Cutting Fluid State Prediction for Condition-Based Maintenance
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Solution Overview
Problem
The contamination of cutting fluids in machine tools is challenging to predict, leading to inefficient maintenance and replacement timing, which requires operator experience and can result in decreased machining quality.
Innovation Solution
A machine learning device and prediction system that acquires machining conditions and cutting fluid state data, generates a learned model through supervised learning, and predicts the cutting fluid's state after machining, enabling timely maintenance or replacement decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If cutting fluid is replaced entirely at predetermined timing, then machining quality is maintained, but operational time is wasted and cost increases
Solution Approach 1:
The patent transitions from fixed-time replacement to condition-based replacement by monitoring parameters such as chip concentration, temperature, and pressure in the cutting fluid. This allows the system to determine replacement timing based on actual contamination levels rather than predetermined schedules, optimizing both machining quality and operational efficiency
Solution Approach 2:
The system implements feedback mechanisms using sensors to continuously monitor cutting fluid conditions and provide real-time information about contamination levels. This feedback loop enables dynamic adjustment of replacement timing, preventing both premature replacement and operation with degraded fluid
2Reliability
If operator experience is used to determine maintenance timing, then cutting fluid state can be assessed, but operator burden increases
Solution Approach 1:
The system enables self-service by automatically monitoring and assessing cutting fluid conditions through sensors and processing units. The system independently determines maintenance needs without relying on operator inspection, thereby maintaining reliable assessment while eliminating operator burden
Solution Approach 2:
The patent replaces manual operator assessment with automated sensing and processing systems. Sensors detect physical and chemical parameters of the cutting fluid, and processing units analyze this data to determine maintenance timing, substituting human judgment with automated systems
3Manufacturing precision
If filter is used to remove chips from cutting fluid, then cutting fluid cleanliness is improved, but complete removal of impurities is not achieved
Solution Approach 1:
The system monitors multiple parameters including chip concentration, temperature, and pressure to comprehensively assess cutting fluid condition. By tracking these parameters over time, the system can detect when impurity levels affect machining quality, providing a more reliable indication than simple filtration status
Data Source
AI summary
The state of a cutting fluid after machining is predicted. A machine learning device includes: an input data acquisition unit that acquires input data including arbitrary machining conditions for an arbitrary work in machining by an arbitrary machine tool and state information indicating a state of a cutting fluid before machining is performed under the machining conditions; a label acquisition unit that acquires label data indicating state information of the cutting fluid after the machining is performed under the machining conditions included in the input data; and a learning unit that executes supervised learning using the input data acquired by the input data acquisition unit and the label data acquired by the label acquisition unit to generate a learned model.