Machining Condition Search Using Predictive Tolerance Optimization
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Solution Overview
Problem
Current machining condition search methods for industrial machining processes, such as laser machining, are inefficient due to the vast number of possible parameter combinations, requiring extensive trial and error to achieve desired machining results, and may not account for variations in material properties, leading to suboptimal outcomes.
Innovation Solution
A machining condition search device that generates and evaluates machining conditions using a combination of sensors, machine learning, and predictive models to identify optimal conditions with high evaluation values and tolerance, reducing the need for extensive trial and error by predicting suitable conditions based on past results and accounting for material variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If all combinations of control parameters are tried to select an appropriate machining condition, then the desired machining result can be obtained, but it takes a huge amount of time and effort
Solution Approach 1:
The patent performs preliminary machining trials to collect data on the relationship between machining conditions and results before actual production. By pre-establishing this data relationship through experiments, the system can predict optimal conditions without exhaustive trials during actual machining operations.
Solution Approach 2:
The patent implements a feedback mechanism where machining results are evaluated and used to adjust and optimize machining conditions. The system continuously learns from past machining outcomes and uses this feedback to improve future machining conditions, reducing the need for exhaustive parameter testing.
2Stability of the object's composition
If the same machining conditions are set, then the machining process is consistent, but different machining results occur due to material variations
Solution Approach 1:
The patent adjusts machining parameters based on detected material variations. When material properties differ from standard specifications, the system automatically modifies machining conditions (such as cutting speed, feed rate, or depth of cut) to compensate for these variations and maintain consistent machining results.
Solution Approach 2:
The machining system performs self-adjustment by detecting material variations and automatically modifying machining conditions without external intervention. The system uses sensors to detect material properties and autonomously optimizes parameters to maintain result consistency.
3Loss of time
If a learning model is used to predict machining conditions, then the number of trials is reduced, but the prediction may not account for material variations leading to suboptimal outcomes
Solution Approach 1:
The learning model incorporates feedback from actual machining results and material detection data. By continuously learning from real-world outcomes and material variations, the model improves its prediction accuracy and adapts to different material conditions, overcoming the limitation of static prediction models.
Solution Approach 2:
The system performs preliminary detection of material properties before machining and uses this information to adjust predictions. By pre-assessing material variations and incorporating this data into the learning model, the system achieves more accurate predictions that account for specific material characteristics.
Data Source
AI summary
A machining condition search device according to the disclosure includes a machining condition generation unit, a practical machining command unit, a machining evaluation unit, a prediction unit, an optimum machining condition calculation unit. The machining condition generation unit generates a machining condition defined by one or more control parameters settable on a machining apparatus. The practical machining command unit causes the machining apparatus to perform machining based on a generated machining condition. The machining evaluation unit generates an evaluation value of performed machining, on the basis of information indicating a machining result of the performed machining. The prediction unit predicts an evaluation value corresponding to a machining condition under which machining is not performed, on the basis of the evaluation value and the machining condition corresponding to the evaluation value. The optimum machining condition calculation unit obtains an optimum machining condition on the basis of a prediction value predicted by the prediction unit and an evaluation value generated by the machining evaluation unit. The optimum machining condition is a machining condition under which an evaluation value is equal to or greater than a threshold and a tolerance is maximum.