Production Facility Control Using Causal Models for Operating Conditions
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Solution Overview
Problem
Existing methods for determining operating conditions in production facilities fail to accurately account for the interplay of multiple factors, leading to insufficient results.
Innovation Solution
A control apparatus that utilizes a learned model to optimize process data by setting constraint conditions and objective functions, incorporating causality information and cost considerations, allowing for accurate determination of operating conditions that satisfy desired criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional prediction systems are used to determine operating conditions, then basic prediction capability is provided, but accuracy is insufficient due to failure to account for interplay of multiple factors
Solution Approach 1:
The system segments the complex prediction task into multiple independent mathematical expression models, each representing a specific stage or aspect of the manufacturing process. This allows accurate modeling of individual factors while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The system combines multiple mathematical expression models into a composite prediction framework that integrates various factors (material properties, process parameters, environmental conditions) to accurately capture the interplay of multiple influences on product quality and operating conditions.
2Manufacturing precision
If multiple factors are considered in determining operating conditions, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The calculation system is divided into separate mathematical expression models for different process stages, allowing complex multi-factor analysis to be performed through sequential, manageable calculations rather than a single overwhelming computation.
Solution Approach 2:
The system performs preliminary calculations and data preparation in advance, organizing process data and establishing mathematical relationships before final optimization, which reduces the complexity of real-time computations while maintaining high accuracy.
Data Source
AI summary
To accurately obtain operating conditions of a production facility that satisfies desired criteria. A control apparatus includes a process data acquisition unit that reads process data from a storage device that stores the process data obtained from a production facility, and a control unit that obtains an optimum solution for a predetermined adjustment target of the process data and controls the production facility on the basis of the optimum solution. The optimum solution satisfies constraint conditions and optimizes an objective function. The constraint conditions are defined by using at least some of the process data. The objective function is defined by using at least some of the process data. A learned model having learned features of the process data obtained from the production facility is further stored in a storage device on the basis of causality information that defines a combination of first process data and second process data. The first process data is used as an explanatory variable. The second process data is used as a response variable. The constraint conditions include a condition that the second process data or a value corresponding to the second process data is a value calculated by using the first process data and the learned model, and an upper limit or a lower limit for at least some of the process data.