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In this paper, the impact of cutting speed and feed amount on cutting deformation and surface quality and cutter wear have been studied by the test of high manganese steel cutting with metal ceramic tool at high speed. The results show that it is feasible by cutting high manganese steel with cermet tool at high-speed cutting (HSC). Chip changes into succession segment from band in macrostate. Sawteeth can be seen on the top surface of chip while high temperature feature in burning color and cold welding form appear on the bottom of the chip. The model of surface roughness built can be used to predict surface quality at HSC, and provided cutting optimized parameters scheme. Further research indicates that high-speed cutting has its special deformation mechanism and plastic chips exhibit hot brittleness which provides a favorable condition for solving the chip breaking problem in the cutting of high manganese steel.

Based on the processing and analysis of the fixed-point horizontal deformation data across the fault in the middle and southern part of the Tan-Lu fault zone in recent 20 years, this paper preliminarily analyzes the vertical deformation characteristics of the middle and southern section of the Tan-Lu fault. The research shows that the overall activity level of the middle and southern part of the Tan-Lu fault zone is not high, and the average combining rate is 0.19 mm/a. However, it has obvious characteristics of time-sharing activities: from 2000 to 2010, the activity in Huaibai-Suqian section was large, while the activity in Xinyi-Linyi section was relatively small. There was a relatively closed section between Suqian and Xinyi; The overall activity rate has decreased from 2011 to 2019, but the activity rate of Anqiu station has increased significantly compared with that of 2000 to 2011. There is a certain correspondence between the activitycharacteristics of the fault zone and the seismicity of the region i

Chinese painting is always a kind of resource to explore ancient Chinese dynasties and the ideology of painters. Nowadays, more and more Chinese-antique museums have created corresponding animations from Chinese painting for representing daily life of ancient Chinese and providing immersive experience to attract visitors. However, it is not easy to produce animation sequences directly from Chinese paintings. It is laborious and time-consuming to build every motion of animated characters in Chinese painting. Since the irregular borders of characters in Chinese painting are torn and the connectivity of stroke line will be lost while implementing deformation. This research therefore presents a procedure of generating Chinese painting animation sequences to overcome these problems. It focuses on two technical issues: sample point processing and animation generation. Sample point distribution and allocation are the major tasks in sample pint processing. A point distribution method is proposed to preserve connectivity of stroke line of object and a weight scheme is implemented to decide control bone of individual sample points. For the stage of animation, the skeleton-based deformation is firstly used to generate key frames through user-provided skeleton. Then an interpolation method is exploited to create smooth animations. The proposed scheme could also be applied to multiple-character and multiple-skeleton animations.

Super-deformation in character design refers to a simplified modeling of character illustrations that are drawn in detail. Such super-deformation requires both texture and geometrical translation. However, directly adopting conventional image-to-image translation methods for super-deformation is challenging as these methods use a pixel-wise loss which makes the translated images highly dependent on the spatial information of the input image. This study proposes a novel deep architecture-based method for the super-deformation of illustrated character faces using an unpaired dataset of detailed and super-deformed character face images collected from the Internet. First, we created a dataset construction pipeline based on image classification and character face detection using deep learning. Then, we designed a generative adversarial network (GAN) that was trained using two discriminators, each for detailed and super-deformed images, and a single generator, capable of synthesizing identical pairs of characters with different textural and geometrical appearance. As ornaments are an important element in character identification, we further introduced ornament augmentation to enable the generator to synthesize a variety of ornaments on the generated character faces. Finally, we constructed a loss function to project character illustrations provided by the user to the learned GAN latent space, which can find an identical super-deformed version. The experimental results show that compared to baseline methods, the proposed method can successfully translate character illustrations to identical super-deformed versions. The codes are available on the Internet.

Since Chinese characters are composed of components, deforming the components in a small number of existing calligraphy characters to generate new characters is an effective method to produce a Chinese character library in the same style. Usually, the component deformation is achieved by affine transformation. However, when calculating the parameters in affine transformation, existing methods usually have the problems of a large amount of manual participation or complicated calculation. In this paper, we proposed an Analytic Hierarchy Process (AHP)-based Chinese character component deformation method, which is simple in calculation and can effectively realize the deformation of Chinese character components on the basis of reducing manual intervention. We first determined the factors that affect the selection of control points in affine transformation, then used AHP to calculate the weights of feature points and select the control points according to the weights. As a prerequisite for affine transformation, a matching method of Chinese character feature points based on the Chinese character skeleton map and neighborhood information is also proposed, which helps to achieve more efficient deformation. Experimental results on different fonts demonstrate the effectiveness and generality of our method.

