View source for 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)

=JUDGING METHOD FOR A MODULE PEELING TIME OF A SOFT ELECTRONIC FABRIC MODULE AND A SYSTEM APPLYING THE SAME=


==Organization Name==

[[:Category:INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE|INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE]]


[[Category:INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE]]

==Inventor(s)==

[[:Category:Cheng-Hung San of Xinpu Township (TW)|Cheng-Hung San of Xinpu Township (TW)]][[Category:Cheng-Hung San of Xinpu Township (TW)]]

[[:Category:Hsin-Chung Wu of Sihu Township (TW)|Hsin-Chung Wu of Sihu Township (TW)]][[Category:Hsin-Chung Wu of Sihu Township (TW)]]

[[:Category:Ming-Hong Chiueh of Taipei City (TW)|Ming-Hong Chiueh of Taipei City (TW)]][[Category: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.



[[Category:G06N20/00]]
[[Category:G06N7/01]]