Three-Dimensional Cellular Automata For Simulation Of Microstructure Evolution During Recrystallization

In this paper, we study 2-dimensional nite cellular automata dened by hexagonal local rule with periodic boundary over the eld Z3.We construct the rule matrix corresponding to the hexagonal cellular automata.For some given coecients and the number of columns of hexagonal information matrix, we prove that the hexagonal cellular automata are reversible.

Investigation on dynamic recrystallization using a modified cellular automaton

Dynamic recrystallization (DRX) takes place when FeMnSiCrNi shape memory alloy (SMA) is subjected to compression deformation at high temperatures. Cellular automaton (CA) simulation was used for revealing the DRX mechanism of FeMnSiCrNi SMA by predicting microstructures, grain size, flow stress, and dislocation density. The DRX of FeMnSiCrNi SMA has a characteristic of repeated nucleation and finite growth. The size of recrystallized grains increases with increasing deformation temperatures, but it decreases with increasing strain rates. The increase of deformation temperature leads to the decrease of the flow stress, whereas the increase in strain rate results in the increase of the flow stress. The dislocation density exhibits the same situation as the flow stress. The simulated results were supported by the experimental ones very well. Dislocation density is a crucial factor during DRX of FeMnSiCrNi SMA. It affects not only the nucleation but also the growth of the recrystallized grains. Occurrence of DRX depends on a critical dislocation density. The difference between the dislocation densities of the recrystallized and original grains becomes the driving force for the growth of the recrystallized grains, which lays a solid foundation for the recrystallized grains growing repeatedly.

Cellular Automata (CA) is used in pseudo-random number generators (PRNG) to produce high-rate random numbers. The randomness of such numbers directly depends upon the CA rules and the number of neighbor cells. The two-dimensional (2D) CA has several limitations such as, finding the best CA rules, boundary cell problems, etc. To address the problems existing in the 2D CA, we propose a random number generator based on 3D cellular automata. The proposed generator is based on rule numbers 43, 85, 170, and 201, and incremental boundary conditions. The rules are randomly changed after each cycle depending on a clock bit mechanism. The output bits are then passed to the Diehard and ENT test suites to test its randomness [1]. The results reveal that the bit stream generated by the proposed scheme passes all the tests present in the test suites.

Residual stress is an important mechanical property parameter and a key evaluation index of the surface integrity. Residual stress induced in micro-grinding is related to the process parameters, the wheel properties, and material microstructure. The micro-grinding process is distinct from the conventional grinding owing to size effect, and the crystal effect. Typically, the effects of crystallographic orientation (CO) on process behaviors are significant by influencing the flow stress of workpiece material due to the depth of cut is usually less than the average grain size. In this paper, a Taylor factor model is developed to estimate the effect of CO on the flow stress and plastic modulus of monocrystalline materials. The flow stress model takes into account strain, strain rate, temperature, and material microstructure including grain size, CO, and dislocation density. The plastic modulus of monocrystalline materials h is given as the function of hardness, strain, and microstructure. The analytical model of residual stress is proposed considering the effects of CO. The hybrid McDowell algorithm is used to predict the residual stress with the developed plastic modulus. Finally, the sensitivity analysis is carried out, with the result showing that the effect of CO on residual stress is significant.

The evolution of the microstructure in AISI 304L stainless steel during the flat rolling – modeling by frontal cellular automata and verification

Cellular automaton simulation of dynamic recrystallization behavior in V-10Cr-5Ti alloy under hot deformation conditions

Materials which form the surface and subcutaneous layers of an extrudate experience large deformations when they traverse the die land. This, when added to the inhomogeneity caused by the dead metal zone, leads to considerable modifications to the deformation parameters when compared to the remainder of the extrusion. The distribution of structure is therefore greatly inhomogeneous. Reference to both empirical and physical models of the recrystallisation process indicates that nucleation and growth will differ at these locations in those aluminium alloys that are usually solution treated and aged subsequent to the deformation process. Since static recrystallisation has a significant influence on many of the properties of the extrudate, it is therefore essential to provide the methodology to predict these variations. In the work presented, a physical model, for AA2024, based on dislocation density, subgrain size and misorientation is modified and integrated into the commercial finite element method (FEM) code, FORGE, to study the microstructure changes. Axi-symmetrical and shape extrusion are presented as examples. The evolution of the substructure influencing static recrystallisation is studied. The predicted results show an agreement with the experimental measurement. The distribution of equivalent strain, temperature compensated strain rate and temperatures are also presented to aid interpretation. Importantly the properties of hard alloys improve as the temperature of the extrusion is raised. This phenomenon is discussed and theoretically justified. This paper also presents some innovative work where the physically based models, and the Cellular Automata (CA) method, are combined to simulate the static recrystallisation process. The FEM is adopted to provide the initial morphology and state variables for the structure models, such as the equivalent strain, the temperature and the equivalent strain rate. The subgrain size, and dislocation densities are calculated from physically based models and are transferred to CA models to construct the data required to define the initial state for recrystallisation. Simulation results are compared with experimental measurements. It is demonstrated that CA integrated with the physically based models is effective in predicting the structural changes by selecting a suitable neighbourhood and reasonable transition rules.

