Digital twin-driven modeling and application of carbon emission for machine tool

As the "mother machine" of manufacturing, machine tools have been widely employed, but with high energy consumption, low energy efficiency, and serious carbon emissions. How to reveal the dynamic characteristics of carbon emissions is of great significance for machine tools to achieve green and sustainable development. Existing research neglected the dynamic characteristics of carbon emissions and focused on a single phase for machine tool. To this end, this study carried out the digital twin-driven modeling and application of carbon emission for machine tool considering both the design and usage stages. Utilizing real-time data acquisition and model refinement of digital twin system, this research proposed an adaptive algorithm for the dynamic prediction and optimization of processing parameters. Related management services in usage stage based on digital twin was developed and a digital twin-driven carbon emission prototype system was established. And a milling experiment was performed to validate the practicality and feasibility of the proposed algorithm. Besides, combining with operation data recorded by digital twin model, working conditions of machine tool were simulated in the digital space. Optimization of tool part selection under simulated conditions can be realized during design stage, which makes the closed-loop design for machine tool. Considering both design and usage stages, this research offers a reference solution for the multidimensional, intelligent, and collaborative management of carbon emissions for machine tool.