A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin

By Weichao Luo, Tianliang Hu, Yingxin Ye, Chengrui Zhang, Yongli Wei. Robotics Comput. Integr. Manuf., 2020

457 citations recorded by the source corpus.

Abstract As a typical manufacturing equipment, CNC machine tool (CNCMT) is the mother machine of industry. Fault of CNCMT might cause the loss of precision and affect the production if troubleshooting is not timely. Therefore, the reliability of CNCMT has a big significance. Predictive maintenance is an effective method to avoid faults and casualties. Due to less consideration of the status variety and consistency of CNCMT in its life cycle, current methods cannot achieve accurate, timely and intelligent results. To realize reliable predictive maintenance of CNCMT, a hybrid approach driven by Digital Twin (DT) is studied. This approach is DT model-based and DT data-driven hybrid. With the proposed framework, a hybrid predictive maintenance algorithm based on DT model and DT data is researched. At last, a case study on cutting tool life prediction is conducted. The result shows that the proposed method is feasible and more accurate than single approach.

Subfields: Industrial and Manufacturing Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/5608f8c0347ce1f282f8fa1b7a60c1b9c0c48c85

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In Industrial and Manufacturing Engineering

In Mechanical Engineering

Citations in the corpus

Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects (2022, methodology): “In this context, Luo et al. [171] have proposed a DT-based PdM scheme that uses physics-based degradation and simulation models to generate theoretical baselines for the machine state, as well as data-centric models that consume real-time streams of data from the sensors installed in the machine.”
Predictive maintenance using digital twins: A systematic literature review (2022, methodology): “10, we see that, in total, three studies used such a Hybrid approach [38, 40, 44].”
A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, background): “2019) and machine tools (Luo et al. 2020) on a production floor, the material properties and structural health parameters of an unmanned aerial vehicle (UAV) (Kapteyn et al.”
Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0 (2020, background): “Luo, W. et al. [79] employed predictive maintenance approach for machine tool driven by digital twin to avoid faults and causalities.”
A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics (2021, methodology): “Besides statistical [140,141] and hybrid modeling [142] approaches, DL models for time-series forecasting, such as LSTM [144–146], were adopted in numerous studies to estimate wear status and equipment utilization.”