Machine Tool Control Using Workpiece Accuracy Feedback
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Solution Overview
Problem
Conventional machine tool control techniques do not consider the quality of the manufactured workpiece, leading to inconsistent results.
Innovation Solution
A machine learning-based control method that generates command data by training models using state data from the machine tool and accuracy data of the workpiece, incorporating multiple linear regression and neural network models to determine optimal machining parameters for consistent quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional machine tool control techniques are used, then the control system is simple and easy to operate, but the quality consistency of manufactured workpieces deteriorates
Solution Approach 1:
The control system implements feedback by collecting state data during machining operations and accuracy data from measured workpieces, then using this feedback to train and update machine learning models that generate command data for the machine tool, creating a closed-loop system that continuously improves quality consistency
Solution Approach 2:
The patent replaces conventional mechanical control systems with a machine learning-based control system that uses neural networks and regression models to process state data and generate optimal command data, substituting traditional control mechanisms with intelligent algorithms that learn from data
2Manufacturing precision
If machine learning models are introduced to improve quality consistency, then manufacturing precision improves, but device complexity increases
Solution Approach 1:
The system performs preliminary action by collecting and storing state data and accuracy data before actual machining operations, training machine learning models in advance with this accumulated data so that when machining occurs, the models are already prepared to generate optimal command data without real-time computation delays
Solution Approach 2:
The control system is made dynamic through the machine learning models that continuously learn and adapt from new data, allowing the system to evolve and improve its control strategies over time rather than relying on static control parameters
3Productivity
If adaptive control is used to adjust feed speed based on load torque, then machining efficiency improves, but quality consistency deteriorates
Solution Approach 1:
The system changes multiple machining parameters simultaneously based on state data, not just feed speed. The machine learning model processes comprehensive state data including machining conditions, machine tool status, and workpiece properties to dynamically adjust multiple parameters together, achieving both efficiency and quality consistency
Data Source
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AI summary
A machine tool control technique that takes into consideration the quality of the manufactured workpiece is provided. An embodiment of the present disclosure relates to a control system comprising a state observation unit configured to collect state data relating to a machine tool, the state data including one or more of machining state data, equipment state data, and material state data, an accuracy result observation unit configured to acquire accuracy data of a workpiece made by the machine tool, a learning unit configured to train a machine learning model by using the collected state data and the acquired accuracy data, and a command determination unit configured to, when machining the workpiece a same portion of which is machined a plurality of times, determine command data, through the trained machine learning model, with respect to the machine tool from the state data collected during machining of the workpiece at a certain point in time before a final process among a plurality of times of processes performed by the machine tool, and cause the machine tool to perform the final process with respect to the workpiece by using the command data.