Process Defect Profiling Using Entity-Specific Signatures
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current process control systems in process-manufacturing enterprises are inefficient in defect monitoring due to inability to encode complex interactions between process parameters, lack of customizability, and difficulty in interpreting multi-variate data, leading to high defect rates and inefficient defect tracking.
Innovation Solution
A defect profiling and tracking system that generates unique entity-specific process signatures (EPS) by analyzing product quality defect data and process parameter data, allowing for real-time detection of quality defects and process deviations through comparison with pre-defined EPS, enabling customizable and interpretable defect monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multi-variate control charts are used to encode complex interactions of process parameters, then the ability to capture complex parameter interactions is improved, but interpretability by human operators deteriorates
Solution Approach 1:
The patent segments the complex multi-variate analysis into two distinct components: (1) an automated machine-learning model that processes complex parameter interactions to generate defect predictions, and (2) a simplified visual interface that presents these predictions in an interpretable format to human operators. This segmentation allows the system to capture complex interactions through the ML model while maintaining ease of operation through the simplified visual display showing only relevant defect predictions and confidence scores.
2Device complexity
If a single unified process control system is used throughout the enterprise, then system simplicity is improved, but adaptability to different enterprise behaviors and defect types deteriorates
Solution Approach 1:
The patent implements a dynamic defect prediction system where the machine learning model is retrained periodically with new data from each enterprise's own process parameters and defect patterns. This allows the system to adapt to different enterprise behaviors and defect types over time while maintaining a unified architectural framework. Each enterprise's model is customized through continuous learning from their specific operational data, providing adaptability without requiring completely separate systems.
3Measurement precision
If domain expertise is required to narrow down unusual process states to defect categories, then diagnostic accuracy is improved, but operational complexity and time consumption worsen
Solution Approach 1:
The patent implements a self-service defect prediction system where the machine learning model automatically performs the diagnostic function that previously required domain expert intervention. The model takes process parameter data as input and directly outputs predicted defect types with confidence scores, eliminating the need for operators to manually analyze complex parameter interactions and classify defects. This automation maintains diagnostic accuracy through sophisticated algorithms while dramatically reducing the time and expertise required for defect identification.
Data Source
AI summary
A defect profiling and tracking system for a process-manufacturing enterprise is provided. The system includes a memory and a processor. The processor is configured to access entity data for a plurality of entities of the process-manufacturing enterprise and process parameter data for one or more deviating entities. The processor is configured to analyze the entity data and the process parameter data for each of the deviating entities to determine a plurality of relationships between quality defects and the process parameters to generate a unique entity specific process signature (EPS) for each entity. The processor is configured to receive real-time process parameter data for one or more entities to generate a real-time process signature for the one or more entities and compare the real-time process signature of each entity with EPS corresponding to the entity to detect one or more EPS matches that are indicative of a quality defect.