Dynamic Nest Offset and Part Routing for Adaptive Automation
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Solution Overview
Problem
Modern manufacturing and automation systems face complexity due to the need for flexibility, speed, and accuracy over time, exacerbated by varying product requirements and equipment maintenance, which necessitates an improved system for dynamic nest and station adjustment.
Innovation Solution
A method and system that utilize machine learning to analyze operational data from conveyor systems and automation stations, determining and implementing adjusted nest offsets and part routing to adapt to changing conditions, including controlling accessories and moving elements within the conveyor system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual adjustment of nest offsets and routing is performed, then system adaptability to equipment failures and product variations is improved, but system complexity and operational time increase
Solution Approach 1:
The system automatically monitors operational data, detects equipment failures and product variations, and adjusts nest offsets and routing decisions without human intervention. The automation system serves itself by making real-time adaptive decisions based on sensor data and machine learning algorithms, eliminating the need for manual adjustment while maintaining high adaptability.
Solution Approach 2:
The patent replaces manual mechanical adjustment processes with automated electronic control systems. Sensors, processors, and actuators substitute for human operators who would physically adjust nest positions and routing, thereby reducing operational complexity while maintaining or improving adaptability to changing conditions.
2Productivity
If frequent adjustments to nest offsets and routing are made to maintain productivity, then system efficiency is improved, but system stability and reliability deteriorate
Solution Approach 1:
The system continuously monitors operational data from sensors and equipment, comparing actual performance against target parameters. When deviations are detected, the system makes targeted adjustments to nest offsets and routing decisions. This closed-loop feedback mechanism maintains productivity by responding only when necessary, rather than making frequent unnecessary adjustments, thereby preserving system stability.
Solution Approach 2:
The system dynamically adapts nest offsets and routing based on real-time conditions such as equipment status, product variations, and operational parameters. This dynamic adjustment capability allows the system to maintain high productivity by optimizing performance continuously while remaining stable because adjustments are made smoothly and only when performance thresholds are breached.
3Speed
If automated machine learning systems are implemented for dynamic adjustment, then operational speed and accuracy are improved, but initial system complexity and implementation time increase
Solution Approach 1:
The system is pre-configured with machine learning models, sensor integration frameworks, and decision-making algorithms during the implementation phase. This preliminary setup enables the system to immediately begin automated adjustments upon deployment, achieving high operational speed without requiring complex real-time configuration. The initial complexity is front-loaded during implementation, simplifying ongoing operations.
Data Source
AI summary
A system and method for dynamic nest and routing adjustment in an automation system. The method includes: operating the automation system; receiving operational data related to the automation system from a conveyor system and at least one automation station; analyzing the operational data to determine if adjustment of nest offsets or routing of parts is required; determining an adjusted nest offset or part routing; implementing the adjusted nest offset or part routing; and return to operating the automation system. The system includes: a controller to operate the automation system; an input for receiving the operational data; a machine learning (ML) module to analyze the operational data to determine if adjustment of nest offsets or routing of parts is required; a processor to determine an adjusted nest offset or part routing; and a configuration module to implement the adjusted nest offset or part routing.