Bowl Feeder Control Using Flow Velocity to Prevent Jams
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Solution Overview
Problem
Manufacturing processes face inefficiencies due to asynchronous movement of workpieces on inline feeders and bowl feeder jams, leading to production losses and downtime.
Innovation Solution
A system and method utilizing a processor to receive images from sensors, determine flow velocity and other parameters, and apply predictive models to generate control settings for bowl feeders, including motor, blow-off, and hopper controls, to optimize feeding and detect anomalies in manufacturing lines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If bowl feeders are used to feed parts one-by-one to manufacturing lines, then parts can be fed individually with specific orientation, but jams and faults occur resulting in downtime and production losses
Solution Approach 1:
The system performs preliminary actions by using machine learning models to predict potential jams and faults before they occur. The system analyzes historical jam data, identifies patterns and risk factors, and takes preventive measures by adjusting bowl feeder parameters or alerting operators before actual jams happen, thus maintaining reliable operation while preserving individual part feeding capability
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring bowl feeder operation, comparing actual performance against predicted patterns, and automatically adjusting parameters or notifying operators when deviations indicate potential jams. This closed-loop feedback system prevents jams before they occur while maintaining the bowl feeder's ability to feed parts individually with precise orientation
2Productivity
If inline feeders transport workpieces synchronously, then production flow is maintained, but asynchronous workpieces cause production losses
Solution Approach 1:
The system uses feedback from sensors and machine learning models to detect asynchronous workpieces in real-time. When workpiece synchronization deviations are detected, the system provides feedback to control systems to adjust feeder parameters or alert operators, ensuring continuous synchronous operation and preventing production losses while maintaining high productivity
Solution Approach 2:
The system replaces purely mechanical synchronous transport with an intelligent system that uses sensors, data processing, and machine learning algorithms to monitor and maintain workpiece synchronization. This substitution enables dynamic detection and correction of asynchronous conditions while preserving production flow efficiency
3Reliability
If bowl feeders are stopped to clear jams, then operational faults are resolved, but production downtime increases
Solution Approach 1:
The system takes preliminary action by predicting potential jams before they occur using machine learning models that analyze operational patterns and risk factors. By identifying and addressing potential issues before they manifest as actual jams, the system prevents stoppages and eliminates production downtime while maintaining the ability to clear faults when necessary
Solution Approach 2:
The system introduces an intermediary intelligence layer between the bowl feeder and operators. This intermediary uses machine learning models to predict jams, suggest preventive actions, and guide operators through fault clearance procedures, reducing the frequency and duration of stoppages while ensuring reliable fault resolution
Data Source
AI summary
Computer-implemented methods and systems for feeding workpieces to a manufacturing line are provided. An example method involves operating at least one processor to: receive, from at least one image device proximal to a bowl feeder, a sequence of images of workpieces within the bowl feeder; determine a flow velocity of the workpieces within the bowl feeder; generate bowl feeder control settings by applying the flow velocity to a predictive model; and automatically apply the bowl feeder control settings to the bowl feeder. Computer-implemented methods and systems for predicting anomalies in a manufacturing line are also provided. An example method involves operating at least one processor to: receive a sequence of images of workpieces in the manufacturing line; extract feature data from the sequence of images; apply the feature data to a predictive model to detect anomalies in the manufacturing line; and generate annotations to locate the anomalies within the images.