Data-Driven Strain Sensor Design Based on a Knowledge Graph Framework
Abstract
:1. Introduction
2. Materials and Methods
2.1. Knowledge Graph Building of the Strain Sensors
2.2. Representation Learning
2.3. Knowledge Reasoning
2.4. Fabrication of a Designed Strain Sensor
2.5. Characterization
3. Results and Discussion
3.1. The Effect of the KGAI Representation Method
3.2. Navigation Model of Prediction
3.3. Entity Prediction
3.4. Automatic Strain Sensor Design
3.5. Characteristics of the Designed Strain Sensor
3.6. Demonstration of a Human Action Monitoring Application
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Code: Graph Query Implemented to Access Results |
---|
Query rules: Match (v:matrix{name: “PDMS”})\WHERE v. matrix.name == “PDMS”\ RETURN v; |
Results = graph. run(match_str) |
Input | Verification | |
---|---|---|
Flexible substrate: yarn | Gauge factor:50 Strain range: 90% | Reference [38] |
Functional materials: rGO | ||
Method: dip-coating | ||
Flexible substrate: cotton | Gauge factor: 4 Strain range: 11.6% | Reference [39] |
Functional materials: rGO | ||
Method: dip-coating |
Input | Verification | |
---|---|---|
Functional material: carbon nanotubes (CNTs) | Substrate: Flax fabric | Reference [40] |
Gauge factor: 1.24 | ||
Strain range: 120% | ||
Substrate: Polyaniline | Functional material: Silver nanowires | Reference [41] |
Gauge factor: 1 | ||
Strain range: 500% | ||
Strain range: 240% |
Ref. | Main Materials | Strain Range | Sensitivity | Repeatability | Response/Recovery Times | Limit Detection |
---|---|---|---|---|---|---|
[1] | CNTs ink/PU | 350% | 2.7 | 1000 | ~ | ~ |
[6] | CNTs/CNF/PDMS/TPU | 217.5% | 12.7 | 800 | ~ | ~ |
[7] | Silver fillers/LM ink | 170% | ~ | 5000 | ||
[8] | CNTs/latex tube | 200% | 91.1 | 4000 | 290 ms/310 ms | 0.1% |
[15] | CB/PDMS | 250% | 12 | 1000 | ~ | ~ |
[16] | CNTs/CB/PDMS | 80% | 7.7 | 10,000 | 100 ms/110 ms | 0.04% |
[20] | CNTs/LiCl/elastic core-spun yarn | 100% | 1.35 | 1000 | 300 ms | ~ |
[30] | Graphene/PU | 160% | 86.86 | 100 | ~ | ~ |
[31] | LM/TPU | 548% | 6 | 1000 | ~ | ~ |
[32] | CNTs/TPU | 140% | ~ | 1250 | ~ | ~ |
[33] | PAN/graphene/TPU | 2% | 1700 | 300 | ~ | ~ |
[37] | CNTs@carbon black/PDMS | 80% | 7.7 | 10,000 | 2% | |
[40] | CNTs/fabric | 128% | 4.73 | ~ | ~ | ~ |
[42] | MXene/TPU/PAN | 80% | 9.69 | 1750 | 140.6 ms | 0.1% |
[43] | Conductive ink/rubber | 50% | 12.14 | 5000 | 71.43 ms/178.49 ms | ~ |
[44] | CNTs/cotton | 300% | 21.85 | ~ | ~ | ~ |
This work | Graphene/CB/TPU | 300% | 110 | 1000 | 110 ms/112 ms | ~ |
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Ke, J.; Liu, F.; Xu, G.; Liu, M. Data-Driven Strain Sensor Design Based on a Knowledge Graph Framework. Sensors 2024, 24, 5484. https://doi.org/10.3390/s24175484
Ke J, Liu F, Xu G, Liu M. Data-Driven Strain Sensor Design Based on a Knowledge Graph Framework. Sensors. 2024; 24(17):5484. https://doi.org/10.3390/s24175484
Chicago/Turabian StyleKe, Junmin, Furong Liu, Guofeng Xu, and Ming Liu. 2024. "Data-Driven Strain Sensor Design Based on a Knowledge Graph Framework" Sensors 24, no. 17: 5484. https://doi.org/10.3390/s24175484