Pick-and-Place Feeder Replenishment Using Real-Time PCB Position
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Solution Overview
Problem
Current methods for predicting and ensuring the timely replenishment of pick and place material in pick and place lines are inaccurate, leading to potential downtimes and inefficiencies due to reliance on idealized operations rather than real-time conditions.
Innovation Solution
A method that detects current filling levels, planned operations, and component carrier positions to predict replenishment times, accounting for disruptions and optimizing material supply across multiple feed tracks, allowing for prioritization and automated replenishment by robots or operators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If threshold value monitoring is used to track material filling levels, then material replenishment can be triggered when levels drop, but this method does not account for real-time operational disruptions and transport delays, leading to inaccurate replenishment timing
Solution Approach 1:
The system continuously monitors the actual position of component carriers on the transport line and feeds this information back to the prediction algorithm. This real-time feedback loop allows the system to adjust replenishment predictions based on actual operational conditions, including any disruptions or delays in carrier transport, thereby improving both reliability and timing accuracy
Solution Approach 2:
The system automatically calculates and predicts replenishment times based on real-time data without requiring manual intervention. The prediction algorithm self-adjusts by incorporating actual carrier positions and transport conditions, enabling the system to serve itself by making autonomous replenishment decisions based on current operational state
2Ease of manufacture
If centralized material warehouse picking is used with hourly cycles, then material quantity can be determined and transferred, but the actual replenishment process on pick and place machines is not monitored, leading to potential stockouts
Solution Approach 1:
The system implements continuous monitoring of material filling levels at component feeders and feeds this information back to the central control. This real-time feedback enables the system to track the actual replenishment process and trigger new replenishment cycles before stockouts occur, maintaining both ease of management and supply reliability
Solution Approach 2:
The system transitions from discrete hourly replenishment cycles to continuous monitoring and prediction. The prediction algorithm continuously calculates replenishment needs based on real-time carrier consumption rates and positions, ensuring uninterrupted material supply while maintaining centralized logistics management
3Productivity
If component feeders are replaced automatically by robots to prevent standstill, then machine availability is maintained, but accurate prediction of replacement timing is critical to avoid unnecessary interruptions
Solution Approach 1:
The system performs preliminary prediction of feeder replacement timing by continuously analyzing material consumption rates and carrier positions. This advance prediction allows the robot to prepare and execute feeder replacements at the optimal moment, maintaining productivity while minimizing disruption time
Solution Approach 2:
The prediction algorithm dynamically adjusts replacement timing based on real-time operational conditions. As carrier positions and consumption rates change, the predicted replacement time is continuously updated, allowing the system to adapt to varying production speeds and maintain optimal productivity
4Reliability
If splicing is performed manually by operators to ensure continuous material supply, then material continuity is maintained, but operators have limited time to complete splicing before component feeders become empty
Solution Approach 1:
The system automatically performs the splicing function through robotic feeder replacement, eliminating the need for manual operator intervention. The robot autonomously executes the replenishment action at the predicted optimal time, ensuring material continuity while removing the complexity and time pressure of manual splicing operations
Data Source
AI summary
A method and a pick and place line for predicting a time for replenishing pick and place material (C) comprises (a) detecting a current filling level of component material from a first component feeder; (b) determining the planned pick and place operations which are still required in order to populate all component carriers (PCB) of a current batch production in a first pick and place station; (c) determining a position of a component carrier along the transport path which has been populated in a second pick and place station and which is still to be populated in the first pick and place station; and (d) predicting the time for replenishing pick and place material at the first feed track based on the detected current filling level, the determined planned populated operations and the determined current position.