18089108. JUDGING METHOD FOR A MODULE PEELING TIME OF A SOFT ELECTRONIC FABRIC MODULE AND A SYSTEM APPLYING THE SAME simplified abstract (INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE)
Contents
- 1 JUDGING METHOD FOR A MODULE PEELING TIME OF A SOFT ELECTRONIC FABRIC MODULE AND A SYSTEM APPLYING THE SAME
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 JUDGING METHOD FOR A MODULE PEELING TIME OF A SOFT ELECTRONIC FABRIC MODULE AND A SYSTEM APPLYING THE SAME - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 How does this method compare to traditional manual testing methods for judging module peeling time?
- 1.11 What are the limitations of this machine learning model in predicting module peeling time accurately?
- 1.12 Original Abstract Submitted
JUDGING METHOD FOR A MODULE PEELING TIME OF A SOFT ELECTRONIC FABRIC MODULE AND A SYSTEM APPLYING THE SAME
Organization Name
INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE
Inventor(s)
Cheng-Hung San of Xinpu Township (TW)
Hsin-Chung Wu of Sihu Township (TW)
Ming-Hong Chiueh of Taipei City (TW)
JUDGING METHOD FOR A MODULE PEELING TIME OF A SOFT ELECTRONIC FABRIC MODULE AND A SYSTEM APPLYING THE SAME - A simplified explanation of the abstract
This abstract first appeared for US patent application 18089108 titled 'JUDGING METHOD FOR A MODULE PEELING TIME OF A SOFT ELECTRONIC FABRIC MODULE AND A SYSTEM APPLYING THE SAME
Simplified Explanation
The abstract describes a method for judging the peeling time of a soft electronic fabric module using machine learning.
- Preselect a variety of module material combinations, each consisting of a substrate material, wire material, and packaging material.
- Extract parameters from the material combinations to create training data.
- Train a machine learning model with the training data to predict module peeling time accurately.
Potential Applications
This technology could be applied in the manufacturing of soft electronic fabric modules to optimize the peeling process and improve product quality.
Problems Solved
This innovation solves the problem of accurately predicting the peeling time of soft electronic fabric modules, which is crucial for manufacturing efficiency and product reliability.
Benefits
The benefits of this technology include increased production efficiency, improved product quality, and reduced material waste in the manufacturing process.
Potential Commercial Applications
A potential commercial application of this technology could be in the textile industry for the production of wearable electronic devices.
Possible Prior Art
One possible prior art could be research on machine learning applications in manufacturing processes to optimize production parameters.
Unanswered Questions
How does this method compare to traditional manual testing methods for judging module peeling time?
The article does not provide a direct comparison between this machine learning method and traditional manual testing methods for judging module peeling time.
What are the limitations of this machine learning model in predicting module peeling time accurately?
The article does not discuss any potential limitations of the machine learning model in accurately predicting module peeling time.
Original Abstract Submitted
A judging method for a module peeling time of a soft electronic fabric module is provided. The method includes: preselecting a plurality of module material combinations, the plurality of module material combinations respectively comprising a substrate material, a wire material and a packaging material; extracting the plurality of module material combinations to generate a plurality of module material combination parameters; generating a plurality of machine learning training data based on the plurality of module material combination parameters and a plurality of module pre-processing conditions; and training a machine learning model according to the plurality of machine learning training data to provide an optimized prediction model for judging a module peeling time.
