Multi-objective optimization of steel AISI 1040 dry turning using genetic algorithm

Neural Computing and Applications - This study investigated the AISI 1040 steel turning in dry environment with four cutting inserts of different corner radii coated by CVD method. Experimental...     

Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel

In the present investigation, three different type of support vector machines (SVMs) tools such as least square SVM (LS-SVM), Spider SVM and SVM-KM and an artificial neural network (ANN) model were developed to estimate the surface roughness values of AISI 304 austenitic stainless steel in CNC turning operation. In the development of predictive models, turning parameters of cutting speed, feed rate and depth of cut were considered as model variables. For this purpose, a three-level full factorial design of experiments (DOE) method was used to collect surface roughness values. A feedforward neural network based on backpropagation algorithm was a multilayered architecture made up of 15 hidden neurons placed between input and output layers. The prediction results showed that the all used SVMs results were better than ANN with high correlations between the prediction and experimentally measured values.     

Prediction of surface roughness during hard turning of AISI 4340 steel (69 HRC)

In this study, 39 sets of hard turning (HT) experimental trials were performed on a Mori-Seiki SL-25Y (4-axis) computer numerical controlled (CNC) lathe to study the effect of cutting parameters in influencing the machined surface roughness. In all the trials, AISI 4340 steel workpiece (hardened up to 69 HRC) was machined with a commercially available CBN insert (Warren Tooling Limited, UK) under dry conditions. The surface topography of the machined samples was examined by using a white light interferometer and a reconfirmation of measurement was done using a Form Talysurf. The machining outcome was used as an input to develop various regression models to predict the average machined surface roughness on this material. Three regression models – Multiple regression, Random forest, and Quantile regression were applied to the experimental outcomes. To the best of the authors’ knowledge, this paper is the first to apply random forest or quantile regression techniques to the machining domain. The performance of these models was compared to ascertain how feed, depth of cut, and spindle speed affect surface roughness and finally to obtain a mathematical equation correlating these variables. It was concluded that the random forest regression model is a superior choice over multiple regression models for prediction of surface roughness during machining of AISI 4340 steel (69 HRC).     

Optimization studies in high speed turning of Ti-6Al-4V

Surface roughness is a major concern to the present manufacturing sector without the wastage of material. Hence, in order to achieve good surface roughness and reduce production time, optimization is necessary. In this study optimization techniques based on swarm intelligence (SI) namely firefly algorithm (FA), particle swarm optimization (PSO) and a newly introduced metaheuristic algorithm namely bat algorithm (BA) has been implemented for optimizing machining parameters namely cutting speed, feed rate, depth of cut and tool flank wear and cutting tool vibrations in order to achieve minimum surface roughness. Two parameters R_a and R_t have been considered for evaluating the surface roughness. The performance of BA algorithm has been compared with FA algorithm and PSO, which is a commonly and widely used optimization algorithm in machining. The results conclude that BA produces better optimization, when compared to FA and PSO. Based on the literature review carried out, this work is a first attempt at using a metaheuristic algorithm namely BA in machining applications.     

A comparison of machine learning methods for cutting parameters prediction in high speed turning process

Support vector machines are arguably one of the most successful methods for data classification, but when using them in regression problems, literature suggests that their performance is no longer state-of-the-art. This paper compares performances of three machine learning methods for the prediction of independent output cutting parameters in a high speed turning process. Observed parameters were the surface roughness (Ra), cutting force \((F_{c})\), and tool lifetime (T). For the modelling, support vector regression (SVR), polynomial (quadratic) regression, and artificial neural network (ANN) were used. In this research, polynomial regression has outperformed SVR and ANN in the case of \(F_{c}\) and Ra prediction, while ANN had the best performance in the case of T, but also the worst performance in the case of \(F_{c}\) and Ra. The study has also shown that in SVR, the polynomial kernel has outperformed linear kernel and RBF kernel. In addition, there was no significant difference in performance between SVR and polynomial regression for prediction of all three output machining parameters.     

Design of piezoelectric micromachined ultrasonic transducers (pMUTs) for high pressure output

A novel design of piezoelectric micromachined ultrasonic transducers (pMUTs) with a fully free edge structure by introducing a deep trench between cells was proposed, and its performance was evaluated by finite element method in both time and frequency domains for the resonant frequency and acoustic pressure output. In comparison to current cell configurations of clamped and simply suspended boundaries, our design has a great increase in the output pressure. The effect of the covering area of piezoelectric material (i.e., AlN) was also investigated. It is found that when the piezoelectric layer has the same size as the cavity the generated acoustic pressure will reach its maximum value. Altogether, the newly designed structure of pMUTs has the potentials of high pressure applications (i.e., ultrasound therapy) if driven properly.

