Probe-Guided Machining Control With ML Deviation Prediction
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Solution Overview
Problem
Machine learning techniques have not been fully applied in 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 comprising an automated manipulator configurable between a measuring and machining configuration, utilizing a machine learning agent to estimate machining coordinate deviation based on determined coordinate data, and a controller to decide whether machining can proceed based on a threshold deviation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If machine learning techniques are applied to predict machining accuracy, then scrappage is reduced and machining 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 control system. This agent receives coordinate data from the position probe and predicts machining coordinate deviations, enabling accuracy improvement without requiring direct modification of the core machining hardware, thus managing complexity through software-based mediation.
Solution Approach 2:
The machine learning agent performs preliminary prediction of machining coordinate deviations before the actual machining operation commences. By analyzing coordinate data from the measuring configuration and predicting potential deviations, the system can preemptively adjust parameters or alert operators, preventing accuracy issues before they manifest in the finished workpiece.
2Manufacturing precision
If coordinate data is measured and analyzed before machining, then machining precision is improved, but loss of time increases
Solution Approach 1:
Coordinate data measurement and machine learning analysis are performed in advance during the measuring configuration phase, before the workpiece is committed to machining. This preliminary action establishes baseline accuracy predictions that guide subsequent machining operations, ensuring precision without delaying the actual production timeline.
Solution Approach 2:
Traditional extensive physical measurement and trial machining processes are replaced with a machine learning-based prediction system. The agent rapidly analyzes coordinate data and predicts deviations using computational models, substituting time-consuming mechanical measurement and iterative testing with faster information-based decision making.
3Manufacturing precision
If threshold coordinate deviation is enforced to reduce scrappage, then manufacturing precision is improved, but productivity decreases
Solution Approach 1:
The machine learning agent performs preliminary assessment of coordinate deviations before machining begins. By predicting accuracy outcomes in advance and comparing them against threshold criteria, the system can approve or reject machining operations proactively, preventing scrappage without requiring post-processing inspection or rework, thus maintaining productivity.
Solution Approach 2:
The system implements feedback through the machine learning agent that continuously monitors coordinate data and predicts deviations. This real-time feedback mechanism enables dynamic decision-making about whether to proceed with machining, allowing the system to maintain strict accuracy thresholds while optimizing throughput by only rejecting operations with predicted failures.
Data Source
AI summary
A machining system includes an automated manipulator configurable between a measuring configuration, in which a position probe is operable, and a machining configuration, in which a tool is operable, a fixture for holding a template or a workpiece, and a controller. The controller is configured cause the automated manipulator, in the measuring configuration, to move the position probe to at least one reference feature of the template held in the fixture, to determine coordinate data associated with the at least one reference feature. Then, the controller 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.