reliability
If frequent start and stop operations are performed to clear jams, then misalignment issues are addressed, but equipment wear increases
Solution Approach 1:
By replacing mechanical feeders with vision-guided robotics, the system achieves reliable alignment from the start without needing to stop and correct misalignments. The robotic system's precision eliminates the repetitive start-stop cycles that cause wear, thereby extending equipment lifespan.
Solution Approach 2:
The vision system detects bottle and cap positions and orientations before the feeding process begins, allowing the AI to plan precise robotic movements in advance. This preliminary detection and planning prevents misalignment before it occurs, eliminating the need for corrective stoppages and reducing wear on equipment.
4Reliability
If manual intervention is required to clear jams, then alignment problems are resolved, but operational downtime increases
Solution Approach 1:
The vision-guided robotic system automatically detects and corrects alignment issues through precise robotic placement, eliminating the need for manual intervention. The system continuously monitors and adjusts positions, resolving alignment problems without stopping production and without requiring human operators to clear jams.
Solution Approach 2:
The system performs self-correction of alignment issues through its vision detection and robotic adjustment capabilities. When misalignment is detected, the AI plans corrective movements and the robotic arms execute them automatically, allowing the system to service itself without external intervention or production downtime.
Data Source
AI summary
Improved methods and a system for feeding containers and caps into a filling line and a capping line are provided. A 3D vision inspection system identifies a container located proximal to a top of a heap within a containers bin. A first set of robotic arms picks the identified container, places it onto a conveyor input in an upright orientation with the open end of the container facing upwards. The container is then transferred into a conveyor or accumulation table for transport to a filling station. Similarly, a third 3D camera identifies caps within a caps bin. A second set of robotic arms picks the identified caps, places them onto an alignment station, and orients them for placement into the capping line. The system incorporates Artificial Intelligence (AI)-based computer vision models for object identification and orientation, enhancing the reliability and efficiency of automated bottling and capping operations.