COMPREHENSIVE EVALUATION OF DIMENSIONAL DEVIATION, FLANK WEAR, SURFACE ROUGHNESS AND MATERIAL REMOVAL RATE IN DRY TURNING OF C45 STEEL

The study investigated the turning of C45 steel in a dry environment. The input parameters that were varied were cutting speed, feed, depth of cut, corner radius and insert type. The experimental investigations were carried out according to a custom experimental design using the D-optimality criterion. The measured output parameters were dimensional deviation, flank wear and surface roughness, while the material removal rate was calculated. A detailed analysis and evaluation of the effects of the input parameters on the output parameters was carried out. The model was diagnosed and appropriate regression equations was established. Based on the obtained regression equations, multi-objective optimisation was performed using particle swarm optimisation. The objective function was to simultaneously minimise dimensional deviation, flank wear and surface roughness and maximise material removal rate. The optimisation was carried out for different weighting coefficients of each output function for different production requirements. The obtained models and optimal values were verified by additional confirmation experiments

THE MODELLING OF SURFACE ROUGHNESS AFTER THE TURNING OF INCONEL 601 BY USING ARTIFICIAL NEURAL NETWORK

This research was funded by the Ministry of Science, Technological Development and Innovation of Republic of SerbiaThis research includes longitudinal turning of Inconel 601 in a dry environment with PVD coated cutting inserts. Turning was performed for different levels of cutting speeds, feeds, depth of cuts and corner radius. After turning, the arithmetical mean surface roughness was measured. Mean arithmetic surface roughness values ranging from 0.156 μm to 6.225 μm were obtained. Based on the obtained results, an artificial neural network (ANN) was created. This ANN model was used to predict surface roughness after machining for different variants of input variables. Performance evaluation of the generated model was performed on the basis of additional - confirmation experiments. The mean absolute errors are 0.005 μm and 0.012 μm for the training and confirmation experiments, respectively. The mean percentage errors are 0.894 % and 1.303 % for the training and confirmation experiments, respectively. The obtained results showcase the possibility of practical application of the developed ANN model.Publishe

Improving The Convergence Rate Of Parareal-In-Time Power System Simulation Using The Krylov Subspace

The performance of parareal-in-time algorithms is determined on the number of sequential, coarse step iterations. A common tradeoff in designing an efficient parareal-in-time algorithm is between accuracy of the coarse solver and the number of iterations. Traditional parareal implementation for the power system simulation can also have difficulties handling complex power systems. In this paper, we propose a Krylov subspace enhanced parareal algorithm to reduce the number of coarse iterations. The proposed approach is demonstrated on a single-machine-infinite-bus system and the IEEE 10-machine 39-bus system. Noticeable decrease of number of iterations is observed in both cases

Numeric Modified Adomian Decomposition Method for Power System Simulations

This paper investigates the applicability of numeric Wazwaz El Sayed modified Adomian Decomposition Method (WES-ADM) for time domain simulation of power systems. WES-ADM is a numerical method based on a modified Adomian decomposition (ADM) technique. WES-ADM is a numerical approximation method for the solution of nonlinear ordinary differential equations. The non-linear terms in the differential equations are approximated using Adomian polynomials. In this paper WES-ADM is applied to time domain simulations of multi-machine power systems. WECC 3-generator, 9-bus system and IEEE 10-generator, 39-bus system have been used to test the applicability of the approach. Several fault scenarios have been tested. It has been found that the proposed approach is faster than the trapezoidal method with comparable accuracy

Embedding Spatial Decomposition In Parareal In Time Power System Simulation

We propose an approach for combining two decompositions in power system simulation, temporal and spatial, with a goal to achieve faster simulation time than utilizing either one of them alone. The proposed approach builds upon the structure of the Parareal algorithm for temporal decomposition and explores an efficient way of embedding the spatial decomposition into its coarse and fine solutions. This introduces two types of simultaneous parallelism in power system simulations - parallelism between system areas and between coarse integration intervals for time integration. The results from the application of the proposed approach are shown on a two-area system comprised of the IEEE 16-machine, 68-bus, and the IEEE 50-machine, 145-bus system

Adaptive Model Reduction For Parareal In Time Method For Transient Stability Simulations

Real time or faster than real time simulation can enable system operators to foresee the effect of crucial contingencies on the power system dynamics and take timely actions to prevent system instability. Parareal in time method uses concurrent computations on different segments of the time domain of interest to speed up the dynamic simulations. This paper describes the application of an adaptive nonlinear model reduction method in improving computational speed of the Parareal solver. The proposed method adaptively switches between a hybrid system with selective linearization and a completely linear system based on the size of a disturbance. The functions in the hybrid system are linearized based on the electrical distance between specific generators and the area where disturbances originated. The proposed method is tested on the 327-machine 2383-bus Polish system