The soil pressure can be influenced by the deformation of retaining wall because of the interaction of wall and soil. The dynamic soil pressure and plastic bottom displacement induced by earthquake action will subsequently result the deformation of the wall. The dynamic soil pressure will vary with the wall deformation and, on the other hand, change the deformation of the wall. The wall under lateral earthquake is assumed to be a single freedom system, thus only one deformation mode is considered in this paper. Based on this assumption, the differential equation of wall-soil dynamic interaction system is proposed.

Virtual Reality (VR) games on Head Mounted Displays (HMDs) require exceedingly quick running time to reduce the latency that is the primary cause of motion sickness. An efficient interface is essential when manipulating 3D character deformation in developing games; this paper proposes the synthesis of a physiological feature-driven virtual character deformation. To meet performance demands, a geometric-based deformation method is employed, which takes a physically based Cross Sectional Area (CSA) as an increase parameter. Cross Sectional Area Mapping (CSAM) then maps the CSA into a displacement. The method utilizes manipulation of each segmented body as well as a whole, and runs HMD VR games to allow targeting of emerging applications. In experiments, we show the deformation results of both human characters and digital creatures. The techniques can be extended to a wide range of characters.

This chapter contains sections titled: Introduction General moments under a polynomial transform Explicit and implicit invariants Implicit invariants as a minimization task Numerical experiments Conclusion References

The automatic recognition of labels marked on steel slab surfaces is of significance to information management and intelligent manufacturing in steel plants. However, it is not an easy task due to complex factors like low printing quality, motion distortion and thermal blurring, especially while handling the prone-to-deform dot-matrix labels generated by a hot spray marking (HSM) technique. In this paper, a machine vision system is presented for the HSM dot-matrix label recognition. With a brief description of the imaging system, our emphasis is put on image analysis. First, a coarse-to-fine strategy is applied to locate HSM characters from captured images, where a weighted gravity-center estimation method is extended to search the enclosure of label regions, and an edge projection scheme is adopted to refine the label extraction. Subsequently, a Multidirectional Line Scanning (MLS) method is proposed to determine the boundaries between adjacent dot-matrix characters with tilt, adhesion or dot-missing abnormalities. Finally, by converting the dot-matrix character into a 2D point set, we introduce a Point Cloud registration for DOt-matrix Character (PC4DOC) method to recognize prone-to-deform characters, which appears to accommodate various distortions and abnormalities owing to the inherent deformation correction of affine transformation and fault tolerance of robust correspondence matching. According to our experiments, the proposed method can achieve real-time recognition with an accuracy of 93.84% in spite of severely degraded images and incomplete characters. The system has been installed and run in a steel mill for more than one year, and its stability was also verified.

Grain boundary engineering (GBE) is considered to be an attractive approach to microstructure control, which significantly enhances the grain-boundary-related properties of face-centered cubic (FCC) metals. During the twinning-related GBE, the microstructures are characterized as abundant special twin boundaries that sufficiently disrupt the connectivity of the random boundary network. However, controlling the grain boundary character distribution (GBCD) is an extremely difficult issue, as it strongly depends on diverse processing parameters. This article provides a comprehensive review of controlling GBCD during the twinning-related GBE of FCC materials. To commence, this review elaborates on the theory of twinning-related GBE, the microscopic mechanisms used in the optimization of GBCD, and the optimization objectives of GBCD. Aiming to achieve control over the GBCD, the influence of the initial microstructure, thermo-mechanical processing (TMP) routes, and thermal deformation parameters on the twinning-related microstructures and associated evolution mechanisms are discussed thoroughly. Especially, the development of twinning-related kinetics models for predicting the evolution of twin density is highlighted. Furthermore, this review addresses the applications of twinning-related GBE in enhancing the mechanical properties and corrosion resistance of FCC materials. Finally, future prospects in terms of controlling the GBCD during twinning-related GBE are proposed. This study will contribute to optimizing the GBCD and designing GBE routes for better grain-boundary-related properties in terms of FCC materials.