Prediction of workpiece elastic deflections under cutting forces in turning

One of the problems faced in turning processes is the elastic deformation of the workpiece due to the cutting forces resulting in the actual depth of cut being different than the desirable one. In this paper, a cutting mechanism is described suggesting that the above problem results in an over-dimensioned part. Consequently, the problem of determining the workpiece elastic deflection is addressed from two different points of view. The first approach is based on solving the analytical equations of the elastic line, in discretized segments of the workpiece, by considering a stored modal energy formulation due to the cutting forces. Given the mechanical properties of the workpiece material, the geometry of the final part and the cutting force values, this numerical method can predict the elastic deflection. The whole approach is implemented through a Microsoft Excel^© workbook. The second approach involves the use of artificial neural networks (ANNs) in order to develop a model that can predict the dimensional deviation of the final part by correlating the cutting parameters and certain workpiece geometrical characteristics with the deviations of the depth of cut. These deviations are calculated with reference to final diameter values measured with precision micrometers or on a CMM. The verification of the numerical method and the development of the ANN model were based on data gathered from turning experiments conducted on a CNC lathe. The results support the proposed cutting mechanism. The numerical method qualitatively agrees with the experimental data while the ANN model is accurate and consistent in its predictions.     

Comparison of model reduction techniques for large mechanical systems

Model reduction is a necessary procedure for simulating large elastic systems, which are mostly modeled by the Finite Element Method (FEM). In order to reduce the system’s large dimension, various techniques have been developed during the last decades, many of which share some common characteristics (Guyan, Dynamic, CMS, IRS, SEREP). A fact remains that many reduction approaches do not succeed in reducing the system’s dimension without damaging the dynamical properties of the model. The mathematical field of control theory offers alternative reduction methods, which can be applied to second order Ordinary Differential Equations (ODEs), derived by the FE-discretization of large elastic Multi Body Systems (MBS), e.g., Krylov subspace method or balanced truncation. In this paper, some of these methods are applied to the elastic piston rod. The validity of the reduced models is checked by applying Modal Correlation Criteria (MCC), since only the eigenfrequency comparison is not sufficient. Diagonal Perturbation is proposed as an efficient method for iteratively solving ill-conditioned large sparse linear systems (A x=b, A: ill-conditioned) when direct methods fail due to memory capacity problems. This is the case of FE-discretized systems, when tolerance failure occurs during the discretization procedure.     

上拔荷载作用下等截面桩的承载性状数值模拟

利用轴对称模型对等截面桩进行承受上拔载荷作用的有限元模拟.通过数值模拟研究桩的承载性状,包括桩的上拔位移、载荷传递特性和桩侧摩阻力的发展,并分析桩底部土体的局部应变以及土体位移场的分布.研究发现,在达到极限上拔载荷时,桩端周围土体出现剪应变方向相反的两个区域并形成剪切带,桩周土体的位移场逐渐呈倒锥形分布.     

Estimation of distribution and differential evolution cooperation for large scale economic load dispatch optimization of power systems

Economic Load Dispatch (ELD) is an important and difficult optimization problem in power system planning. This article aims at addressing two practically important issues related to ELD optimization: (1) analyzing the ELD problem from the perspective of evolutionary optimization; (2) developing effective algorithms for ELD problems of large scale. The first issue is addressed by investigating the fitness landscape of ELD problems with the purpose of estimating the expected performance of different approaches. To address the second issue, a new algorithm named “Estimation of Distribution and Differential Evolution Cooperation” (ED-DE) is proposed, which is a serial hybrid of two effective evolutionary computation (EC) techniques: estimation of distribution and differential evolution. The advantages of ED-DE over the previous ELD optimization algorithms are experimentally testified on ELD problems with the number of generators scaling from 10 to 160. The best solution records of classical 13 and 40-generator ELD problems with valve points, and the best solution records of 10, 20, 40, 80 and 160-generator ELD problems with both valve points and multiple fuels are updated in this work. To further evaluate the efficiency and effectiveness of ED-DE, we also compare it with other state-of-the-art evolutionary algorithms (EAs) on typical function optimization tasks.