Manufacturing Process Graph Matching for Equipment Repurposing
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Solution Overview
Problem
Manufacturers face challenges in identifying alternate production processes due to the complexity and varying naming conventions across industries, making it difficult to repurpose production equipment for producing different products without substantial reconfiguration.
Innovation Solution
An analysis system utilizing machine learning models to generate graphs of production processes, identifying isomorphic subgraphs to match production steps across different processes, enabling the identification of equipment capable of performing similar operations despite different naming conventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manufacturers attempt to identify alternate production processes manually, then they can find some alternatives, but they miss many due to process complexity and varying naming conventions
Solution Approach 1:
The patent replaces manual analysis with an automated machine learning system that uses natural language processing and graph isomorphism algorithms to identify alternate production processes. The system automatically parses process descriptions, builds knowledge graphs, and matches processes across different naming conventions, eliminating the limitations of human analysts while handling complex production processes at scale.
Solution Approach 2:
The patent transforms unstructured text descriptions of production processes into structured graph representations with standardized nodes and edges. By changing the representation parameters from natural language to formal graph structures, the system enables automated comparison and identification of isomorphic subgraphs, thereby improving the ability to find alternate processes despite naming variations.
2Ease of manufacture
If manufacturers repurpose production equipment for different products, then they can reduce operational expenses, but they face difficulties due to naming conventions and process complexity
Solution Approach 1:
The patent introduces an intermediary machine learning system that acts as a bridge between different production process descriptions. This system uses natural language processing to interpret varying naming conventions and graph isomorphism to identify underlying process similarities, thereby preventing information loss and enabling accurate matching of alternate processes for equipment repurposing.
Solution Approach 2:
The patent creates a universal framework that handles multiple production process descriptions with varying naming conventions through a single standardized graph representation approach. The system can process different input formats and naming styles while maintaining consistent analysis capabilities, enabling broad applicability across diverse manufacturing contexts.
3Measurement precision
If human analysts review production processes to find alternatives, then they can identify some matches, but they are unable to identify similar manufacturing steps due to volume and complexity
Solution Approach 1:
The patent replaces human analysts with an automated machine learning system capable of processing large volumes of production process descriptions efficiently. The system uses natural language processing and graph isomorphism algorithms to achieve both high precision in identifying similar manufacturing steps and high productivity in analyzing large numbers of processes simultaneously.
Solution Approach 2:
The patent creates simplified graph representations (copies) of complex production processes that capture essential structural relationships. These graph copies enable efficient automated comparison and matching without requiring detailed human analysis of each full process description, thereby maintaining precision while increasing productivity.
Data Source
AI summary
A computing system is configured to obtain a description of a first production process comprising first production steps. The computing system is further configured to apply a machine learning (ML) model to the description of the first production process to produce a first graph of the first production process comprising a first subgraph of first nodes. The computing system is further configured to identify a second graph of a second production process comprising a second subgraph of second nodes at least weakly isomorphic to the first subgraph, wherein each second node of the second nodes represents a corresponding second production step of second production steps of the second production process. The computing system is further configured to, based on the identified second graph, output an indication that equipment capable of performing the first production steps is capable of performing the second production steps of the second production process.