Machine Learning Weld Verification for Specific Missing Welds
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Solution Overview
Problem
Conventional part tracking systems cannot identify specific missing welds during the welding process, leading to potential defects in assembled parts, as they only detect the number of welds performed and fail to differentiate between missed or extra welds, which can compromise the integrity and quality of the part.
Innovation Solution
A system utilizing machine learning techniques, including processing circuitry and memory circuitry with computer-readable instructions, identifies initial and subsequent welds by comparing feature characteristics with a typical part model, determining if required welds are present, and outputs notifications to indicate missing welds, enabling the system to disable welding equipment and provide specific feedback on which welds are missed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional part tracking systems only detect the number of welds performed, then the system complexity is reduced, but the measurement precision and ability to identify specific missing welds deteriorates
Solution Approach 1:
The patent segments the welding process into individual weld events, each with unique identifiers and characteristics. By dividing the overall welding sequence into discrete, trackable units with specific features (start time, end time, location, parameters), the system can precisely identify which specific welds are missing rather than just counting total welds, thereby improving measurement precision without proportionally increasing system complexity.
Solution Approach 2:
The patent introduces an intermediary database that stores expected weld information and actual weld detection data. This intermediary layer enables comparison between planned and executed welds, allowing the system to identify specific missing welds by matching detected welds against the expected sequence, thus improving measurement precision while keeping the overall system architecture manageable.
2Ease of operation
If conventional systems only count total welds, then the ease of operation is improved, but the reliability of part quality assurance deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors detected welds, compares them against the expected weld sequence stored in the database, and provides real-time feedback on which specific welds are missing. This feedback loop maintains ease of operation through automated comparison while significantly improving reliability by ensuring that each required weld is verified rather than just counting total welds.
Solution Approach 2:
The patent replaces manual inspection and simple counting mechanisms with automated sensor-based detection and computational analysis. By using sensors to automatically detect weld characteristics and computational algorithms to compare detected welds against expected sequences, the system maintains ease of operation through automation while improving reliability through precise, objective verification of each weld's presence and quality.
3Manufacturing precision
If the system identifies specific missing welds using machine learning, then the manufacturing precision is improved, but the device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-storing the expected weld sequence and characteristics in a database before the welding process begins. By having the reference information ready in advance, the system can quickly compare actual welds against expected ones during execution, achieving high manufacturing precision in identifying missing welds without requiring complex real-time analysis, thus limiting the increase in device complexity.
Solution Approach 2:
The patent creates a digital copy of the expected weld sequence and characteristics stored in a database. This copy serves as a reference template that can be efficiently compared against actual weld detection data. By using this copied reference information rather than requiring complex real-time generation of expected values, the system achieves high accuracy in identifying missing welds while keeping the computational complexity manageable.
Data Source
AI summary
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.