IP Library Granted Patent US 11,951,562
Granted Patent B2
US 11,951,562 · App. 16/283,130 · Granted Apr 9, 2024

Laser processing system

Inventors: Yohei Kobayashi (Tokyo, JP); Shuntaro Tani (Tokyo, JP); Yutsuki Aoyagi (Tokyo, JP)
Assignee: THE UNIVERSITY OF TOKYO
B23K26/032B23K26/0624B23K26/064B23K26/36B23K26/362B23K26/705G06N3/08
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Quick Facts
Patent No.
US 11,951,562
App. No.
16/283,130
Granted
Apr 9, 2024
Kind
B2
Abstract

A laser processing system is equipped with a processing laser beam irradiation device configured to irradiate a processing object with processing laser beam and perform ablation processing. The laser processing system is configured to obtain an ablation image of a processed portion of the processing object based on scattered light from the processed portion during processing of the processing object with the processing laser beam and to estimate an ablation volume by applying a learning result obtained by deep learning of a relationship between the ablation image and the ablation volume to the obtained ablation image.

Claims (18)

1. A laser processing system equipped with a processing laser beam irradiation device configured to irradiate a processing object with a processing laser beam and perform ablation processing, the laser processing system comprising:

a camera configured to obtain an ablation image of a processed portion of the processing object based on scattered light from the processed portion during processing of the processing object with the processing laser beam; and

a processor programmed to estimate an ablation volume by inputting the ablation image into a deep learning model that specifies a relationship between the ablation image and the ablation volume and outputs the estimated ablation volume,

wherein the deep learning model has been trained by outputting a processing parameter based on the input ablation image, and applying a loss function to the deep learning model based on actual values of the processing parameter, and the deep learning model estimates the ablation volume based on the processing parameter.

2. The laser processing system according to claim 1 ,

wherein the deep learning is learning that uses a plurality of consecutive ablation images as input data in laser processing.

3. The laser processing system according to claim 1 ,

wherein the processing parameter includes a parameter relating to a total energy of the processing laser beam which the processed portion is irradiated with since a start of the processing.

4. The laser processing system according to claim 1 , further comprising:

an imaging laser beam irradiation device configured to irradiate the processed portion with imaging laser beam that has a wavelength different from a wavelength of the processing laser beam, so as to obtain the ablation image.

5. The laser processing system according to claim 1 ,

wherein the camera obtains the ablation image at a plurality of predetermined time intervals.

6. The laser processing system according to claim 5 ,

wherein the processing laser beam irradiation device is configured to output a pulse laser beam having a pulse width of femtosecond order as the processing laser beam and perform ablation processing,

the camera is configured to obtain the ablation image at every time of radiation of each pulse laser beam or a plurality of pulse laser beams, and

the processor is programmed to estimate the ablation volume prior to radiation of next processing laser beam by the processing laser beam irradiation device.

7. The laser processing system according to claim 1 , wherein the processing parameter is one of a plurality of processing parameters, and

the plurality of processing parameters comprise (1) a fluence value, and (2) a number of irradiation pulses applied by the processing laser beam irradiation device.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 10, 2019
From: KOBAYASHI, YOHEI; TANI, SHUNTARO; AOYAGI, YUTSUKI
To: THE UNIVERSITY OF TOKYO
Reel/Frame 049149/0294 →
Priority Claims (1)
JP 2018-030874 · Feb 23, 2018 · national
Continuity (1)
Related Publication 20190262936A1 · Aug 29, 2019