18260342. MACHINE LEARNING DEVICE simplified abstract (FANUC CORPORATION)
Contents
- 1 MACHINE LEARNING DEVICE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MACHINE LEARNING DEVICE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Machine Learning Device for Optimizing Oscillation Cutting Conditions
- 1.13 Original Abstract Submitted
MACHINE LEARNING DEVICE
Organization Name
Inventor(s)
Yuutarou Horikawa of Yamanashi (JP)
Masashi Yasuda of Yamanashi (JP)
MACHINE LEARNING DEVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18260342 titled 'MACHINE LEARNING DEVICE
Simplified Explanation
The machine learning device described in the patent application can control the idling of a tool and determine optimal oscillation conditions for producing high-quality workpieces.
- Machine learning device for optimizing oscillation cutting conditions
- Acquires set conditions for cutting and evaluation data of finished workpieces
- Uses supervised learning to train oscillation conditions that improve workpiece quality
Key Features and Innovation
- Machine learning device for optimizing oscillation cutting conditions
- Acquires set conditions and evaluation data to train oscillation conditions
- Supervised learning to improve workpiece quality
Potential Applications
This technology can be applied in industries that require precision cutting and high-quality workpiece finishing, such as manufacturing, aerospace, and automotive.
Problems Solved
This technology addresses the challenge of determining optimal oscillation conditions for cutting processes to achieve favorable evaluation data of finished workpieces.
Benefits
- Improved workpiece quality
- Enhanced precision in cutting processes
- Increased efficiency in manufacturing operations
Commercial Applications
Optimizing oscillation cutting conditions can lead to higher-quality products, reduced waste, and improved overall efficiency in manufacturing processes.
Prior Art
Researchers interested in this technology may explore prior studies on machine learning in manufacturing processes, optimization of cutting conditions, and quality control in machining operations.
Frequently Updated Research
Stay updated on advancements in machine learning algorithms for optimizing cutting processes, new techniques for improving workpiece quality, and applications of supervised learning in manufacturing.
Questions about Machine Learning Device for Optimizing Oscillation Cutting Conditions
How does the machine learning device acquire set conditions for oscillation cutting?
The machine learning device acquires set conditions for oscillation cutting through a set conditions acquisition unit.
What is the main benefit of using supervised learning in training oscillation conditions?
Supervised learning helps improve workpiece quality by optimizing oscillation conditions based on evaluation data of finished workpieces.
Original Abstract Submitted
Provided is a machine learning device capable of causing idling of a tool, and of calculating oscillation conditions that realize favorable evaluation data of finished workpieces. The machine learning device that learns oscillation conditions of a machine tool that performs oscillation cutting while oscillating a tool and a workpiece relative to each other is provided with a set conditions acquisition unit that acquires set conditions for the oscillation cutting, a label acquisition unit that acquires evaluation data of finished workpieces by the machine tool as a label, and a learning unit that performs supervised learning using a set of the set conditions and the label as training data, the learning unit being provided with a learning model for training oscillation conditions that optimize the evaluation data of the finished workpieces.