     

碳原子簇Cn(n=2-20)的结构优化及构型转换

将并行快速退火演化算法结合Brenner势能函数用于小碳原子簇Cn(n=2—20)的结构优化,得到了最稳定构型：C2-C4为线型结构;C5-C17,为单环;C18和C19为类富勒烯的笼状结构;C30为最小的富勒烯。在Brenner势中使用了关于键级的修正项Fij以考虑成键轨道的非正常重叠和非、局域效应,研究了Fij项对碳原子簇键级以及结构转变产生的影响和原因,结果表明该修正项可以降低多环碳原子簇的键级,使得原子簇C18和C20的最稳定结构发生从多环到笼状的转变。     

Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM,ANN and SVM

In this paper, statistical models were developed to investigate effect of cutting parameters on surface roughness and root mean square of work piece vibration in boring of stainless steel. A mixed level design of experiments was prepared with process variables of nose radius, cutting speed and feed rate. According to design of experiments, eighteen experiments were conducted on AISI 316 stainless steel with PVD coated carbide tools. Surface roughness, tool wear and vibration of work piece were measured in each experiment. A laser Doppler vibrometer was used to measure vibration of work piece in the form of acousto optic emission signals. These signals were processed and transformed in to different frequency zones using a fast Fourier transformer. Analysis of variance was used to identify significant cutting parameters on surface roughness and root mean square of work piece vibration. Predictive models like response surface methodology, artificial neural network and support vector machine were used to predict the surface roughness and root mean square of work piece vibration. Cutting parameters were optimized for minimum surface roughness and root mean square of work piece vibration using a multi response optimization technique.     

Design of novel,semi-transparent flip-flops (STFF) for high speed and low power application

This paper presents a novel,semi-transparent structure to implement single-edge and dual-edge triggered flip-flops.These two novel flip-flops can achieve high speed and low power due to their short data paths and fewer redundant transitions.Simulation results show that the proposed semi-transparent flip-flop and dual-edge triggered semi-transparent flip-flop perform best compared with conventional flip-flops.STFF reduces the power-delay-product 33.1% compared with Antonio’s sense amplifier based flip-flop.DSTFF improves the PDP 9.1% and 47.8% as compared with dual-edge triggered sense amplifier based flip-flop and high speed dual-edge triggered modified hybrid latch flip-flop respectively.     

Design of novel,semi-transparent flip-flops(STFF) for high speed and low power application

This paper presents a novel,semi-transparent structure to implement single-edge and dual-edge triggered flip-flops.These two novel flip-flops can achieve high speed and low power due to their short data paths and fewer redundant transitions.Simulation results show that the proposed semi-transparent flip-flop and dual-edge triggered semi-transparent flip-flop perform best compared with conventional flip-flops.STFF reduces the power-delay-product 33.1% compared with Antonio’s sense amplifier based flip-flop.DSTFF improves the PDP 9.1% and 47.8% as compared with dual-edge triggered sense amplifier based flip-flop and high speed dual-edge triggered modified hybrid latch flip-flop respectively.     

A tool for voltage stability and optimization (VS&OP) in radial distribution systems using matlab graphical user interface (GUI)

This paper describes a software package whose purpose is to provide a tool to be used for identifying the closeness of the each bus and the critical bus which is the most sensitive to the voltage collapse in radial distribution networks. The package also facilitates two optimization algorithms to be used for the optimal placement of DG and shunt capacitors with their optimal size in distribution networks for power system studies. It uses the graphical user interface (GUI) capabilities of Matlab and is called voltage stability and optimization (VS&OP) Package. It is available at http://www.gyte.edu.tr/Dosya/102/power/ and can freely be downloaded.     

Mathematical modeling and optimization strategies (genetic algorithm and knowledge base) applied to the continuous casting of steel

The control of quality in continuous casting products cannot be achieved without a knowledge base which incorporates parameters and variables of influence such as: equipment characteristics, steel, each component of the system and operational conditions. This work presents the development of a computational algorithm (software) applied to maximize the quality of steel billets produced by continuous casting. A mathematical model of solidification works integrated with a genetic search algorithm and a knowledge base of operational parameters. The optimization strategy selects a set of cooling conditions (mold and secondary cooling) and metallurgical criteria in order to attain highest product quality, which is related to a homogeneous thermal behavior during solidification. The results of simulations performed using the mathematical model are validated against both experimental and literature results and a good agreement is observed. Using the numerical model linked to a search method and the knowledge base, results can be produced for determining optimum settings of casting conditions, which are conducive to the best strand surface temperature profile and metallurgical length.     

