Sensor Signal Evaluation for Reliable In-Process Object Detection
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Solution Overview
Problem
Existing sensor devices in machine tools face challenges in reliably detecting target objects due to incorrect threshold settings, leading to potential machine damage or unnecessary process interruptions, as they struggle to differentiate between target objects and disturbance variables like foreign bodies or dust.
Innovation Solution
A machine tool equipped with a sensor device that includes a sensor unit and an evaluation device, where the evaluation device processes both sensor signals and process parameters to generate output signals, allowing for more accurate detection of target objects by considering the influence of process parameters on the target object's movement and properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If threshold values are set to be sensitive for detecting target objects, then detection sensitivity is improved, but false detections of disturbances increase
Solution Approach 1:
The patent applies dynamics by making the threshold values adjustable and adaptable rather than fixed. The evaluation device dynamically adjusts threshold values based on process parameters and learned disturbance patterns, allowing the system to optimize detection sensitivity while minimizing false alarms for different operating conditions
Solution Approach 2:
The patent changes parameters by introducing multiple adjustable threshold levels and adapting them based on process conditions. The system modifies threshold values according to process parameters and learned disturbance characteristics, enabling flexible optimization between detection sensitivity and false detection reduction
2Reliability
If threshold values are set to be robust against disturbances, then false detections are reduced, but detection accuracy for target objects decreases
Solution Approach 1:
The system dynamically adapts threshold values based on process conditions and learned disturbance patterns, allowing it to maintain high reliability while preserving detection accuracy through context-aware threshold adjustment
Solution Approach 2:
The evaluation device uses feedback from process parameters and learned disturbance patterns to continuously optimize threshold values, ensuring both robustness against false detections and maintained detection accuracy for target objects
3Measurement precision
If threshold values are redefined for each application, then detection accuracy is improved, but device complexity and setup time increase
Solution Approach 1:
The system applies self-service by automatically learning disturbance patterns and adapting threshold values without requiring manual reconfiguration for each application. The evaluation device performs self-optimization based on process parameters and observed disturbances, reducing setup complexity while maintaining high detection accuracy
Solution Approach 2:
The patent implements universality by creating a flexible threshold adaptation mechanism that can handle multiple applications and disturbance types through a single unified system. The evaluation device universally applies learning-based threshold optimization across different operating conditions without requiring application-specific configuration
4Productivity
If sensor devices monitor material flow continuously, then productivity is maintained, but false interruptions due to incorrect detection increase
Solution Approach 1:
The system uses feedback from process parameters and learned disturbance patterns to continuously optimize detection reliability, enabling continuous monitoring without false interruptions while maintaining high detection accuracy for actual target objects
Solution Approach 2:
The evaluation device performs self-optimization by automatically adapting to disturbance patterns, allowing continuous production monitoring without manual intervention while minimizing false interruptions through improved detection reliability
Data Source
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AI summary
A sensor device for detecting a target object which is influenced by a process or is formed during the process comprises a sensor unit and an analysis device. The sensor unit is designed to detect the target object in a detection region of the sensor unit and to generate a sensor signal which can be influenced by the target object. The analysis device is designed to process the sensor signal as a first input variable and to generate an output signal on the basis of the sensor signal, said output signal indicating the detection of the target object. Furthermore, the analysis device is designed to process a process parameter acting on the target object and/or a target object parameter, which characterizes the target object and is influenced by the process, as a respective additional input variable and to generate the output signal on the basis of the process parameter and/or the target object parameter.