An Analytical Model of Residual Stress for Flank Milling of Ti-6Al-4V

AbstractResidual stress is one of the most critical parameters in surface integrity, which has a great impact on fatigue life of the machined components. While the flank milling of titanium alloy Ti-6Al-4V has been widely applied to the manufacture of jet engine for its high productivity in aerospace industry, prediction of residual stress induced by this process is seldom reported. In this paper, an analytical model of residual stress is proposed, based on comprehensive analysis of the mechanical loading during flank milling. For the first time, the sequential discontinuous variable loading feature of flank milling is taken into consideration. An incremental elasto-plastic method followed by a relaxation procedure is used to get the stress-strain history of an arbitrary point in the subsurface so as to predict the residual stress retained in the workpiece after several loading cycles. We find that during the last phase in which the machined surface is generated, the main load comes from the plough effect of cutting edge as the uncut depth approaches zero. The simulation results indicate that the flank milled surface shows more compressive residual stress in the axial direction than in the feed direction. To validate the prediction, a series of cutting tests are conducted on Ti-6Al-4V using finish parameters and X-ray diffraction is utilized to obtain the residual stress

Towards Robust Dataset Learning

Adversarial training has been actively studied in recent computer vision research to improve the robustness of models. However, due to the huge computational cost of generating adversarial samples, adversarial training methods are often slow. In this paper, we study the problem of learning a robust dataset such that any classifier naturally trained on the dataset is adversarially robust. Such a dataset benefits the downstream tasks as natural training is much faster than adversarial training, and demonstrates that the desired property of robustness is transferable between models and data. In this work, we propose a principled, tri-level optimization to formulate the robust dataset learning problem. We show that, under an abstraction model that characterizes robust vs. non-robust features, the proposed method provably learns a robust dataset. Extensive experiments on MNIST, CIFAR10, and TinyImageNet demostrate the effectiveness of our algorithm with different network initializations and architectures

WuYun: Exploring hierarchical skeleton-guided melody generation using knowledge-enhanced deep learning

Although deep learning has revolutionized music generation, existing methods for structured melody generation follow an end-to-end left-to-right note-by-note generative paradigm and treat each note equally. Here, we present WuYun, a knowledge-enhanced deep learning architecture for improving the structure of generated melodies, which first generates the most structurally important notes to construct a melodic skeleton and subsequently infills it with dynamically decorative notes into a full-fledged melody. Specifically, we use music domain knowledge to extract melodic skeletons and employ sequence learning to reconstruct them, which serve as additional knowledge to provide auxiliary guidance for the melody generation process. We demonstrate that WuYun can generate melodies with better long-term structure and musicality and outperforms other state-of-the-art methods by 0.51 on average on all subjective evaluation metrics. Our study provides a multidisciplinary lens to design melodic hierarchical structures and bridge the gap between data-driven and knowledge-based approaches for numerous music generation tasks

MelodyGLM: Multi-task Pre-training for Symbolic Melody Generation

Pre-trained language models have achieved impressive results in various music understanding and generation tasks. However, existing pre-training methods for symbolic melody generation struggle to capture multi-scale, multi-dimensional structural information in note sequences, due to the domain knowledge discrepancy between text and music. Moreover, the lack of available large-scale symbolic melody datasets limits the pre-training improvement. In this paper, we propose MelodyGLM, a multi-task pre-training framework for generating melodies with long-term structure. We design the melodic n-gram and long span sampling strategies to create local and global blank infilling tasks for modeling the local and global structures in melodies. Specifically, we incorporate pitch n-grams, rhythm n-grams, and their combined n-grams into the melodic n-gram blank infilling tasks for modeling the multi-dimensional structures in melodies. To this end, we have constructed a large-scale symbolic melody dataset, MelodyNet, containing more than 0.4 million melody pieces. MelodyNet is utilized for large-scale pre-training and domain-specific n-gram lexicon construction. Both subjective and objective evaluations demonstrate that MelodyGLM surpasses the standard and previous pre-training methods. In particular, subjective evaluations show that, on the melody continuation task, MelodyGLM gains average improvements of 0.82, 0.87, 0.78, and 0.94 in consistency, rhythmicity, structure, and overall quality, respectively. Notably, MelodyGLM nearly matches the quality of human-composed melodies on the melody inpainting task

A Sun-Tracking Algorithm for Satellite-Borne Spectrometers Based on the Orbital Motion Model

