Cross-Tool Process Control for Consistent Manufacturing Output
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Solution Overview
Problem
Manufacturing tools with varying types, models, and environmental conditions lead to product variation, despite programming and calibration efforts, due to factors like environmental changes, tool differences, and sensor variability.
Innovation Solution
A process control device system that includes sensors, processing resources, memory, and radios configured for cellular or WLAN communication, allowing data exchange between manufacturing tools. This system identifies attributes of data from one tool, determines settings for another tool, and sends commands to replicate output attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If tools are programmed and calibrated to perform the same functions, then manufacturing consistency should improve, but product variation still occurs due to environmental factors, tool differences, and sensor variability
Solution Approach 1:
The system collects data from sensors on manufacturing tools and uses machine learning models to analyze variations in real-time. The system provides feedback by automatically adjusting tool settings and parameters to compensate for environmental factors and tool differences, maintaining consistent product output despite variability in manufacturing conditions.
Solution Approach 2:
The system dynamically changes multiple parameters including tool settings, environmental conditions, and process parameters based on real-time data analysis. By adjusting these parameters adaptively, the system compensates for variations in tools and environmental factors to maintain manufacturing precision.
2Manufacturing precision
If data is collected and analyzed from multiple manufacturing tools, then product variation can be reduced, but system complexity increases
Solution Approach 1:
The system introduces a centralized data collection and analysis platform that acts as an intermediary between multiple manufacturing tools. This platform aggregates data from various tools, applies machine learning models, and coordinates adjustments across the manufacturing system, reducing overall system complexity while maintaining precision control.
Solution Approach 2:
The system employs universal machine learning models and standardized data protocols that can be applied across different tool types and manufacturing processes. This multi-functional approach allows the same system architecture to handle diverse manufacturing tools, reducing complexity through standardization.
Data Source
AI summary
Methods, devices, and systems related to process control in manufacturing are described. In an example, a method can include receiving data from a first process control device affixed to a first manufacturing tool of a first type, identifying one or more attributes of the data via a second processing resource of a second process control device affixed to a second manufacturing tool of a second type different from the first type, determining one or more settings for the second manufacturing tool via the second processing resource in response to identifying the one or more attributes of the data, and sending a command including the one or more settings to the second manufacturing tool from the second process control device.