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Then, the established thermal error prediction model is applied on the CNC machine tool to make error compensation at the tool center point (TCP) through the real-time CNC data and temperature data (shown in Figure 1(b)).
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CNC undertook clinical data analysis.
This information is obtained from the CNC code and history data.
With separated data layers, CNC can be applied in some data layers even though the overall data rates (or equivalently the transmission capacities) of the involved unicast sessions are not equal.
Overall these data show that CNCs have the capacity to make extensive interactions with loci on other chromosomes.
The layered CNC-FB architecture is proposed, which simplifies the design of a CNC machine controller with the architecture layers responsible for data processing, data storage and execution.
It results in reducing data processing time in CNC enhance the manufacturing performance.
However, CNC may affect the reliability of data in each unicast flow, especially in lossy wireless mesh networks, since the success of a unicast session now depends on another unicast session involved in CNC.
Nevertheless, we also define dense CNCs (d-CNCs), which are not porous, amorphous CNCs (a-CNCs), as well as flexible CNCs (f-CNCs) and rigid CNCs (r-CNCs).
The data provide evidence that CNCs are capable of interacting with loci that are enriched for CNCs.
Unfortunately, there are no experimental data reported for ultrathin CNCs with diameters down to several nanometers.
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