Manufacturing Control Model Using Reinforcement Learning Feedback
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Solution Overview
Problem
Conventional methods for manufacturing systems, such as bioreactors, fail to determine suitable operation contents efficiently, leading to suboptimal manufacturing processes.
Innovation Solution
An apparatus and method that utilize a learning process to determine operation contents for a manufacturing system by acquiring state parameter sets, executing reinforcement learning with a control model, and adjusting operation contents based on reward values to optimize manufacturing outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional methods are used to determine operation content for manufacturing systems, then the manufacturing process can be executed, but the manufacturing efficiency and suitability of operation content remain suboptimal
Solution Approach 1:
The patent implements feedback mechanisms where state parameter sets are continuously acquired from the manufacturing system, processed through learning algorithms, and used to adjust operation contents. The system monitors the relationship between operation contents and state parameters, using this feedback loop to iteratively improve manufacturing efficiency and operation content suitability based on actual system responses.
Solution Approach 2:
The manufacturing system performs self-optimization through autonomous learning processes. The control model automatically learns optimal operation contents by processing state parameter data and generating improved operation sequences without external intervention, enabling the system to self-adjust and improve manufacturing efficiency while ensuring operation content suitability.
2Adaptability or versatility
If trial and error methods are used to optimize manufacturing processes, then operation contents can be adjusted, but time and resources are wasted
Solution Approach 1:
The system performs preliminary learning and analysis of state parameter data before actual manufacturing optimization is needed. By pre-processing data and building control models in advance, the system prepares optimal operation contents ahead of time, enabling rapid adaptation to changing environments without requiring time-consuming trial and error during production.
Solution Approach 2:
The patent replaces traditional mechanical trial-and-error adjustment methods with automated learning algorithms and control models. The system uses computational processing of state parameter data to determine optimal operation contents, substituting physical experimentation with intelligent computation, thereby reducing time loss while maintaining adaptability.
3Productivity
If learning processes are implemented to determine operation contents, then manufacturing efficiency improves, but system complexity increases
Solution Approach 1:
The learning system is segmented into distinct functional modules: state parameter acquisition units, learning processing units, control models, and operation content generation units. Each module performs a specific function, making the overall complex system manageable through modular design. This segmentation allows the system to achieve high manufacturing efficiency while controlling complexity through clear functional boundaries.
Solution Approach 2:
The control model is designed as a universal platform that can handle multiple manufacturing scenarios and types of state parameters. By creating a multi-functional learning system that can process various data types and generate different operation contents across diverse manufacturing contexts, the patent achieves high productivity without proportionally increasing complexity, as the same core architecture serves multiple purposes.
Data Source
AI summary
An apparatus is provided, which includes a setting unit for setting an operation content for a manufacturing system configured to manufacture an object to be manufactured, a first acquisition unit for acquiring a posterior state parameter set indicating a state of at least one of the manufacturing system or the object to be manufactured after the operation content is set, and a learning processing unit for executing, by using learning data including the operation content and the posterior state parameter set, a learning process of a control model of the manufacturing system configured to output the operation content that increases a reward value determined by a preset reward function in response to input of a state parameter set indicating a state of at least one of the manufacturing system or the object to be manufactured.