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公开(公告)号：US20210405620A1
公开(公告)日：2021-12-30
申请号：US17237252
申请日：2021-04-22
Applicant: ILLINOIS TOOL WORKS INC.
Inventor： Stephen Paul Ivkovich
IPC: G05B19/418 , G06N20/00 , G06N5/04
Abstract: Systems and methods for part tracking using machine learning techniques are described. In some examples a part tracking system analyzes feature characteristics related to one or more welds to identify, determine characteristics of, and/or label one or more parts repeatedly assembled by the welds. Identifying parts assembled from the welds may make it possible to do part based analytics (e.g., related to part quality, cost, production efficiency, etc.), as opposed to just weld based analytics, on past welding data. Additionally, identifying a part assembled from several welds results in an ordering of those several welds used to create the part, which can make it easier to compare/contrast similar welds across parts. Further, determining the characteristics of the parts can assist in configuring certain part tracking systems, thereby reducing the expertise, time, and personnel required.
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Patent Agency Ranking
公开(公告)号：US12246399B2
公开(公告)日：2025-03-11
申请号：US17238667
申请日：2021-04-23
Applicant: ILLINOIS TOOL WORKS INC.
Inventor： Stephen Paul Ivkovich
Abstract: Systems and methods for part tracking using machine learning techniques are described. In some examples a part tracking system analyzes feature characteristics related to one or more welds to identify, determine characteristics of, and/or label one or more parts repeatedly assembled by the welds. Identifying parts assembled from the welds may make it possible to do part based analytics (e.g., related to part quality, cost, production efficiency, etc.), as opposed to just weld based analytics, on past welding data. Additionally, identifying a part assembled from several welds results in an ordering of those several welds used to create the part, which can make it easier to compare/contrast similar welds across parts. Further, determining the characteristics of the parts can assist in configuring certain part tracking systems, thereby reducing the expertise, time, and personnel required.
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公开(公告)号：US12251773B2
公开(公告)日：2025-03-18
申请号：US17319670
申请日：2021-05-13
Applicant: ILLINOIS TOOL WORKS INC.
Inventor： Stephen Paul Ivkovich
Abstract: Systems and methods for missing weld identification using machine learning techniques are described. In some examples, a part tracking system uses machine learning techniques to identify whether an operator has missed one or more welds when assembling a part. The part tracking system may additionally identify which specific welds were missed (e.g., the first weld, the third weld, the fifteenth weld, etc.). The part tracking system may be able to identify missing welds after a part has been completed, or in real-time, during assembly of the part. Identification of the particular weld(s) missed during the welding process can help an operator quickly assess and resolve any issues with the part being assembled, saving time and ensuring quality.
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公开(公告)号：US20220032397A1
公开(公告)日：2022-02-03
申请号：US17319670
申请日：2021-05-13
Applicant: ILLINOIS TOOL WORKS INC.
Inventor： Stephen Paul Ivkovich
Abstract: Systems and methods for missing weld identification using machine learning techniques are described. In some examples, a part tracking system uses machine learning techniques to identify whether an operator has missed one or more welds when assembling a part. The part tracking system may additionally identify which specific welds were missed (e.g., the first weld, the third weld, the fifteenth weld, etc.). The part tracking system may be able to identify missing welds after a part has been completed, or in real-time, during assembly of the part. Identification of the particular weld(s) missed during the welding process can help an operator quickly assess and resolve any issues with the part being assembled, saving time and ensuring quality
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公开(公告)号：US20220032396A1
公开(公告)日：2022-02-03
申请号：US17319345
申请日：2021-05-13
Applicant: ILLINOIS TOOL WORKS INC.
Inventor： Stephen Paul Ivkovich
Abstract: Systems and methods for missing weld identification using machine learning techniques are described. In some examples, a part tracking system uses machine learning techniques to identify whether an operator has missed one or more welds when assembling a part. The part tracking system may additionally identify which specific welds were missed (e.g., the first weld, the third weld, the fifteenth weld, etc.). The part tracking system may be able to identify missing welds after a part has been completed, or in real-time, during assembly of the part. Identification of the particular weld(s) missed during the welding process can help an operator quickly assess and resolve any issues with the part being assembled, saving time and ensuring quality
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公开(公告)号：US20210402523A1
公开(公告)日：2021-12-30
申请号：US17238667
申请日：2021-04-23
Applicant: ILLINOIS TOOL WORKS INC.
Inventor： Stephen Paul Ivkovich
Abstract: Systems and methods for part tracking using machine learning techniques are described. In some examples a part tracking system analyzes feature characteristics related to one or more welds to identify, determine characteristics of, and/or label one or more parts repeatedly assembled by the welds. Identifying parts assembled from the welds may make it possible to do part based analytics (e.g., related to part quality, cost, production efficiency, etc.), as opposed to just weld based analytics, on past welding data. Additionally, identifying a part assembled from several welds results in an ordering of those several welds used to create the part, which can make it easier to compare/contrast similar welds across parts. Further, determining the characteristics of the parts can assist in configuring certain part tracking systems, thereby reducing the expertise, time, and personnel required.
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