Rationalizing energy consumption in the Electrical Discharge Machining (EDM) efficiently saves energy and improves machining quality. Since the conventional methods for estimating useful EDM energy are based on theoretical heat transfer studies or empirical assessments of processing conditions, the development of an industrially applicable method for assessing useful energy is an important problem. Here we show that the performance of the EDM process is directly related to acoustic emission (AE). The effectiveness of the proposed method has been evaluated in experiments. As part of the execution of the experiment, AlCuMg1 workpiece was machined using a copper electrode with different duty cycles with pulse widths varying from 10% to 80%. For comparative analysis, the root-mean-square vibroacoustic signal in the range of 1-10 kHz and the root-mean-square of the discharge current were used. It was found that the amplitude of the vibroacoustic (VA) signal monotonically increases with the increasing EDM performance. The properties of the VA signal allows using the VA monitoring to assess the performance of EDM, i.e., to determine the fraction of energy spent on removing the workpiece material. The advantage of the proposed method of monitoring is that the control of useful energy is carried out using accelerometers installed on the parts of the technological system on the workpiece side. The distance from accelerometers to the workpiece being processed can be quite large that is convenient for performing experiments. In particular, in the high frequencies range, the obtained results are protected from mechanical interference coming from drives, hydraulic units and wire rewinding mechanisms. Such VA signals are shown to be important indicators of EDM efficiency because they are observed only if the energy fluxes reach the workpiece surface. This provides a more reliable indication of raising concentrations of electroerosion products that prevents short circuits and breakage of wire electrodes.
A new method for determining surface roughness based on improving the kinematics of the milling cutter movement during micro-cutting has the advantage of the precise spatial position of the micro cutter edge. A change in the components of the speed of movement and rotation during a complex movement of the cutter changes the mechanism of plunging of the cutting edge into the workpiece material. Based on the model developed in this work, the kinematic parameters of the cutter were determined, and new relationships between the cutter geometry and parameters of the technological process were discovered. The revealed new relationships made it possible to determine not only the mechanism of chip formation but also the dimensions of damages to the workpiece surface during plunger cutting.
KEYWORDS: Robotics, Computing systems, Field programmable gate arrays, Robots, Neural networks, System on a chip, Integration, Energy efficiency, Graphics processing units, Application specific integrated circuits
The article discusses a heterogeneous processor based on an open source 64-bit core of the RISC-V architecture, combined with a reconfigurable neural network accelerator. The features of the implementation of a binary matrix neural network on FPGA and its combination with the RISC-V RV64GC core in tasks of cognitive robotics and industrial production are investigated in order to increase safety in the interaction of a robot and a person.
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