Laser Processing State Estimation Across Different Sensing Devices
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Solution Overview
Problem
Existing laser processing state estimation models generated by one device are difficult to accurately apply to another device due to machine differences in light detection, leading to variations in sensing data even in similar processing states, and are challenging to adapt to non-standard production line conditions.
Innovation Solution
The proposed solution involves generating an estimation model using waveform data from a first device and converting waveform data from a second device using calculated coefficients to match the first device's data, allowing the same estimation model to be used across different devices, thereby maintaining the ratio of average signal intensities and improving estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an estimation model is generated using waveform data from one laser processing device, then the model can be created with device-specific characteristics, but the model cannot be accurately applied to other devices due to machine differences in light detection
Solution Approach 1:
The patent applies parameter changes by converting waveform data parameters between different devices. Specifically, it transforms the detection waveform parameters (intensity, timing) from a second device to match the parameter characteristics of a first device, enabling the estimation model trained on first device data to accurately process second device data through parameter transformation
Solution Approach 2:
The patent introduces waveform data conversion as an intermediary process between different laser processing devices and the estimation model. The conversion unit acts as a mediator that transforms second device waveform data into first device equivalent data, allowing the model to universally process data from multiple devices without direct retraining
2Adaptability or versatility
If waveform data is converted using calculated coefficients to match another device's data, then the same estimation model can be used across different devices, but additional processing steps are required
Solution Approach 1:
The patent applies preliminary action by pre-calculating conversion coefficients between different devices' waveform characteristics. These coefficients are determined in advance through comparison of device characteristics, and stored for later use, eliminating the need for complex real-time transformations during actual estimation processes
Solution Approach 2:
The patent simplifies the conversion process by transforming complex multi-dimensional waveform data differences into simple scalar conversion coefficients. This parameter reduction approach changes the complexity from handling full waveform transformations to applying straightforward multiplicative factors, significantly reducing processing complexity
Data Source
AI summary
An estimation model generation device is a device that generates an estimation model for estimating a processing state of laser processing. First thermal radiation, first visible light, first reflected light, and first laser light are observed from a workpiece during laser processing by a first device, and the device includes an information acquiring section that acquires first waveform data including a first waveform and a second waveform for at least two of the first thermal radiation, the first visible light, the first reflected light, and the first laser light, an estimation model generating section that performs machine learning by using teacher data having the first waveform data as an explanatory variable and the processing state as a target variable in association with each other to generate a first estimation model for estimating the processing state by the first device, and a storage that stores the first estimation model.