A Model for Mechanical Property Evaluation of the Periodic Porous Low-k Materials by SAW

The influence of the distribution of nano-pores on the mechanical properties evaluation of porous low-k films by surface acoustic waves （SAW） is studied. A theoretical SAW propagation model is set up to characterize the periodic porous dielectrics by transversely isotropic symmetry. The theoretical deductions of SAW propagating in the low-k film/Si substrate layered structure are given in detail. The dispersive characteristics of SAW in differ- ent propagation directions and the effects of the Young＇s moduli E, E′ and shear modulus G′ of the films on these dispersive curves are found. Computational results show that E′ and G′ cannot be measured along the propagation direction that is perpendicular to the nano-pores＇ direction.

Deep Learning Applied to Computational Mechanics:A Comprehensive Review,State of the Art,and the Classics

Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.

On microstructure and performance of tempered high-boron high-speed steel roll

Influences of the tempering temperature on the microstructure, mechanical property and wear resistance of High-Boron High Speed Steel （HBHSS） roll materials were investigated by means of optical microscopy, scanning electron microscopy （SEM）, X-ray diffraction, hardness measurement, impact tester, tensile tester and pin abrasion tester. The results show that the as-cast structure of HBHSS consists of a great amount of martensite and M2（B,C） and a few retained austenites and M23（B,C）6. After solution treated at 1,050℃ and followed by oil cooling, the amount of M23（B,C）6 carbo-borides in quenched HBHSS increases obviously and the macrohardness of the quenched HBHSS is 66 HRC, which is very close to the 65.8 HRC of as-cast HBHSS. On the whole, the hardness of HBHSS alloy shows a trend of slight decrease with increasing tempering temperature when tempered below 500 ℃. While when above 500 ℃, the hardness increases slightly as the tempering temperature increases and reaches a peak at 525 ℃ and then decreases obviously. The impact toughness of HBHSS has a tendency to increase as the tempering temperature increases. Tempering can improve the tensile strength and elongation of HBHSS, but a higher tempering temperature causes a slight decrease in both tensile strength and elongation. Excellent wear resistance can be obtained by tempering at 500 to 550 ℃.