4,466 research outputs found

    Prediction of surface roughness in low speed turning of AISI316 austenitic stainless steel

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    Surface roughness is an important quality in manufacturing, as it affects the product’s tribological, frictional and assembly characteristics. Turning stainless steel at low cutting speeds may result in a rougher surface due to built up edge formation, where as speed increases the surface roughness improves, due to the low contact time between the chip and the tool to allow bonding to occur.However, this increase in cutting speed produces higher tool wear rates, which increases the machining costs. Previous studies have indicated that savings in cost and manufacturing time are obtained when predicting the surface roughness, prior to the machining process. In this paper, experimental data are used to develop prediction models using Multiple Linear Regression and Artificial Neural Network methodologies. Results show that the neural network outperforms the linear model by a fair margin (1400%). Moreover, the developed Artificial Neural Network model has been integrated with an optimisation algorithm, known as Simulated Annealing (SA),this is done in order to obtain a set of cutting parameters that result in low surface roughness. A low value of surface roughness and the set of parameters resulting on it, are successfully yielded by the SA algorithm

    Simulate Turning Process using ANN, Predict Optimum Control Factors to achieve Minimum Surface Roughness

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    poster abstractAbstract Turning is a material removal process, a subtractive form of machining which is used to create parts of circular or rotational form of desired geometry/shape by removing unwanted material. Accuracy of any process depends on involvement of operational variables. The operating parameters that contribute to turning process are Cutting speed, Feed rate, Depth of cut. Vibrations, tool wear, tool life, surface finish and cutting forces etc are also in direct relation with values selected for process parameters. Hence to improve the efficiency of process and quality of the product it is necessary to control the process parameters. Surface roughness is the parameters with main focus, as it dictates the aesthetics and sometimes ergonomical characteristics of the product. The tests were carried out on AISI 4140 steel. 12 speed Jones and Lamson Lathe model was used for turning operation. The specimen with a diameter of 60mm, 500mm length and hardened 35 HRC is used. The tool used for this is one that is most commonly used for turning process DTGNR 163 C 0° Lead Angle 60° Triangle insert. It is product of Kennametal. Statistical Design of Experiments was used to reduce the total number of trials in order to save the time and resources without compromising the accuracy of prediction. These readings are used to train and validate the Neural Network. ANN is found to be very useful with simulations tasks which have complex and explicit relation between control factors and result of process. Neural Network was created using feed forward back propagation technique for simulation of the process using the Matlab Neural network toolbox. With assurance of accuracy of the predictive capabilities of the neural network, it was then used for optimization. Particle Swarm Optimization Algorithm, an evolutionary computation technique is used find out the optimum values of the input parameters to achieve the minimum surface roughness. The objective function used here is to minimize the surface roughness. Limits of the operational variables are used as constraints for developing the code for optimization algorithm. Keywords: Turning process, Surface roughness, Artificial Neural Network, Particle swarm optimization

    Surface roughness modeling of CBN hard steel turning

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    Study in the paper investigate the influence of the cutting conditions parameters on surface roughness parameters during turning of hard steel with cubic boron nitrite cutting tool insert. For the modeling of surface roughness parameters was used central compositional design of experiment and artificial neural network as well. The values of surface roughness parameters Average mean arithmetic surface roughness (Ra) and Maximal surface roughness (Rmax) were predicted by this two-modeling methodology and determined models were then compared. The results showed that the proposed systems can significantly increase the accuracy of the product profile when compared to the conventional approaches. The results indicate that the design of experiments modeling technique and artificial neural network can be effectively used for the prediction of the surface roughness parameters of hard steel and determined significantly influential cutting conditions parameters

    Surface profile prediction and analysis applied to turning process

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    An approach for the prediction of surface profile in turning process using Radial Basis Function (RBF) neural networks is presented. The input parameters of the RBF networks are cutting speed, depth of cut and feed rate. The output parameters are Fast Fourier Transform (FFT) vector of surface profile for the prediction of surface profile. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. A very good performance of surface profile prediction, in terms of agreement with experimental data, was achieved with high accuracy, low cost and high speed. It is found that the RBF networks have the advantage over Back Propagation (BP) neural networks. Furthermore, a new group of training and testing data were also used to analyse the influence of tool wear and chip formation on prediction accuracy using RBF neural networks

    A predictive surface profile model for turning based on spectral analysis

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    This article presents a predictive approach of surface topography based on the FFT analysis of surface profiles. From a set of experimental machining tests, the parameters investigated are: feed per revolution, insert nose radius, depth of cut and cutting speed. The first step of the analysis consists of normalizing the measured profiles with the feed per revolution. This results in normalized profiles with a feed per revolution and a signal period equal to 1. The effect of each cutting parameter on the surface profile is expressed as a spectrum with respect to the period length. These effects are quantified and can be sorted in descending order of importance as feed per revolution, insert nose radius, depth of cut and cutting speed. The second part of the paper presents a modeling of the surface profile using the parameters effects and one interaction. The proposed model gives the spectrum of the profile to be predicted. The inverse Fourier transform applied to the spectrum yields the expected surface profile. Measured and simulated profiles are compared for two cutting conditions and results correlate well

