Machine Tool Status Diagnosis Using Idle-Interval Sensor Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current machine tool diagnosis methods are inefficient due to the long time and high costs required for data collection and algorithm development, and are limited by noise and varied machining patterns, resulting in poor diagnostic accuracy.
Innovation Solution
Mounting sensors on specific machine tool elements and collecting data during predefined operating conditions, such as idling the spindle at a specific speed, between machining operations to perform status or failure diagnosis using machine learning or AI algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is collected during machining operations to perform diagnosis, then diagnostic coverage is improved, but noise increases and measurement precision deteriorates
Solution Approach 1:
The patent extracts the diagnostic data collection process from the machining operation itself by utilizing the idle time between operations. Instead of collecting data during noisy machining activities, the system collects sensor data during idle periods when the machine tool is not performing cutting operations, thereby separating the diagnostic function from the productive function and eliminating noise contamination.
Solution Approach 2:
The patent introduces an intermediary time period (idle time between machining operations) as a mediator for data collection. This intermediary period serves as a buffer that allows diagnostic data to be collected without interfering with productive machining operations and without being contaminated by machining-related noise.
2Quantity of substance
If data is collected during machining operations, then operational data availability is improved, but data collection time increases
Solution Approach 1:
The patent implements periodic action by collecting data during regular idle periods between machining operations. Instead of continuous monitoring during all operations, the system utilizes the periodic idle time that naturally occurs in manufacturing workflows, transforming waste time into productive diagnostic data collection opportunities.
Solution Approach 2:
The system performs self-service by utilizing its own idle time for diagnostic data collection. The machine tool serves its dual purpose of both productive machining and self-diagnosis by collecting sensor data during its own idle periods, eliminating the need for separate dedicated testing time.
3Measurement precision
If sensors are mounted on machine tool elements, then measurement capability is improved, but device complexity increases
Solution Approach 1:
The patent applies universality by designing the sensor system to serve multiple functions: both monitoring machining operations and performing diagnostic assessments during idle periods. The same sensors mounted on machine tool elements are used for both productive monitoring and diagnostic purposes, eliminating the need for separate diagnostic hardware.
Solution Approach 2:
The machine tool system performs self-diagnosis using its own built-in sensors and control system. The control unit utilizes the machine's existing sensor infrastructure to collect diagnostic data and perform status assessment, eliminating the need for external diagnostic equipment and reducing overall system complexity.
Data Source
AI summary
In order to increase the accuracy of status or failure diagnosis of machine tools and reduce the time and costs required for data collection and algorithm development for diagnosis, sensors are mounted on specific elements of the machine tools and measurement data is collected while operating under the specific operating conditions during a time interval between one machining operation and another machining operation to perform status or failure diagnosis of the machine tools. According to the present invention, by using data collected while operating machine tools under the predefined specific conditions using a time interval between one machining operation and another machining operation, it is possible to more accurately perform diagnosis while allowing less noise to be mixed in the data and reducing the time required for data collection for the development of diagnostic algorithm.