Textile Machine Configuration Using ML for Consistent Production Quality
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Solution Overview
Problem
Current textile production methods face challenges in achieving consistent quality due to varying factors like hardware wear, indoor climate, and fiber blends, leading to inefficient manual configuration and significant waste production.
Innovation Solution
A computer-aided method for adapting textile machine configurations using machine learning to optimize settings through iterative production experiments, allowing for automated adjustment of settings based on detected impacts to achieve optimal production specifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual configuration determination is used, then operator experience can guide settings, but the process is time-consuming and optimization degree varies heavily
Solution Approach 1:
The system performs self-configuration by automatically determining optimal settings through production experiments. The machine learns and adjusts its own parameters based on sensor feedback and machine learning algorithms, eliminating the need for manual operator intervention and significantly reducing configuration time.
Solution Approach 2:
The patent replaces manual operator judgment with automated sensor-based detection and machine learning systems. Physical measurement devices and computational algorithms substitute for human experience, enabling consistent and rapid configuration determination without relying on operator expertise.
2Manufacturing precision
If configurations are determined manually, then some optimization can be achieved, but consistency cannot be maintained across different production runs
Solution Approach 1:
The system continuously monitors production parameters using sensors and feeds this data back to the control system. Based on this feedback, the machine learning algorithms automatically adjust configurations to maintain consistent production quality across different runs, eliminating variability introduced by manual configuration.
Solution Approach 2:
The patent dynamically adjusts production parameters based on real-time sensor data and learned patterns. By automatically modifying configuration parameters according to actual production conditions, the system maintains consistent quality outcomes despite variations in raw materials or environmental factors.
3Manufacturing precision
If off-site tests are conducted to find configuration, then configuration can be determined, but significant waste is produced
Solution Approach 1:
Instead of conducting extensive off-site tests that produce significant waste, the system performs minimal on-line production experiments using small amounts of material. The machine learning approach requires only partial testing to learn optimal configurations, dramatically reducing waste while maintaining configuration accuracy.
Solution Approach 2:
The patent uses sensor systems and simulation models as intermediaries between configuration determination and actual production. These intermediaries allow the system to test and optimize settings virtually or with minimal physical material, reducing the need for wasteful trial-and-error physical testing.
4Device complexity
If a single configuration is used for different production runs, then setup is simplified, but production quality varies due to hardware wear, climate, and fiber blends
Solution Approach 1:
The system transitions from static, fixed configurations to dynamic, adaptive configurations. Machine learning models continuously learn from production data and automatically adjust settings in real-time based on detected conditions such as hardware wear, climate variations, and material differences, maintaining quality without increasing operational complexity.
Solution Approach 2:
The patent automatically modifies configuration parameters based on detected production conditions. By continuously adapting parameters such as temperature, speed, and tension according to real-time sensor feedback, the system maintains consistent quality outcomes despite variations in equipment state or environmental factors.
Data Source
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AI summary
The invention relates to a method (100) for computer-aided adaptation of a configuration for different textile productions (210), in which a production specification (230) is varied for the different productions (210), and in which, in at least one production step (220) based on the production specification (230), a textile material (2) is processed by at least one machine (10), comprising the following steps for automated production preparation and production initiation of the respective textile production (210) with the varied production specification (230): - Initiating (101) the execution of at least one or more production experiments (215), wherein in the respective production experiment (215) the at least one production step (220) is configured with a setting (240) and carried out based on the production specification (230),- Determining (102) an effect (250) of the configuration based on the setting (240) in the respective production experiment (215), - Adjusting (103) the setting (240) based on the determined effect (250) in order to determine a setting (240) optimized for the production requirement (230), - Initiating (104) an execution of the textile production (210) in which the at least one production step (220) is configured with the optimized setting (240) and carried out based on the production requirement (230), wherein at least one machine learning method (300) is used to perform the adjustment (103) based on the determined effect (250), wherein preferably the at least one machine learning method (300) is implemented as at least one kernel method (300).