    Neural network modelling of Abbott-Firestone roughness parameters in honing processes

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    In present study, three roughness parameters defined in the Abbott-Firestone or bearing area curve, Rk, Rpk and Rvk, were modelled for rough honing processes by means of artificial neural networks (ANN). Input variables were grain size and density of abrasive, pressure of abrasive stones on the workpiece's surface, tangential or rotation speed of the workpiece and linear speed of the honing head. Two strategies were considered, either use of one network for modelling the three parameters at the same time or use of three networks, one for each parameter. Overall best neural network consists of three networks, one for each roughness parameter, with one hidden layer having 25, nine and five neurons for Rk, Rpk and Rvk respectively. However, use of one network for the three roughness parameters would allow addressing an indirect model. In this case, best solution corresponds to two hidden layers having 26 and 11 neurons.Peer ReviewedPostprint (author's final draft

    Model-based observer proposal for surface roughness monitoring

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    Comunicación presentada a MESIC 2019 8th Manufacturing Engineering Society International Conference (Madrid, 19-21 de Junio de 2019)In the literature, many different machining monitoring systems for surface roughness and tool condition have been proposed and validated experimentally. However, these approaches commonly require costly equipment and experimentation. In this paper, we propose an alternative monitoring system for surface roughness based on a model-based observer considering simple relationships between tool wear, power consumption and surface roughness. The system estimates the surface roughness according to simple models and updates the estimation fusing the information from quality inspection and power consumption. This monitoring strategy is aligned with the industry 4.0 practices and promotes the fusion of data at different shop-floor levels

    Using Multiple Linear Regression and Artificial Neural Network to Predict Surface Roughness in Turning Operations

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    Quality of surface roughness has a great impact on machine parts during their useful life. The machining process is more complex, and therefore, it is very hard to develop a comprehensive model involving all cutting parameters. In this paper, the surface roughness is measured during turning operation at different cutting parameters such as speed, feed rate, and depth of cut. Two mathematical models are developed to predict the surface roughness and to select the required surface roughness by using the Multi-regression model and Artificial Neural Networks (ANN). To test the developed models, 27 pieces of steel alloy HRC15 were operated and the roughness of their surfaces measured. The results showed that the ANN model estimates the surface roughness with high accuracy compared to the multiple regression model with the average deviation from the real values of about 1%

    Modelling and Prediction of Surface Roughness in CNC Turning Process using Neural Networks

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    The paper presents an approach to solving the problem of modelling and prediction of surface roughness in CNC turning process. In order to solve this problem an experiment was designed. Samples for experimental part of investigation were of dimensions 30 × 350 mm, and the sample material was GJS 500 - 7. Six cutting inserts were used for the designed experiment as well as variations of cutting speed, feed and depth of cut on CNC lathe DMG Moriseiki-CTX 310 Ecoline. After the conducted experiment, surface roughness of each sample was measured and a data set of 750 instances was formed. For data analysis, the Back-Propagation Neural Network (BPNN) algorithm was used. In modelling different BPNN architectures with characteristic features the results of RMS (Root Mean Square) error were controlled. Specially analysed were the RMS errors realised by different number of neurons in hidden layers. For the BPNN architecture with one hidden layer the architecture (4 – 8 - 1) was adopted with RMS error of 3,37%. In modelling the BPNN architecture with two hidden layers, a considerable amount of architectures was investigated. The adopted architecture with two hidden layers (4 - 2 - 10 - 1) generated the RMS error of 2,26%. The investigation was also directed at the size of the data set and controlling the level of RMS error

    Prediction of Surface Roughness and Power in Turning Process Using Response Surface Method and ANN

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    This paper examines the influence of three cutting parameters (cutting speed, cutting depth and feed rate) on surface roughness and power in the longitudinal turning process of aluminium alloy. For the analysis of data gathered by experiments, two methods for prediction of responses were employed, namely Response Surface Methodology (RSM) and Artificial Neural Network (ANN). The research has shown that the ANN gives a better prediction of surface roughness than the RSM. In the modelling of the power, the average error value obtained by the ANN does not differ significantly from its value obtained by the RSM. This research is conducted to reveal the rigidity of the machine tool in order to select an appropriate spindle motor for retrofit purpose. The unexpected surface roughness and the error between the experimental and predicted values show that the obtained models are, in most cases, not adequate to predict surface roughness when the power is greater than a given limit. Therefore, the servo motor with smaller power than the original motor is selected which is cost-effective and it will not cause inappropriate strong vibrations that lead to the unexpected surface roughness and excessive noise inside the Learning Factory environment in which the machine tool is used
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