Machine Tool Work Area Cleaning Detection by Brightness Histogram
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Solution Overview
Problem
Current systems for determining whether to clean the work area of a machine tool lack accuracy in assessing the need for cleaning, leading to inefficiencies and potential damage from foreign matter accumulation.
Innovation Solution
A device and method utilizing an imaging system to capture and compare image data before and after machining, generating data on brightness changes, and using a histogram to statistically determine the necessity for cleaning, with a cleaning nozzle for fluid injection when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional cleaning determination systems are used, then the cleaning process can be performed, but the accuracy in determining whether cleaning is necessary is insufficient
Solution Approach 1:
The patent replaces conventional mechanical or simple sensor-based cleaning determination systems with an optical imaging system. The imaging device captures images of the work area, and image processing algorithms analyze brightness changes to determine cleaning necessity, achieving higher accuracy in detecting foreign matter and surface contamination.
Solution Approach 2:
The patent introduces brightness change analysis as an intermediary mechanism between the work area state and the cleaning determination. By comparing brightness values before and after machining, the system creates a quantitative measure of contamination that improves both measurement precision and reliability of cleaning necessity determination.
2Reliability
If cleaning is performed frequently to ensure work area cleanliness, then foreign matter accumulation is prevented, but machine tool operation efficiency decreases due to unnecessary cleaning
Solution Approach 1:
The patent implements a feedback mechanism where the imaging device continuously monitors the work area, and the cleaning determination unit provides feedback on whether cleaning is actually necessary. This closed-loop system adjusts cleaning operations based on real-time conditions, preventing unnecessary cleaning and maintaining productivity while ensuring cleanliness when needed.
Solution Approach 2:
The system enables the work area to essentially monitor itself for contamination through the imaging device. The automatic analysis of brightness changes allows the system to self-determine when cleaning is necessary, eliminating the need for frequent scheduled cleaning operations and improving operational efficiency.
3Productivity
If cleaning is delayed to maintain productivity, then machine tool operation efficiency is improved, but foreign matter accumulation occurs causing potential damage
Solution Approach 1:
The continuous monitoring and feedback mechanism detects foreign matter accumulation in real-time, providing early warning before contamination reaches harmful levels. This allows scheduling of cleaning operations at optimal moments that minimize impact on productivity while preventing damage from excessive accumulation.
Solution Approach 2:
The system performs preliminary detection of contamination through brightness analysis, identifying the need for cleaning before foreign matter accumulation becomes problematic. This preliminary action enables proactive scheduling of cleaning operations that prevent damage while minimizing disruption to productive operations.
Data Source
AI summary
A device capable of determining whether or not to clean a work area of a machine tool with higher accuracy. The device includes an imaging device configured to capture first image data of the work area before machining, and configured to capture second image data of the work area after machining, an image data generation section configured to generate third image data indicating a degree of change between brightness of a pixel of the first image data and brightness of a pixel of the second image data, and a determination section configured to determine whether or not to clean the work area based on a histogram indicating a relationship between the brightness of the pixel of the third image data and the number of pixels of the third image data.