Template-Referenced Machining with ML Coordinate Deviation Screening
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Solution Overview
Problem
Machine learning techniques have not been fully applied in the context of high-accuracy manufacturing processes, such as high-accuracy drilling, particularly on an industrial scale, to provide predictive capability for machining accuracy.
Innovation Solution
A machining system incorporating an automated manipulator with a position probe and tool configuration, utilizing a machine learning agent to estimate machining coordinate deviation, allowing or halting the machining process based on a threshold deviation, and optionally providing alerts or recalibration suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If machine learning techniques are applied to predict machining accuracy, then scrappage is reduced and manufacturing precision is improved, but device complexity increases
Solution Approach 1:
A machine learning agent is introduced as an intermediary component between the automated manipulator and the machining process. The agent receives coordinate data from the position probe and predicts machining accuracy, enabling informed decisions about whether to proceed with machining. This intermediary layer adds predictive capability without fundamentally redesigning the core machining system.
Solution Approach 2:
The machine learning agent performs preliminary prediction of machining accuracy before the actual machining operation commences. By analyzing coordinate data and predicting potential deviations, the system can determine in advance whether the workpiece should be machined, preventing unnecessary machining operations and reducing scrappage before resources are committed.
2Measurement precision
If coordinate data is collected and analyzed using machine learning, then prediction accuracy is improved, but loss of time increases
Solution Approach 1:
The system collects coordinate data from reference features on the template, which is a subset of the complete workpiece geometry. This partial measurement approach provides sufficient information for the machine learning agent to predict machining accuracy without requiring exhaustive measurement of all workpiece features, thereby balancing prediction accuracy with measurement time.
Solution Approach 2:
The machine learning agent replaces traditional mechanical measurement and analysis methods with computational prediction. Instead of physically testing or measuring the workpiece after machining to determine accuracy, the system uses algorithms to predict outcomes from coordinate data, significantly reducing the time required for accuracy assessment.
Data Source
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AI summary
A machining system (100) comprises: an automated manipulator (102) configurable between a measuring configuration, in which a position probe (114) is operable, and a machining configuration, in which a tool (120) is operable; a fixture (106) for holding a template (104) or a workpiece (108); and a controller (116). The controller (116) is configured cause the automated manipulator (102), in the measuring configuration, to move the position probe (114) to at least one reference feature (124, 126) of the template held in the fixture (106), to determine coordinate data associated with the at least one reference feature (124. 126). Then, the controller (116) provides the determined coordinate data to a machine learning agent which is trained to provide an estimate of machining coordinate deviation based on the determined coordinate data. If the estimated machining coordinate deviation is below a threshold coordinate deviation, the automated manipulator is allowed to proceed to machine a workpiece (108).