Causal Model Control for Adaptive Manufacturing Process Optimization

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Solution Overview

Problem

Existing manufacturing process control systems either rely on passive modeling techniques that struggle to discover effective patterns or active control techniques that require costly experimentation, failing to accurately model causal relationships between control settings and environment responses.

Innovation Solution

A control system that actively selects and adjusts control settings based on a causal model, repeatedly measuring their impact on manufacturing process success, allowing for swift and accurate modeling of these relationships, including uncontrollable environmental factors, and continuously updating to maximize process success.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If passive modeling techniques are used to determine control settings, then the system can observe historical data without active intervention, but the system fails to accurately discover causal relationships and patterns in the data

Engineering Contradiction:

Improveaccuracy of causal relationship modelingVSAvoidspeed of model improvement

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system dynamically transitions between passive observation mode and active experimentation mode. The causal model actively selects control settings to test specific causal hypotheses, transforming the static passive modeling approach into a dynamic system that can both observe and actively probe the manufacturing environment to discover causal relationships more efficiently

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system implements feedback loops where the causal model receives environment responses from control settings, updates its causal relationships based on observed outcomes, and uses this refined understanding to select subsequent control settings. This continuous feedback mechanism enables the system to progressively improve model accuracy while maintaining productivity through targeted experimentation

Inventive Principle:

Principle #23Feedback

2Productivity

If active control techniques are used to generate knowledge, then the system can actively test control settings, but the experimentation becomes costly and time-consuming

Engineering Contradiction:

Improvespeed of knowledge generationVSAvoidtime required for experimentation

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The causal model performs preliminary analysis of historical data and current system state to identify the most promising control settings to test. By pre-selecting which experiments are most likely to yield valuable causal knowledge, the system avoids wasteful random experimentation and focuses resources on high-value tests that advance understanding of causal relationships

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system changes parameters of the causal model itself, adjusting which control settings are selected based on the current state of knowledge. The causal model dynamically modifies its exploration strategy, shifting between exploiting known causal relationships and exploring new ones, thereby optimizing the balance between knowledge generation speed and experimentation cost

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If traditional control systems are used, then the system can maintain stable operation, but the system cannot rapidly adapt to new conditions or improve process performance

Engineering Contradiction:

Improveability to adapt to environmental changesVSAvoidstability of manufacturing process

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The causal model dynamically adapts its understanding of causal relationships as new data becomes available, allowing the system to adjust control settings in response to changing environmental conditions. This dynamic adaptation maintains reliability by building on established causal knowledge while improving adaptability through continuous learning from new observations

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system uses feedback from environment responses to continuously refine the causal model. When environmental conditions change, the causal model detects these changes through observed deviations in process outcomes and adapts its causal relationships accordingly, maintaining stable operation under normal conditions while enabling rapid adaptation when changes occur

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250355410A1Controlling a manufacturing process using causal models

Publication Date: 2025.11.20 3M INNOVATIVE PROPERTIES CO

AI summary

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing a manufacturing process. In one aspect, the method comprises repeatedly performing the following: i) selecting a configuration of control settings for a manufacturing process, based on a causal model that measures causal relationships between control settings and a measure of a success of the manufacturing process; ii) determining the measure of the success of the manufacturing process using the configuration of control settings; and iii) adjusting, based on the measure of the success of the manufacturing process using the configuration of control settings, the causal model.