Fabric Dyeing Control Data Modeling for Consistent Scale-Up
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Solution Overview
Problem
The current fabric dyeing process is inefficient due to the reliance on experience to determine control data sets, leading to repeated stages and increased costs, as the dye formula and control data sets decided in the laboratory may not meet the requirements for large-scale production, resulting in suboptimal dyeing results.
Innovation Solution
An apparatus and method that utilize historical control data sets to calculate a target control data set by deciding dyeing quality-related models and minimizing dyeing target-related models, considering multiple control factors and their interactions, to efficiently determine optimal control data for the fabric dyeing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the control data set is decided by experience in the laboratory, then the dye formula can be determined, but the dyeing result may not meet the requirement for large-scale production
Solution Approach 1:
The system performs preliminary analysis by establishing a dyeing quality-related model using historical control data sets before actual production. This preliminary modeling action predicts the optimal control data set for large-scale production, avoiding the need for repeated trial-and-error cycles and ensuring the dyeing result meets requirements from the outset.
Solution Approach 2:
The patent replaces the experience-based mechanical decision-making process with an automated computational system. The processor automatically establishes the dyeing quality-related model, calculates the optimal control data set, and provides recommendations, substituting human experience with systematic computational analysis that ensures consistency and reliability.
2Manufacturing precision
If the three stages are repeated multiple times to achieve expected dyeing result, then the dyeing quality can be improved, but the production cost will remarkably increase
Solution Approach 1:
The system utilizes historical control data sets as feedback from previous dyeing operations to establish the dyeing quality-related model. This feedback mechanism allows the system to learn from past experiences and predict the optimal control data set, avoiding repeated trial-and-error cycles and reducing the need for multiple three-stage repetitions, thereby lowering production costs while maintaining dyeing quality.
Solution Approach 2:
By performing preliminary modeling and prediction before actual production, the system determines the optimal control data set in advance. This preliminary action prevents the need for repeated three-stage cycles, directly reducing the energy loss and production costs associated with multiple iterations while ensuring the desired dyeing quality is achieved.
3Ease of operation
If the control data set is decided by experience, then the process is simple, but the efficiency is low and requires repeated stages
Solution Approach 1:
The system performs self-service by automatically establishing the dyeing quality-related model and calculating the optimal control data set without requiring manual intervention. The processor autonomously analyzes historical control data sets, determines the relationship between control factors and dyeing quality, and generates the optimal control data set, maintaining ease of operation while dramatically improving efficiency.
Solution Approach 2:
The patent replaces the simple but inefficient experience-based decision-making process with an automated computational system. The processor automatically establishes the dyeing quality-related model and calculates the optimal control data set, substituting manual experience-based operations with efficient computational analysis that maintains simplicity while improving productivity.
Data Source
AI summary
An apparatus, method, and non-transitory computer readable medium thereof for deciding a target control data set of a fabric dyeing process are provided. The apparatus decides a plurality of determination factors of a dyeing quality-related model according to a plurality of control factors corresponding to a plurality of historical control data set and calculates a coefficient corresponding to each of the determination factors. The apparatus further calculates the target control data set that minimizes a dyeing target-related model according to a control condition set, wherein the control condition set includes a predetermined range of the dyeing quality-related model and a predetermined range of each control factor.