应用MIMO的TD-SCDMA上行链路性能

介绍了一种结合MIMO技术的TD-SCDMA上行链路系统,用于在尽量减少硬件改变的情况下来提高现有TD-SCDMA系统的性能.该系统在移动终端使用相对简单的双发射天线分集,在基站接收端使用多天线接收联合检测技术.建立了该上行链路系统模型,并分析了在该系统模型下的信噪比变化情况.最后通过仿真得到了TD-SCDMA信号在上述情况下的误码率性能,并和仅仅在基站处使用联合检测技术的TD-SCDMA系统性能进行了比较.通过仿真表明该模型切实可用,并证明了TD-SCDMA系统在移动终端使用双天线发射分集可以改善原系统的性能.     

乙烯裂解炉模拟优化系统(EPSOS)在工业装置上的应用

EPSOS是由兰州化工研究中心、兰州石化公司和清华大学合作开发的乙烯裂解模拟优化系统.该系统采用Kumar分子反应动力学模型,具有界面友好、可视化强和炉型组态灵活等特点,能够对乙烯裂解炉进行伞周期模拟及操作优化.EPsOs已经在兰州石化公司运行1年多,优化戍用结果表明,乙烯总平均产率比优化前提高了0.72%、三烯总收率提高0.55%.利用EPSOS系统能够较好的指导生产,对于提高乙烯生产装置的操作水平及经济效益具有重要意义.     

The probability of “undefined” (non-converging) output in generating the universal probability distribution

In order to generate a universal probability distribution to extrapolate a binary string x of length i, we feed random bits into a universal device, M. When we find an input string that gives an output matching x, we continue the successful input with random bits until M produces a zero or one as output. The relative probabilities of these two continuations can give a normalized prediction for the probability of the symbol following x. There is, however, a probability, Pi+1(u) that the continued random input string will not generate any output for the (i+1)th symbol.We will show . Here Eμ is the expected value with respect to μ, the probability distribution that created x. kμ is the Kolmogorov complexity of μ with respect to M. n is any positive integer. Usually we do not know μ and so we do not know kμ. However, if μ is the uniform distribution, we can usually find a good upper bound for kμ.     

Finding maximum (in modulus) complex-conjugate eigenvalues of arbitrarily high dimension

Algorithms of determining maximum (in modulus) complex-conjugate eigenvalues are considered as applied to finding eigenvalues of high-dimension matrices according to the Khilenko method. An advantage of the algorithms is that the amount of calculation does not increase exponentially with the dimension of matrices. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 104–110, March–April 2006.     

图像阈值分割方法研究进展20年（1994—2014）

阈值分割是图像分割领域中使用最为普遍的一类简单而有效的方法,多年来受到许多学者的广泛关注,发表了大量的研究成果。作者曾于20年前对1962-1992年阈值分割的研究状况做了一个阶段性的回顾与总结。时至今日,阈值分割方法已经获得了巨大的发展,新思路、新方法层出不穷。本文旨在对近20年来阈值分割领域常用的一些方法再次进行概括和分类,其中包括近年来新提出的阈值分割方法,也包括对经典方法的改进。文中给出了这些方法的基本思想和公式,阐明了各种方法的特点及其适用范围,以期为今后阈值分割的相关研究提供一些思路和启迪。     

A novel strategy of pareto-optimal solution searching in multi-objective particle swarm optimization (MOPSO)

In multi-objective particle swarm optimization (MOPSO) algorithms, finding the global optimal particle (gBest) for each particle of the swarm from a set of non-dominated solutions is very difficult yet an important problem for attaining convergence and diversity of solutions. First, a new Pareto-optimal solution searching algorithm for finding the gBest in MOPSO is introduced in this paper, which can compromise global and local searching based on the process of evolution. The algorithm is implemented and is compared with another algorithm which uses the Sigma method for finding gBest on a set of well-designed test functions. Finally, the multi-objective optimal regulation of cascade reservoirs is successfully solved by the proposed algorithm.     

Artificial Bee Colony (ABC) algorithm in the design optimization of RC continuous beams

The objective of this study is to obtain the optimum design for reinforced concrete continuous beams in terms of cross section dimensions and reinforcement details using a fine tuned Artificial Bee Colony (ABC) Algorithm while still satisfying the constraints of the ACI Code (2008). The ABC algorithm used in this paper has been slightly modified to include a Variable Changing Percentage (VCP) that further improves its performance when dealing with members consisted of multiple variables. The objective function is the total cost of the continuous beam which includes the cost of concrete, formwork and reinforcing steel bars. The design variables used are beam width, beam height, number and diameter of: bottom continuous reinforcing bars, bottom cutoff reinforcing bars, top continuous reinforcing bars and top cutoff reinforcing bars as well as the diameter of stirrups. Four RC beams of varying complexity are presented and optimized. The first three beams are used to fine tune the control parameters of the ABC algorithm, whereas the fourth beam was previously optimized by Arafa et al. (J Artif Intell 76–88, 2011) and is presented here to prove the superiority of this relatively new optimization algorithm.