Mounting Error Cause Estimation Using Two-Factor Bias Analysis
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Solution Overview
Problem
Conventional methods for estimating the cause of mounting errors in component mounters often inaccurately identify individual devices or data as causative, failing to account for multiple factors, leading to unreliable error determination.
Innovation Solution
A device and method that select two types of factors potentially causing the error, performing determinations while replacing each factor to acquire multiple results, providing a more reliable estimation of the causative individual by assessing bias in error occurrence status across different combinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional ranking methods are used to identify causative individuals, then the estimation process is simple, but the reliability of error cause identification deteriorates due to inability to account for multiple factors
Solution Approach 1:
The estimation method segments the analysis by separating the first factor (whose individual is being evaluated) from the second factor (whose individuals are being compared). This segmentation allows the system to evaluate whether error occurrence status is biased according to differences in the second factor, thereby improving reliability without requiring a completely complex new approach.
Solution Approach 2:
The invention introduces a new dimension of analysis by examining error occurrence status across combinations of multiple factors rather than simply ranking individual devices. By analyzing whether error rates vary according to differences in one factor while holding another factor constant, the method achieves more reliable causative individual identification through multi-dimensional analysis.
2Measurement precision
If multiple factors are considered in error estimation, then the accuracy of causative individual identification improves, but the complexity of the estimation process increases
Solution Approach 1:
The method segments the estimation process into distinct steps: selecting two types of factors, obtaining error occurrence status for combinations of individuals, and determining bias according to differences in one factor. This segmentation makes the multi-factor analysis more manageable and less complex than a comprehensive simultaneous analysis of all factors.
Solution Approach 2:
The invention applies partial action by focusing on two specific factors at a time rather than analyzing all possible factors simultaneously. This partial approach achieves improved precision through multi-factor consideration while keeping the process complexity manageable by limiting the scope of each estimation cycle.
Data Source
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AI summary
A device for estimating a cause of a mounting error includes an error history storage section configured to aggregate and store an error occurrence status of a mounting error that mounting work of mounting a component on a board has failed in a component mounter by dividing the error occurrence status into individuals of devices and data, a factor setting section configured to select two types of factors from among the devices and the data and to respectively set the selected factors as a first factor and a second factor, a first determination section configured to perform a process of determining whether the error occurrence status is biased under a condition that an individual as the first factor is specified according to a difference in the second factor, on each of multiple individuals as the first factor, a second determination section configured to perform a process of determining whether the error occurrence status is biased under a condition that an individual as the second factor is specified according to a difference in the first factor, on each of multiple individuals as the second factor, and a cause estimation section configured to estimate a causative individual causing the mounting error based on determination results in the first determination section and the second determination section.