Bowl Feeder Image Control for Jam-Free Part Flow
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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 conditions, 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 to manufacturing lines, then parts can be fed one-by-one with particular orientation, but jams and faults occur requiring stoppage to clear
Solution Approach 1:
The system performs preliminary detection of part orientation and position using image capture before parts are fully fed into the manufacturing line. By identifying potential jams or misoriented parts in advance, the system can take corrective action before the feeder stops, maintaining continuous operation while ensuring proper part orientation.
Solution Approach 2:
The system implements continuous feedback through image capture devices that monitor part orientation and feeder operation in real-time. This feedback loop enables the system to detect anomalies such as misoriented parts or potential jams and automatically adjust feeder operation to prevent stoppages, thereby maintaining both feeding capability and operational reliability.
2Adaptability or versatility
If workpieces are transported asynchronously on inline feeders, then flexibility is increased, but subsequent production stations are inhibited or delayed
Solution Approach 1:
The system dynamically adjusts the operation of inline feeders based on real-time detection of workpiece orientation and position. By selectively controlling which parts are fed and when, the system maintains asynchronous transport flexibility while ensuring that only properly oriented parts reach production stations, thereby preventing delays and maintaining productivity.
Solution Approach 2:
The system performs preliminary detection and sorting of workpieces before they reach production stations. By identifying and correcting orientation issues in advance during the asynchronous transport phase, the system ensures that parts arrive at production stations in the correct orientation, maintaining both transport flexibility and production flow without inhibition.
3Adaptability or versatility
If workpieces are not similar on inline feeders, then variety is handled, but missing parts result and acceptable products cannot be produced
Solution Approach 1:
The system implements continuous feedback through image capture and analysis that monitors each workpiece for required features and orientation. This feedback enables real-time identification of workpieces with missing parts or incorrect features, allowing the system to selectively feed only acceptable workpieces to production stations, thereby maintaining both variety handling capability and manufacturing precision.
Solution Approach 2:
The system performs preliminary inspection of workpiece features and orientation before feeding decisions are made. By detecting missing parts or incorrect features in advance during the transport phase, the system can prevent defective workpieces from reaching production stations, ensuring that only complete, properly oriented parts are processed while still handling diverse workpiece varieties.
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.