Surface roughness and forces prediction of milling Inconel 718 with neural network

Wiciak-Pikuła, Martyna; Felusiak, Agata; Chwalczuk, Tadeusz; Twardowski, Paweł

doi:10.1109/metroaerospace48742.2020.9160301

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…By analysing the data presented in Table 1, it can be seen that in the case of the milling process, the tests were carried out for various materials, such as aluminium alloys [39,43,[45][46][47]50,53,54], magnesium alloys [55,56], titanium alloys [37,40,57], cobalt alloys [44], nickel alloys [48,[58][59][60] and steel [41,42]. Additionally, it can be stated that as far as the milling roughness parameters are concerned, only 2D parameters are modelled, mainly the Ra parameter.…”

Section: Inteligent Methods In Surface and Temperature Parameters Mod...mentioning

confidence: 99%

Research, Modelling and Prediction of the Influence of Technological Parameters on the Selected 3D Roughness Parameters, as Well as Temperature, Shape and Geometry of Chips in Milling AZ91D Alloy

et al. 2022

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The main purpose of the study was to define the machining conditions that ensure the best quality of the machined surface, low chip temperature in the cutting zone and favourable geometric features of chips when using monolithic two-teeth cutters made of HSS Co steel by PRECITOOL. As the subject of the research, samples with a predetermined geometry, made of AZ91D alloy, were selected. The rough milling process was performed on a DMU 65 MonoBlock vertical milling centre. The machinability of AZ91D magnesium alloy was analysed by determining machinability indices such as: 3D roughness parameters, chip temperature, chip shape and geometry. An increase in the feed per tooth fz and depth of cut ap parameters in most cases resulted in an increase in the values of the 3D surface roughness parameters. Increasing the analysed machining parameters did not significantly increase the instantaneous chip temperature. Chip ignition was not observed for the current cutting conditions. The conducted research proved that for the adopted conditions of machining, the chip temperature did not exceed the auto-ignition temperature. Modelling of cause-and-effect relationships between the variable technological parameters of machining fz and ap and the temperature in the cutting zone T, the spatial geometric structure of the 3D surface “Sa” and kurtosis “Sku” was performed with the use of artificial neural network modelling. During the simulation, MLP and RBF networks, various functions of neuron activation and various learning algorithms were used. The analysis of the obtained modelling results and the selection of the most appropriate network were performed on the basis of the quality of the learning and validation, as well as learning and validation error indices. It was shown that in the case of the analysed 3D roughness parameters (Sa and Sku), a better result was obtained for the MLP network, and in the case of maximum temperature, for the RBF network.

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Section: Inteligent Methods In Surface and Temperature Parameters Mod...mentioning

confidence: 99%

Research, Modelling and Prediction of the Influence of Technological Parameters on the Selected 3D Roughness Parameters, as Well as Temperature, Shape and Geometry of Chips in Milling AZ91D Alloy

et al. 2022

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“…The authors of one study [25] presented a model for the prediction of components of cutting force for an AZ91HP alloy with two inputs, namely the machining parameters (cutting speed and feed per tooth). Other materials which have been studied by other researchers using simulations of cutting force components include aluminium alloy (input parameters: spindle speed, feed per tool, radial depth and axial depth) [26], 7050 aluminium alloy (input parameters: spindle speed, feed per tooth, milling width) [27], Inconel 718 (input parameters: tool wear, feed per tooth, cutting depth, cutting width, cutting speed) [28], and also AZ31 magnesium alloys with a PCD tool (cutting speed range and feed per tooth as input parameters) [10]. The models developed allow for the testing of different configurations of input parameters (machining parameters) without having to simultaneously conduct experimental tests.…”

Section: Introductionmentioning

confidence: 99%

Analysis and prediction of the impact of technological parameters on cutting force components in rough milling of AZ31 magnesium alloy

Kulisz

Zagórski

Weremczuk

et al. 2021

Archiv.Civ.Mech.Eng

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This paper presents the results of experimental study of the AZ31 magnesium alloy milling process. Dry milling was carried out under high-speed machining conditions. First, a stability lobe diagram was determined using CutPro software. Next, experimental studies were carried out to verify the stability lobe diagram. The tests were carried out for different feed per tooth and cutting speed values using two types of tools. During the experimental investigations, cutting forces in three directions were recorded. The obtained time series were subjected to general analysis and analysis using composite multiscale entropy. Modelling and prediction were performed using Statistica Neural Network software, in which two types of neural networks were applied: multi-layered perceptron and radial basis function. It was observed that milling with high cutting speed values allows for component values of cutting force to be lowered as a result of the transition into the high-speed machining conditions range. In most cases, the highest values for the analysed parameters were recorded for the component Fx, whereas the lowest were recorded for Fy. Additionally, the paper shows that a prediction (with the use of artificial neural networks) of the components of cutting force can be made, both for the amplitudes of components of cutting force Famp and for root mean square Frms.

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Application of AI techniques for modeling the performance measures in milling of 7075-T6 hybrid aluminum metal matrix composites

Mohanraj

2021

Int. J. Model. Simul. Sci. Comput.

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The prediction of performance measures is an essential one for manufacturers to increase the service life. This paper deals with the application of Artificial Intelligence (AI) to predict the performance measures like surface roughness, material removal rate, and flank wear during the milling process from the experimental data. The milling experiments were conducted in wet conditions based on the Response Surface Methodology (RSM) design of experiments. The spindle speed, feed rate, and axial depth of cut were considered as process parameters. The experimental data were used to develop the regression model, Mamdani fuzzy inference system, Backpropagation Neural Network (BPNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) model. The output of regression, fuzzy, neural network, and ANFIS model was compared with the experimental data, and predicted results were found to be in good conformity with the measured data. The prediction capability of the quadratic and Artificial Neural Network (ANN) model was very close to experimentally measured values and the quadratic model had an accuracy of 97.89% for surface roughness, 98.38% for material removal rate (MRR), and 95.72% for flank wear.

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Surface roughness and forces prediction of milling Inconel 718 with neural network

Cited by 4 publications

References 20 publications

Research, Modelling and Prediction of the Influence of Technological Parameters on the Selected 3D Roughness Parameters, as Well as Temperature, Shape and Geometry of Chips in Milling AZ91D Alloy

Research, Modelling and Prediction of the Influence of Technological Parameters on the Selected 3D Roughness Parameters, as Well as Temperature, Shape and Geometry of Chips in Milling AZ91D Alloy

Analysis and prediction of the impact of technological parameters on cutting force components in rough milling of AZ31 magnesium alloy

Application of AI techniques for modeling the performance measures in milling of 7075-T6 hybrid aluminum metal matrix composites

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