The non-orthogonal multiple access-passive optical network (NOMA-PON) is facing the dual security threats of primary user interference and unauthorized third-party user eavesdropping, so efficient data security enhancement techniques are crucial. To solve these problems, we propose a fixed-point extended (FE)-logistic chaotic mapping to reduce the computational complexity while employing a two-dimensional (2D) cellular automata (CA) and Turing pattern (TP) cascading scramble (CA-TPCS) encryption algorithm to further improve the sensitivity of the NOMA-PON system. The CA-TPCS consists of 2D-CA dynamic bit encryption and Turing symbol substitution (TSS). By using FE chaos to construct 2D-CA and adopting index sort to extract the TSS matrix, dynamic diffusion of bits and scrambling of a 2D symbol matrix are achieved. To ensure the key privacy, we employ a dual key mechanism, and uplink data is introduced as the private key. To verify the feasibility of the proposed method, a simulation validation is built on a 17.6 Gb/s power division multiplexing-orthogonal frequency division multiplexing (PDM-OFDM) NOMA-PON system transmitted over 25 km standard single mode fiber (SSMF). The results show that the proposed scheme has no effect on the optimal power allocation rate (PAR) values and the values are all 3. Meanwhile, the receiver sensitivity gains of 0.2 and 0.3 dB are obtained for high-power and low-power users after encryption. The ciphertext has good diffusion and statistical properties, and the key space is flexibly controlled by the FE precision f, the length l of the transmitted bit, and the size T of the TP, with the value of 22f+l+T×T. The results show that the proposed scheme is not only very compatible with PDM technology but also can realize the dual defense of internal aggression and external aggression. It has a good application prospect in the future NOMA-PON.

In order to research the hot deformation behavior of 9CrMoCoB heat-resistant steel, hot compression tests were performed over a wide range of temperatures from 850 to 1150 °C and strain rates from 0.01 to 10.00 s−1. The flow stress appears to increase with the decrease in deformation temperature and the increase in strain rate. The relationship between microstructural evolution and deformation parameters was studied, indicating that both low strain rate and high deformation temperature appear to promote the dynamic recrystallization, while excessively high temperature with low strain rate would result in the high non-uniformity of grain size. The experimental stress–strain data was applied to calculate the material constants involved in the Arrhenius-type constitutive model and the modified Zerilli-Armstrong (MZA) model, and feasibility of these two models was evaluated. The results show that the MZA model is more accurate to predict the high-temperature flow behavior of the experimental steel than the Arrhenius-type constitutive equation.

Formation mechanism of large grains inside annealed microstructure of GH4169 superalloy by cellular automation method

Feature Extraction is a long active topic in image processing, and it attracts lots of attention over the last five decades due to its uses in real-life applications. Edge detection method allows users to discover the features of an image for a notable change in the gray level. This texture indicates an ending of one region and the beginning of another in the image. Likewise, over the last few decades, researchers have been exploring to exploit the simple computing model of Cellular Automata (CA) with local neighborhood structure in image processing techniques. The usage of CA in applications of medical image-processing is identified by Kendall Preston [1] in 1979. It is found that edge detection using CA has a potential advantage over traditional approaches for its lightweight nature and computational efficiency. This paper introduces a new technique for image edge detection using two-dimensional CA. The method can use specific rule sets of CA using the Von Neumann neighborhood for edge detection with null boundary conditions. The performance analysis of the scheme is done and compared with some existing standard edge detection techniques. The results obtained from the proposed technique is very promising for feature extraction.

An approach based on an application of cellular automata (CA) to the problem of two-dimensional (2D) patterns or images reconstruction from ones with only partial information available is presented in the paper. 2D CA are used to process patterns/images, and genetic algorithm (GA) is applied to discover CA rules, which will be able to reconstruct original patterns/images from, e.g. destroyed or modified ones. A number of experiments have been conducted to reconstruct patterns and human face images with use of the proposed approach. Results of experiments show that CA rules discovered by GA in the learning process allow to reconstruct images with large number of damaged pixels.

In the current study, a new dynamic recrystallization model for predicting high temperature flow stress is developed based on a physical model and the mean field theory. In the model, the grain aggregate is assumed as a representative volume element to describe dynamic recrystallization. The flow stress and microstructure during dynamic recrystallization were calculated using three sub-models for work hardening, for nucleation and for growth. In the case of work hardening, a single parameter dislocation density model was used to calculate change of dislocation density and stress in the grains. For modeling nucleation, the nucleation criterion developed was based on the grain boundary bulge mechanism and a constant nucleation rate was assumed. Conventional rate theory was used for describing growth. The flow stress behavior of pure copper was investigated using the model and compared with experimental findings. Simulated results by cellular automata were used for validating the model.

Cellular automata (CA) are known for their capability in exhibiting interesting emergent behaviour and capacity to generate complex and often aesthetically appealing patterns through the local interaction of rules. Mean information gain has been suggested as a measure of discriminating structurally different two-dimensional (2D) patterns. This paper addresses quantitative evaluation of the complexity of CA generated configurations. In particular, we examine information gain as a spatial complexity measure for discriminating multi-state 2D CA generated configurations. This information-theoretic quantity, also known as conditional entropy, takes into account conditional and joint probabilities of cell states in a 2D plane. The effectiveness of the measure is shown in a series of experiments for multi-state 2D patterns generated by CA. The results of the experiments show that the measure is capable of distinguishing the structural characteristics including symmetries and randomness of 2D CA patterns.