Judging method for a module peeling time of a soft electronic fabric module and a system applying the same

A judging method for a module peeling time of a soft electronic fabric module, comprising:

• 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,
• wherein a heat resistance of the substrate material is 155° C. to 200° C., and a thermal expansion coefficient of the substrate material is 7 10−6/K to 147 10−6/K;
• a heat resistance of the wire material is 155° C. to 300° C., and a thermal expansion coefficient of the wire material is 117 10−6/K to 264 10−6/K;
• a heat resistance of the packaging material is 100° C. to 200° C., and a thermal expansion coefficient is 16 10−6/K to 380 10−6/K.

A prediction system for a module peeling time of a soft electronic fabric module, comprising:

• a data collection system, preselecting a plurality of module material combinations, wherein the plurality of module material combinations respectively comprises a substrate material, a wire material and a packaging material;
• a measuring device, extracting the plurality of module material combinations to generate a plurality of module material combination parameters;
• a lamination device, performing a plurality of module pre-processing conditions to the plurality of module material combinations;
• an environmental testing device, measuring a plurality of module peeling time for training of the plurality of module material combinations after performing the plurality of module pre-processing conditions;
• and
• a model building system, generating a plurality of machine learning training data based on the plurality of module material combination parameters and the plurality of module peeling time for training, 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.