TY  - JOUR
AU  - Ding, Yuxing
PY  - 2026
DA  - 2026/06/29
TI  - Digital Twin Production Lines: A Perspective on Intelligent Sensing, Prediction, and Cognitive Manufacturing
JO  - ICCK Transactions on Intelligent Cyber-Physical Systems
T2  - ICCK Transactions on Intelligent Cyber-Physical Systems
JF  - ICCK Transactions on Intelligent Cyber-Physical Systems
VL  - 1
IS  - 2
SP  - 83
EP  - 85
DO  - 10.62762/TICPS.2026.636441
UR  - https://www.icck.org/article/abs/TICPS.2026.636441
KW  - digital twin
KW  - multi-dimensional data learning
KW  - predictive maintenance
KW  - YOLOv10
KW  - industry 5.0
AB  - Digital twin (DT) technology has evolved beyond static physical mapping into a dynamic intelligence layer for smart manufacturing. We argue that three capabilities are now decisive for advancing DT production lines in complex industrial environments: data-driven quality correction through nonlinear feature learning, real-time edge vision for surface defect sensing, and deep time-series modeling for predictive maintenance. We further contend that the next inflection point lies not in individual algorithmic improvements, but in the cognitive integration of structured engineering knowledge-such as Manufacturer Knowledge Packages (MKP) and the Theory of Inventive Problem Solving (TRIZ)-with generative AI within DT frameworks. This perspective identifies the critical open challenges at each layer and outlines a convergent path toward Industry~5.0 manufacturing systems capable of autonomous conflict resolution and adaptive decision-making.
SN  - 3071-2947
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Ding2026Digital,
  author = {Yuxing Ding},
  title = {Digital Twin Production Lines: A Perspective on Intelligent Sensing, Prediction, and Cognitive Manufacturing},
  journal = {ICCK Transactions on Intelligent Cyber-Physical Systems},
  year = {2026},
  volume = {1},
  number = {2},
  pages = {83-85},
  doi = {10.62762/TICPS.2026.636441},
  url = {https://www.icck.org/article/abs/TICPS.2026.636441},
  abstract = {Digital twin (DT) technology has evolved beyond static physical mapping into a dynamic intelligence layer for smart manufacturing. We argue that three capabilities are now decisive for advancing DT production lines in complex industrial environments: data-driven quality correction through nonlinear feature learning, real-time edge vision for surface defect sensing, and deep time-series modeling for predictive maintenance. We further contend that the next inflection point lies not in individual algorithmic improvements, but in the cognitive integration of structured engineering knowledge-such as Manufacturer Knowledge Packages (MKP) and the Theory of Inventive Problem Solving (TRIZ)-with generative AI within DT frameworks. This perspective identifies the critical open challenges at each layer and outlines a convergent path toward Industry~5.0 manufacturing systems capable of autonomous conflict resolution and adaptive decision-making.},
  keywords = {digital twin, multi-dimensional data learning, predictive maintenance, YOLOv10, industry 5.0},
  issn = {3071-2947},
  publisher = {Institute of Central Computation and Knowledge}
}