Plug-In Quality Review for Automated Manufacturing Exception Detection
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Solution Overview
Problem
Current quality review processes in manufacturing are tedious and reliant on manual data analysis by quality engineers, who must sift through vast amounts of raw data to identify exceptions, making the process time-consuming and prone to variability based on individual expertise.
Innovation Solution
A quality review management system with a configuration system for creating rules to automatically identify exceptions, an exception engine to process data in real-time, and a review interface to organize and manage exceptions, along with plug-ins for interfacing with third-party systems, providing a live view of process snapshots and efficient record presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data analysis by quality engineers is used, then expertise-based quality review can be performed, but the process is time-consuming and prone to variability
Solution Approach 1:
The patent segments the quality review process into distinct functional modules including data collection from multiple sources, exception detection engine with configurable rules, automated analysis components, and reporting systems. This modular architecture enables parallel processing of different data streams and exception types, significantly reducing review time while maintaining consistent quality standards through systematic rule-based evaluation
Solution Approach 2:
The patent replaces manual mechanical analysis by quality engineers with an automated exception detection engine that uses configurable rules, algorithms, and data processing systems. This substitution eliminates human variability and time constraints while maintaining or improving detection accuracy through consistent application of predefined quality criteria across all manufacturing data
2Productivity
If automated exception detection is implemented, then review time is reduced, but system complexity increases
Solution Approach 1:
The patent implements a universal exception detection platform that can handle multiple data sources (manufacturing equipment, test systems, process control systems), various exception types, and different analysis methods through a single configurable system. The engine uses standardized data interfaces and rule templates that can be adapted to different manufacturing contexts without requiring separate systems, thereby managing complexity while maintaining high productivity
Solution Approach 2:
The system manages complexity through configurable parameters and rules that can be adjusted without changing the underlying system architecture. Quality engineers can modify detection thresholds, rule priorities, data source configurations, and exception criteria through parameter settings rather than system redesign, enabling flexible adaptation to different manufacturing scenarios while maintaining a consistent core platform
3Reliability
If comprehensive data collection from multiple sources is performed, then quality review completeness is improved, but data processing complexity increases
Solution Approach 1:
The patent introduces intermediary components including standardized data collection interfaces, data normalization layers, and integration adapters that mediate between diverse manufacturing data sources and the exception detection engine. These intermediaries translate various data formats and protocols into a unified structure, enabling comprehensive data collection from equipment sensors, test systems, and process controls while managing integration complexity through standardized communication protocols
Data Source
AI summary
A quality review management system may be used to analyze the operation of manufacturing processes within a plant based on data collected by various data sources in the plant, such as batch executive applications, to automatically detect, store, and display exceptions within those processes for use by a quality review engineer to determine if the process operation meets certain quality standards. The quality review management system includes a configuration application that enables a user to create one or more exception rules, an exception engine that uses plug-ins in various devices to collect process data and to analyze the process data using the rules to detect one or more exceptions within the process, and a review application that enables quality review personnel to review each determined exception for resolution purposes.