To guarantee that a spectrometer can obtain effective solar spectral data, it is important that the two-dimensional turntable that carries the spectrometer tracks the sun with high accuracy for long time periods, such that the sun is always near the center of the field of view of the spectrometer. However, there is an offset lag problem for the tracking sensor, leading to an increase in the sun-tracking error and inaccuracies in solar spectrum data. To mitigate this problem, an on-axis tracking control algorithm—based on the orbital motion model—is proposed in this paper. First, the sun-tracking model of the spectrometer is established based on the coordinate transformation method, and the analytical relationship between the adjustment angle of the turntable and the solar vector in the orbit system is given by means of the inverse kinematics solution of the target model. Then, the predictive filtering algorithm of the target model is derived to acquire the target position, based on which, the on-axis tracking control algorithm of the spectrometer system is realized to compensate the offset lag and increase the sun-tracking accuracy. Finally, the simulation analysis and experimental verification were performed under the actual working conditions of an orbit. The simulation results demonstrate that the root-mean-square (RMS) of the target position deviation decreased from 2.08″ to 0.77″ after prediction filtering, and the RMS of the tracking error decreased from 7.14″ to 0.97″. The RMS of an orbit’s sun-tracking error decreased from 5.72″ to 1.43″ in the ground test. The simulation and experimental results verify that the algorithm proposed in this paper can improve the tracking accuracy of the spectrometer, providing a reference for the design of a spectrometer in orbit

A Sun-Tracking Algorithm for Satellite-Borne Spectrometers Based on the Orbital Motion Model

To guarantee that a spectrometer can obtain effective solar spectral data, it is important that the two-dimensional turntable that carries the spectrometer tracks the sun with high accuracy for long time periods, such that the sun is always near the center of the field of view of the spectrometer. However, there is an offset lag problem for the tracking sensor, leading to an increase in the sun-tracking error and inaccuracies in solar spectrum data. To mitigate this problem, an on-axis tracking control algorithm—based on the orbital motion model—is proposed in this paper. First, the sun-tracking model of the spectrometer is established based on the coordinate transformation method, and the analytical relationship between the adjustment angle of the turntable and the solar vector in the orbit system is given by means of the inverse kinematics solution of the target model. Then, the predictive filtering algorithm of the target model is derived to acquire the target position, based on which, the on-axis tracking control algorithm of the spectrometer system is realized to compensate the offset lag and increase the sun-tracking accuracy. Finally, the simulation analysis and experimental verification were performed under the actual working conditions of an orbit. The simulation results demonstrate that the root-mean-square (RMS) of the target position deviation decreased from 2.08″ to 0.77″ after prediction filtering, and the RMS of the tracking error decreased from 7.14″ to 0.97″. The RMS of an orbit’s sun-tracking error decreased from 5.72″ to 1.43″ in the ground test. The simulation and experimental results verify that the algorithm proposed in this paper can improve the tracking accuracy of the spectrometer, providing a reference for the design of a spectrometer in orbit

Laser-Induced Point Defects in Fused Silica Irradiated by UV Laser in Vacuum

High-purity fused silica irradiated by third harmonic of the Nd:YAG laser in vacuum with different laser pulse parameters was studied experimentally. Laser-induced defects are investigated by UV spectroscopy, and fluorescence spectra and correlated to the structural modifications in the glass matrix through Raman spectroscopy. Results show that, for laser fluence below laser-induced damage threshold (LIDT), the absorbance and intensity of fluorescence bands increase with laser energies and/or number of laser pulses, which indicates that laser-induced defects are enhanced by laser energies and/or number of laser pulses in vacuum. The optical properties of these point defects were discussed in detail

Laser-Induced Point Defects in Fused Silica Irradiated by UV Laser in Vacuum

High-purity fused silica irradiated by third harmonic of the Nd:YAG laser in vacuum with different laser pulse parameters was studied experimentally. Laser-induced defects are investigated by UV spectroscopy, and fluorescence spectra and correlated to the structural modifications in the glass matrix through Raman spectroscopy. Results show that, for laser fluence below laser-induced damage threshold (LIDT), the absorbance and intensity of fluorescence bands increase with laser energies and/or number of laser pulses, which indicates that laser-induced defects are enhanced by laser energies and/or number of laser pulses in vacuum. The optical properties of these point defects were discussed in detail