Weld Sequencer Outlier Analysis for Stable Weld Parameter Limits
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Solution Overview
Problem
Semi-automatic welding work cells face challenges in detecting suboptimal welds due to human error and the difficulty in identifying flawed data within large reports, leading to either overly strict or lenient weld parameter limits.
Innovation Solution
A weld sequencer system that performs statistical analysis on generated reports to automatically determine weld parameter limits, removing outlier data and defining valid ranges, which are then used to alert operators of invalid welds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual determination of weld parameter limits is used, then operator judgment can be applied, but human error and subjectivity lead to inaccurate weld validation
Solution Approach 1:
The weld sequencer automatically performs statistical analysis on weld data to self-determine weld parameter limits without operator intervention. The system collects weld data, identifies outliers using statistical methods, calculates mean and standard deviation, and automatically sets limits as mean plus or minus a specified number of standard deviations, eliminating human error and subjectivity
Solution Approach 2:
The patent replaces the manual mechanical process of operator judgment with an automated computational system. The weld sequencer uses statistical algorithms and automated data processing to determine weld parameter limits, substituting human cognitive processes with machine-based statistical analysis that provides consistent and accurate results
2Measurement precision
If statistical analysis with outlier removal is performed, then weld parameter limits become more accurate, but additional data processing steps increase system complexity
Solution Approach 1:
The weld sequencer performs preliminary statistical analysis on accumulated weld data to establish baseline parameter limits before actual weld validation begins. By pre-processing the data to identify outliers and calculate statistical parameters, the system prepares accurate validation criteria in advance, improving subsequent weld detection accuracy without adding complexity during active welding operations
Solution Approach 2:
The system continuously collects weld data, performs statistical analysis, and uses the results to refine and update weld parameter limits. This feedback loop allows the system to learn from accumulated data and automatically adjust validation criteria, improving accuracy over time while the automated nature of the feedback process prevents complexity from escalating
Data Source
AI summary
Various systems and methods are provided that allow a weld sequencer to use a statistical analysis of generated reports to automatically determine weld parameter limits for the welds defined by various functions in a sequence file. For example, the weld sequencer can take reports generated for a specific type of part and statistically analyze the weld data included in the reports according to a set of analysis parameters provided by a user. The weld sequencer can use the statistical analysis to identify and remove outlier data and define a set of weld parameter limits based on the remaining data. The weld parameter limits can define a low limit and/or a high limit for one or more weld parameters associated with a function. The weld sequencer can then update the sequence file to include the weld parameter limits.