IP Library Granted Patent US 12705496
Granted Patent B2
US 12705496 · App. 18/374,461 · Granted Aug 11, 2026

Method and apparatus for automatically generating welding procedure specification using machine learning algorithms

Inventor: Sung Won Huh (Seoul, KR)
Assignee: ONJ INC.
G06N3/09
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Quick Facts
Patent No.
US 12705496
App. No.
18/374,461
Granted
Aug 11, 2026
Kind
B2
Abstract

Provided are a method and apparatus for automatically generating a welding procedure specification (WPS) by using a machine learning algorithm. The method, performed by a processor of a WPS generating apparatus, of automatically generating a WPS by using a machine learning algorithm, includes collecting a WPS transmission request signal together with welding-related information including a welding material for a welding target and a thickness of the welding material, generating a WPS corresponding to the welding-related information by using a machine learning model pre-trained to generate the WPS by using the welding-related information, and transmitting a WPS response signal together with the generated WPS, in response to the WPS transmission request signal, wherein the machine learning model is a model trained through supervised learning using training data in which the welding-related information is an input and the WPS corresponding to the welding-related information is a label.

Claims (39)

1 . A method, performed by a processor of a welding procedure specification (WPS) generating apparatus, of automatically generating a WPS by using a machine learning algorithm, the method comprising:

collecting a WPS transmission request signal together with welding-related information comprising a welding material for a welding target and a thickness of the welding material;

generating a WPS corresponding to the welding-related information by using a machine learning model pre-trained to generate the WPS by using the welding-related information; and

transmitting a WPS response signal together with the generated WPS, in response to the WPS transmission request signal,

wherein the machine learning model is a model trained through supervised learning using training data in which the welding-related information is an input and the WPS corresponding to the welding-related information is a label,

wherein the WPS comprises a preliminary WPS before completion, and

the method further comprises, before the transmitting of the WPS response signal, transmitting the preliminary WPS to one or more of a supervisor terminal possessed by a first supervisor, a supervisor terminal possessed by a second supervisor, and a welding device, and

where the method further comprises:

receiving welding result monitoring information for the welding device, generated based on the preliminary WPS, from the supervisor terminal possessed by the first supervisor;

receiving inspection result information for the preliminary WPS, generated based on the welding result monitoring information, from the supervisor terminal possessed by the second supervisor; and

determining approval or disapproval of the preliminary WPS as a completed WPS, based on the inspection result information for the preliminary WPS.

2 . The method of claim 1 , wherein the collecting of the WPS transmission request signal comprises collecting the WPS transmission request signal together with the welding-related information comprising one or more of text, a speech, and an image.

3 . The method of claim 2 , wherein the collecting of the WPS transmission request signal comprises collecting the WPS transmission request signal together with the welding-related information from one or more of a welding machine, a welding protective equipment, and a user terminal.

4 . A non-transitory computer-readable recording medium having stored therein a computer program for executing, by using a computer, a method comprising:

collecting a WPS transmission request signal together with welding-related information comprising a welding material for a welding target and a thickness of the welding material;

generating a WPS corresponding to the welding-related information by using a machine learning model pre-trained to generate the WPS by using the welding-related information; and

transmitting a WPS response signal together with the generated WPS, in response to the WPS transmission request signal,

wherein the machine learning model is a model trained through supervised learning using training data in which the welding-related information is an input and the WPS corresponding to the welding-related information is a label,

wherein the WPS comprises a preliminary WPS before completion, and

the method further comprises, before the transmitting of the WPS response signal, transmitting the preliminary WPS to one or more of a supervisor terminal possessed by a first supervisor, a supervisor terminal possessed by a second supervisor, and a welding device, and

where the method further comprises:

receiving welding result monitoring information for the welding device, generated based on the preliminary WPS, from the supervisor terminal possessed by the first supervisor;

receiving inspection result information for the preliminary WPS, generated based on the welding result monitoring information, from the supervisor terminal possessed by the second supervisor; and

determining approval or disapproval of the preliminary WPS as a completed WPS, based on the inspection result information for the preliminary WPS.

5 . An apparatus for automatically generating a welding procedure specification (WPS) by using a machine learning algorithm, the apparatus comprising:

a processor; and

a memory operatively connected to the processor and storing at least one code performed by the processor,

wherein the memory stores codes that cause, when executed through the processor, the processor to:

collect a WPS transmission request signal together with welding-related information comprising a welding material for a welding target and a thickness of the welding material;

generate a WPS corresponding to the welding-related information by using a machine learning model pre-trained to generate the WPS by using the welding-related information; and

transmit a WPS response signal together with the generated WPS, in response to the WPS transmission request signal,

wherein the machine learning model is a model trained through supervised learning using training data in which the welding-related information is an input and the WPS corresponding to the welding-related information is a label,

wherein the memory further stores codes that cause the processor to, when the WPS comprises a preliminary WPS before completion, transmit the preliminary WPS to one or more of a supervisor terminal possessed by a first supervisor, a supervisor terminal possessed by a second supervisor, and a welding device, before transmitting the WPS response signal, and

wherein the memory further stores codes that cause the processor to:

receive welding result monitoring information for the welding device, generated based on the preliminary WPS, from the supervisor terminal possessed by the first supervisor;

receive inspection result information for the preliminary WPS, generated based on the welding result monitoring information, from the supervisor terminal possessed by the second supervisor; and

determine approval or disapproval of the preliminary WPS as a completed WPS, based on the inspection result information for the preliminary WPS.

6 . The apparatus of claim 5 , wherein the memory further stores codes that cause the processor to collect the WPS transmission request signal together with the welding-related information comprising one or more of text, a speech, and an image, while collecting the WPS transmission request signal.

7 . The apparatus of claim 6 , wherein the memory further stores codes that cause the processor to collect the WPS transmission request signal together with the welding-related information from one or more of a welding machine, welding protective equipment, and a user terminal, while collecting the WPS transmission request signal.