Study of machining indentations over the entire surface of a target ball using the force modulation approach

A modified five-axis cutting system using a force control cutting strategy was to machine indentations in different annuli on the entire surface of a target ball.The relationship between the cutting depths and the applied load as well as the microsphere rotation speed were studied experimentally to reveal the micromachining mechanism.In particular,aligning the rotating center of the high precision spindle with the microsphere center is essential for guaranteeing the machining accuracy of indentations.The distance between adjacent indentations on the same annulus and the vertical distance between adjacent annuli were determined by the rotating speed of the micro-ball and the controllable movement of the high-precision stage,respectively.In order to verify the feasibility and effect of the proposed cutting strategy,indentations with constant and expected depths were conducted on the entire surface of a hollow thin-walled micro-ball with a diameter of 1 mm.The results imply that this machining methodology has the potential to provide the target ball with desired modulated defects for simulating the inertial confinement fusion implosion experiment.

Fabrication of Ordered Micro/Nanostructures Using Probe‑Based Force‑Controlled Micromachining System

This paper presents a probe-based force-controlled nanoindentation method to fabricate ordered micro/nanostructures.Both the experimental and finite element simulation approaches are employed to investigate the influence of the interval between the adjacent indentations and the rotation angle of the probe on the formed micro/nanostructures.The non-contacting part between indenter and the sample material and the height of the material pile-up are two competing factors to determine the depth relationship between the adjacent indentations.For the one array indentations,nanostructures with good depth consistency and periodicity can be formed after the depth of the indentation becoming stable,and the variation of the rotation angle results in the large difference between the morphology of the formed nanostructures at the bottom of the one array indentation.In addition,for the indentation arrays,the nanostructures with good consistency and periodicity of the shape and depth can be generated with the spacing greater than 1μm.Finally,Raman tests are also carried out based on the obtained ordered micro/nanostructures with Rhodamine probe molecule.The indentation arrays with a smaller spacing lead to better the enhancement effect of the substrate,which has the potential applications in the fields of biological or chemical molecular detection.

A Simulated Investigation of Ductile Response of GaAs in Single-Point Diamond Turning and Experimental Validation

In this paper,molecular dynamic(MD)simulation was adopted to study the ductile response of single-crystal GaAs during single-point diamond turning(SPDT).The variations of cutting temperature,coordination number,and cutting forces were revealed through MD simulations.SPDT experiment was also carried out to qualitatively validate MD simulation model from the aspects of normal cutting force.The simulation results show that the fundamental reason for ductile response of GaAs during SPDT is phase transition from a perfect zinc blende structure(GaAs-I)to a rock-salt structure(GaAs-II)under high pressure.Finally,a strong anisotropic machinability of GaAs was also found through MD simulations.

Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and unary classification

Intelligent tpower systems scanimprove operational efficiency by installing a large number of sensors.Data-based methods of supervised learning have gained popularity because of available Big Data and computing resources.However,the common paradigm of the loss function in supervised learning requires large amounts of labeled data and cannot process unlabeled data.The scarcity of fault data and a large amount of normal data in practical use pose great challenges to fault detection algorithms.Moreover,sensor data faults in power systems are dynamically changing and pose another challenge.Therefore,a fault detection method based on self-supervised feature learning was proposed to address the above two challenges.First,self-supervised learning was employed to extract features under various working conditions only using large amounts of normal data.The self-supervised representation learning uses a sequence-based Triplet Loss.The extracted features of large amounts of normal data are then fed into a unary classifier.The proposed method is validated on exhaust gas temperatures(EGTs)of a real-world 9F gas turbine with sudden,progressive,and hybrid faults.A comprehensive comparison study was also conducted with various feature extractors and unary classifiers.The results show that the proposed method can achieve a relatively high recall for all kinds of typical faults.The model can detect progressive faults very quickly and achieve improved results for comparison without feature extractors in terms ofF1 score.