Manufacturing Cycle Time Modeling With LLM-Based Process Analysis
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Solution Overview
Problem
There is a need for improved control and management of manufacturing processes using machine learning tools such as generative AI to enhance data conversion and analysis for effective decision-making.
Innovation Solution
The use of generative AI to convert manufacturing data into near natural language representations, leveraging large language models for analysis and recommendation, and integrating sensor data for real-time process control and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manufacturing data is converted into near natural language representations using generative AI, then data analysis capability is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediate representation layer that converts manufacturing data into near natural language format. This intermediary layer acts as a bridge between raw manufacturing data and large language models, enabling effective analysis while managing system complexity through modular data transformation.
Solution Approach 2:
The system segments the data processing pipeline into distinct components: data conversion to near natural language, summarization by a first LLM, and analysis by a second LLM. This segmentation allows each component to be optimized independently, improving overall analysis capability while maintaining manageable system complexity.
2Measurement precision
If multiple large language models are used for summarization and analysis, then insight generation quality is improved, but processing time increases
Solution Approach 1:
The patent divides the AI processing task into two specialized LLMs: one for summarization and another for analysis. This segmentation allows each model to be optimized for its specific function, improving overall insight quality while enabling parallel processing to mitigate time increases.
Solution Approach 2:
The first LLM performs summarization as a preliminary action before the second LLM conducts detailed analysis. This preliminary processing reduces the complexity of input data for the analysis stage, improving final insight quality while optimizing the overall processing workflow.
3Reliability
If real-time sensor data is integrated into the analysis system, then process monitoring capability is improved, but data processing complexity increases
Solution Approach 1:
The near natural language representation system serves multiple functions: it structures manufacturing data, enables LLM processing, and accommodates real-time sensor data integration. This universal representation format improves monitoring capability while avoiding the need for separate processing pipelines that would increase complexity.
4Loss of information
If manufacturing data is converted into detailed human-readable descriptions, then data interpretability is improved, but data processing time increases
Solution Approach 1:
The system generates near natural language representations that provide sufficient interpretability for manufacturing data without creating overly detailed descriptions. This partial action approach maintains data understandability while avoiding excessive processing time associated with comprehensive detailed descriptions.
Data Source
AI summary
In an industrial processes, a properly instrumented line facilitates capture of data including detected steps, application input, and execution graph transitions, that permit the creation of empirical models of process timing. In this context, a process controlled by individual applications, e.g., at manufacturing workstations, provides a proxy for overall process timing by dividing a workflow into a number of discrete steps completed at each workstation, and further into any number of sub-steps, each controlled by a user and explicitly completed, e.g., by user interactions with widgets or other controls of the application. These applications provide a useful framework for modeling execution timing by providing an initial, implicit model for workflow (based on application control logic) that also facilitates automated detection of process sub-steps based on execution flow, as well as detection and measurement of the contributions of individual widgets and/or combinations of widgets to the process timing. By gathering data in this manner, mixed statistical distributions can be applied based on individual timing data for each possible sub-step, widget, process step, and the like performed with each application.