Machining Failure Detection Using Light and Sound Features
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Solution Overview
Problem
Current machining failure detection techniques in laser cutting and electric discharge machining struggle to accurately detect failures in real-time under varying machining conditions, such as changes in material or workpiece thickness, due to the lack of consideration for feature variations, leading to inaccurate proper value calculations and reduced detection accuracy.
Innovation Solution
A machining failure detection device comprising a machining light measurement unit, a machining sound measurement unit, and a computation unit that extracts features from these signals to calculate a combined failure determination value, allowing for real-time detection of machining failures by comparing the value against a determination criterion, which takes into account varying machining conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single sensor (light or sound) is used for machining failure detection, then the device complexity is reduced, but the detection accuracy deteriorates under varying machining conditions
Solution Approach 1:
The patent combines multiple sensors (light sensor and sound sensor) to form a composite sensing system. The light sensor detects machining light intensity while the sound sensor detects machining sound characteristics. By merging these different sensing modalities, the system achieves more accurate and reliable failure detection under varying machining conditions compared to using a single sensor type.
2Measurement precision
If the proper value is calculated based on values from successful machining only, then the calculation process is simplified, but the detection accuracy deteriorates when machining conditions change
Solution Approach 1:
The patent implements dynamic adjustment of the proper value based on detected machining conditions. Instead of using a fixed proper value calculated from successful machining data, the system dynamically modifies the proper value according to actual conditions such as material type, workpiece thickness, and machining parameters. This allows the detection system to maintain high accuracy when machining conditions change.
Solution Approach 2:
The system changes the parameters used for failure determination based on detected machining conditions. When conditions such as material type or workpiece thickness change, the system adjusts the proper values and determination thresholds accordingly. This parameter adaptation ensures that the failure detection remains accurate across different machining scenarios.
3Productivity
If machining failure detection is performed in real-time during machining, then productivity is improved by enabling timely intervention, but the measurement precision requirements increase
Solution Approach 1:
The patent implements a feedback mechanism where the machining failure determination unit continuously monitors machining light and sound signals during the machining process. When a failure is detected, the system provides feedback that can trigger alarms or automatic adjustments. This real-time feedback enables timely intervention while maintaining high measurement precision through continuous monitoring and comparison against dynamic proper values.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution improves the accuracy of machining failure detection under varying conditions by using a combination of machining light and sound features, enabling timely intervention during the machining process and enhancing the reliability of cut object quality assessment in laser cutting and electric discharge machining.
Implementation Method 1
a machining light measurement unit to measure machining light generated at a machining point during machining
Implementation Method 2
a machining sound measurement unit to measure machining sound generated at the machining point
Data Source
AI summary
A machining failure detection device includes a machining light measurement unit that measures machining light generated at a machining point during machining; a machining sound measurement unit that measures machining sound generated at the machining point; and a computation unit that determines whether a machining failure has occurred in the machining. The computation unit includes a feature extraction unit, a determination value calculation unit, and a determination unit. The feature extraction unit extracts a machining light feature from a machining light signal measured by the machining light measurement unit, and extracts a machining sound feature from a machining sound signal measured by the machining sound measurement unit. The determination value calculation unit calculates a combined failure determination value on the basis of the machining light feature and the machining sound feature. The determination unit compares the combined failure determination value with a determination criterion to determine a failure.