Mounting Error Cause Estimation Across Device and Data-Type Factors
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Solution Overview
Problem
Conventional methods for estimating the cause of mounting errors in component mounters are unreliable, often attributing the cause to a single device or factor when multiple factors may be involved, leading to inaccurate identification of causative individuals.
Innovation Solution
A device and method that aggregate error occurrence status by dividing it into individual devices and data types, using a factor setting section to select two types of factors and determine whether the error occurrence status is biased, allowing for estimation of causative individuals through multiple determination processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional ranking method is used to aggregate mounting errors, then error counting is simple, but estimation reliability is low due to multiple causative factors being attributed to single device
Solution Approach 1:
The patent segments the error analysis by introducing a hierarchical structure with two levels: device level (first factor) and component level (second factor). The error aggregation is divided into device error aggregation and component error aggregation, allowing systematic breakdown of mounting errors to identify specific causative individuals among multiple devices and components.
Solution Approach 2:
The patent adds a new dimension to error analysis by introducing the second factor (component level) alongside the traditional first factor (device level). This two-dimensional error aggregation approach transforms the conventional single-dimension ranking into a multi-dimensional analysis framework, enabling more accurate identification of causative factors.
2Measurement precision
If single factor analysis is used, then error attribution is straightforward, but accuracy decreases when multiple devices or data types are involved
Solution Approach 1:
The analysis is segmented into two distinct factors: first factor representing devices and second factor representing components or data types. Each factor is analyzed separately through dedicated aggregation processes, allowing precise identification of causative individuals while maintaining manageable analysis complexity through structured segmentation.
Solution Approach 2:
The patent introduces error aggregation results as an intermediary that connects device-level analysis with component-level analysis. This intermediary layer processes and organizes error data from multiple sources, enabling accurate cross-factor comparison and identification of causative individuals without overwhelming complexity.
Data Source
AI summary
A device for estimating a cause of a mounting error includes 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.