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However, the setup of machine tools is very complex and needs long time due to the great number of input variables that should be checked and configured.
In fact, the setup of machine adjustable parameters is a central step for any predictive model, whether based on ML or not.
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The setup of the machine is presented.
Experimental setup of EZEECUT NXG machine is shown in Fig. 1.
An experimental setup of the machine tool with an energy consumption monitoring system is constructed to demonstrate the effectiveness of the proposed method.
Then, either the application of double-entry pumps is possible resulting in a calculational reduction of the machine's discharge by 50%% (only one half of the pump's casing is considered), or the setup of various machines in parallel mode can be pursued, whereby single machines can be started and stopped individually depending on the respective discharge (Fig. 8).
For a given tool path and workpiece material, an optimal sequence of feedrate and spindle speed is calculated both for the initial setup of the machining process and for the continuous, in-process adaptation of process parameters to changes the current machining behavior.
The prediction system may include either one (single predictor setup) or an ensemble (multiple predictor setup) of learning machines trained to assess the quality score.
It goes beyond a classic hypervisor (i.e. VirtualBox [ 21], Xen [ 11], VMware [ 12]), and allows the setup of virtual machines dynamically, as computational resources are needed.
It is obvious that route selection cost is dependent on relocation and setup costs of machines.
The setup of the testing machine, jig assembly, and synthetic bone was schematically shown in Figure 2. In the literature, Chapman et al. [ 4] propose an analytical formula to predict the pullout strength of a cancellous screw within synthetic bone.
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