Anodic Dyeing Parameter Setting Using Regression and Reinforcement Learning
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Solution Overview
Problem
Existing material processing methods, such as anodic dyeing, rely heavily on manual adjustment of parameters based on experience, leading to inefficiencies, high labor costs, and poor reproducibility.
Innovation Solution
A method and device utilizing a trained regression model and reinforcement learning algorithm to determine processing parameters, leveraging historical data to improve accuracy and efficiency in setting parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual parameter adjustment based on staff experience is used, then flexibility in handling different dyeing cases is maintained, but adjustment efficiency is low and labor costs are high
Solution Approach 1:
The system enables self-service by automatically determining secondary dyeing parameters through the trained reinforcement learning model and regression model, eliminating the need for manual staff intervention. The model independently processes historical data and current dyeing state to generate optimized parameter recommendations, allowing the system to serve itself rather than relying on human operators.
Solution Approach 2:
The patent replaces the mechanical system of manual parameter adjustment with an intelligent algorithmic system. The reinforcement learning model and regression model substitute human staff's experience-based decision-making with data-driven automated calculations, transforming manual operations into computational processes that run on electronic devices.
2Adaptability or versatility
If manual parameter adjustment based on staff experience is used, then adaptability to different cases is maintained, but labor costs increase
Solution Approach 1:
The system enables self-service by automatically determining secondary dyeing parameters through the trained reinforcement learning model and regression model, eliminating the need for manual staff intervention. The model independently processes historical data and current dyeing state to generate optimized parameter recommendations, allowing the system to serve itself rather than relying on human operators.
3Reliability
If manual parameter adjustment is used, then experience-based judgment is applied, but reproducibility of adjustments is poor
Solution Approach 1:
The system implements feedback by using historical processing data as input for the regression model and reinforcement learning algorithm. The model learns from past dyeing outcomes and continuously refines its parameter recommendations based on accumulated experience, ensuring consistent and reproducible adjustments across different operations while maintaining the benefit of experience-based judgment.
Solution Approach 2:
The patent transforms the subjective parameter adjustments made by staff into objective, standardized parameters derived from historical data analysis. By converting experience-based judgment into quantifiable model inputs and outputs, the system achieves reproducible parameter settings that can be consistently applied across different dyeing operations.
4Manufacturing precision
If multiple tests and adjustments are performed to ensure qualified color, then color quality is improved, but time consumption increases
Solution Approach 1:
The system applies preliminary action by pre-training the reinforcement learning model and regression model on extensive historical processing data before actual dyeing operations. This preliminary training phase allows the model to learn optimal parameter combinations and predict outcomes in advance, enabling accurate parameter determination without requiring multiple iterative tests during actual production, thus reducing time consumption while maintaining color quality.
Data Source
AI summary
A parameter setting method acquires historical processing data, pre-processes the historical processing data to obtain sample data, and creates a trained regression algorithm using the sample data, then acquires preliminary emulated processing data and predetermined objective parameters inputted, wherein the preliminary emulated processing data and the predetermined objective parameters constitute emulated processing data. A reinforcement learning algorithm is trained with the historical processing data, emulated processing data, and the reinforcement learning algorithm; an actual objective parameter are set according to the actual reference parameters and the reinforcement learning algorithm. Scale of the training data for the reinforcement learning algorithm is greatly increased by the trained regression algorithm, which improves the accuracy of the learning algorithm. A parameter setting device, an electronic device, and a storage medium are also disclosed.