Machining Condition Adjustment Using Type-Specific Learning Models
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Solution Overview
Problem
Existing machining condition adjustment systems fail to effectively tailor machining conditions and parameters for different types of machining operations in machine tools, leading to inefficiencies and variations in cycle time and precision.
Innovation Solution
A machining condition adjustment device and system that utilize a data acquisition unit, priority condition storage, preprocessing unit, and machine learning device to adjust machining conditions and parameters based on the specific type of machining, using learning models generated for each machining type to optimize settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single machine learning model is used for all machining types, then the system complexity is reduced, but the machining precision and efficiency cannot be optimized for specific machining types
Solution Approach 1:
The patent divides the machine learning system into multiple specialized models, each trained for a specific machining type (roughing, finishing, drilling, tapping). This segmentation allows each model to optimize for its specific machining type's requirements, improving precision while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent applies local quality by creating specialized learning models with parameters optimized for specific local conditions of each machining type. Each model learns the unique characteristics and requirements of its specific machining type, enabling precise optimization for local machining conditions rather than using a generic one-size-fits-all approach.
2Productivity
If machining conditions are optimized for high cycle time in roughing, then productivity increases, but machining precision deteriorates
Solution Approach 1:
The patent segments the machining process into distinct phases (roughing and finishing) with separate learning models for each. The roughing model optimizes for high cycle time and productivity with relaxed precision requirements, while the finishing model optimizes for high precision with acceptable cycle time, allowing each phase to be independently optimized without compromising the other.
Solution Approach 2:
The patent changes the optimization parameters and priority conditions for different machining types. For roughing, the system prioritizes cycle time reduction and material removal rate. For finishing, the system prioritizes surface quality and dimensional accuracy. This dynamic parameter adjustment allows the system to achieve high productivity in roughing while maintaining high precision in finishing.
3Ease of operation
If generic machining conditions are used for all machining types, then ease of operation increases, but machining efficiency and precision deteriorate
Solution Approach 1:
The patent implements self-service by enabling the system to automatically select the appropriate learning model and optimize machining conditions based on the detected machining type. The system autonomously adjusts parameters without requiring operator intervention to switch between different machining types, maintaining ease of operation while achieving high efficiency through specialized optimization for each machining type.
4Manufacturing precision
If multiple learning models are generated for each machining type, then machining precision for each type improves, but device complexity increases
Solution Approach 1:
The patent manages complexity through segmentation by organizing multiple learning models into a modular architecture where each model is independent and specialized. The system includes a model selection mechanism that automatically chooses the appropriate model based on machining type, making the complexity transparent and manageable rather than requiring manual configuration for each machining scenario.
Data Source
AI summary
A machining condition adjustment device includes a data acquisition unit that acquires at least one piece of data indicating a state of machining including a machining type in a machine tool, a priority condition storage unit that stores priority condition data in which the machining type is associated with a priority condition, a preprocessing unit that produces data to be used for machine learning, and a machine learning device that carries out processing of the machine learning related to at least either of a machining condition and a machining parameter for machining by the machine tool. The machine learning device includes a learning model storage unit that stores a plurality of learning models generated for each machining type and a learning model selection unit that selects a learning model, based on the machining type.