Prediction of tool life in end milling of Ti-6Al-4V alloy using artificial neural network and multiple regression models (original) (raw)
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International Journal of Geomechanics, 2012
Nowadays, the application of artificial neural networks (ANN) is often utilized in solving numerous problems in machining processes. There has been evidence of the significance of a tool life prediction of coated and uncoated cutting tools. The current study aims at applying ANN in the prediction of the tool life of PVD cutting tools using low experimental data sets. It used a feed forward back propagation neural network with a Levenberg-Marquard (L-M) training algorithm is used in modeling the tool life of a PVD insert cutting tool when end milling of Ti6Al4V under dry cutting conditions. One hundred and ten (110) models were designed, trained and tested using Matlab neural network tool box. Based on the same experimental data, a regression model (RM) has been constructed employing SPSS software, and based on the mean square error of ANN and RM models, the two models were compared. The findings revealed that the ANN model resulted into minimum mean square error compared with RM mod...
Journal of materials processing technology, 2002
In this study, the high manganese steel specimens heated with liquid petroleum gas flame were machined on a lathe under different cutting conditions of feed rates, depth of cuts, cutting speeds and surface temperatures. A mathematical model for tool life was obtained from the experimental data using a regression analysis method. In addition, the tool life was estimated using artificial neural network (ANN) with backpropagation (BP) algorithm. Then, this program was trained and tested. Finally, the experimental data are compared with both the regression analysis results and the estimations of ANN.
Journal of Manufacturing Engineering, 2022
The purpose of this comparative study is to improve the predictive accuracy of the cutting force during the turning of Ti-6Al-4V on a lathe machine. By optimizing the machining process parameters such as cutting speed, feed rate, and depth of cut, the cutting force in the machining process can be improved significantly. Cutting force is one of the crucial characteristics that must be monitored during the cutting process in order to enhance tool life and the surface finish of the workpiece. This paper is based on the experimental dataset of cutting forces collected during the turning of titanium alloy under the Minimum Quantity Lubrication (MQL) condition. To predict the cutting forces, two machine learning techniques are explored. Firstly, a black-box model called an Artificial Neural Network (ANN) is proposed to predict cutting force. Using the Levenberg-Marquardt algorithm, a two-layered feedforward neural network is built in MATLAB to predict cutting force. The second model to be implemented was the Genetic Algorithm (GA), a white-box model. GA is an optimization technique which is based on Darwinian theories. It is a probabilistic method of searching, unlike most other search algorithms, which require definite inputs. Using symbolic regression in HeuristicLab, a GA model is developed to estimate cutting force. The anticipated values of cutting forces for both models were compared. Since the ANN model had fewer errors, it was ascertained that the particular model is preferable for machining process optimization.
Regression and ANN models for estimating minimum value of machining performance
Applied Mathematical Modelling
Surface roughness is one of the most common performance measurements in machining process and an effective parameter in representing the quality of machined surface. The minimization of the machining performance measurement such as surface roughness (R a ) must be formulated in the standard mathematical model. To predict the minimum R a value, the process of modeling is taken in this study. The developed model deals with real experimental data of the R a in the end milling machining process. Two modeling approaches, regression and Artificial Neural Network (ANN), are applied to predict the minimum R a value. The results show that regression and ANN models have reduced the minimum R a value of real experimental data by about 1.57% and 1.05%, respectively.
Development of family of artificial neural networks for the prediction of cutting tool condition
Advances in Production Engineering & Management
Recently, besides regression analysis, artificial neural networks (ANNs) are increasingly used to predict the state of tools. Nevertheless, simulations trained by cutting modes, material type and the method of sharpening twist drills (TD) and the drilling length from sharp to blunt as input parameters and axial drilling force and torque as output ANN parameters did not achieve the expected results. Therefore, in this paper a family of artificial neural networks (FANN) was developed to predict the axial force and drilling torque as a function of a number of influencing factors. The formation of the FANN took place in three phases, in each phase the neural networks formed were trained by drilling lengths until the drill bit was worn out and by a variable parameter, while the combinations of the other influencing parameters were taken as constant values. The results of the prediction obtained by applying the FANN were compared with the results obtained by regression analysis at the points of experimental results. The comparison confirmed that the FANN can be used as a very reliable method for predicting tool condition.
Application of AI-Based Techniques For CNC Machining (Milling)
Tool life prediction is crucial in CNC milling to optimize machining operations and reduce costs. This study explores the application of artificial neural networks (ANNs) for predicting tool life based on machining parameters such as RPM, feed speed, axial depth of cut, and radial depth of cut. A dataset is utilized to train and evaluate the ANN model, which is optimized using standard scaling and Adam optimizer. The results demonstrate the efficacy of ANNs in accurately forecasting tool life, enabling proactive maintenance and enhancing machining efficiency and comparing with MATLAB. Keywords: Artificial Neural Networks, CNC Milling, MATLAB
Optimized Prediction and Modeling Under End Milling Machining By ANOVA And Artificial Neural Network
2013
In recent times, the need for characterization of surface roughness in an end milling machining process is essential. Accordingly a better approach can provide accurate machining for post casting of rolled Aluminium which often requires better dimensional tolerances. In present work the study for development of surface roughness model for rolled Aluminium is described. Experimental data is collected as per L18 orthogonal array with three levels defined for each of the factor by design of experiment approach. Analysis of variance (ANOVA) is used to test the adequacy of the developed mathematical model. Further, ANN analysis with multilayer feed forward perceptron structure using graphical user interface (GUI) under MATLAB is adopted with the experimental values as input-output pairs. Back Propagation algorithm using the input cutting conditions viz. Spindle Speed, Feed rate and Depth of Cut is being constructed and the surface roughness of the machined component is been taken as output response. The effect of feed rate is found to be the highest influence on surface roughness followed by the cutting speed and depth of cut. The result revealed the close correlation between the measured values and the model output in much lesser time and with a high accuracy.
Tool life predictions in milling using spindle power with the neural network technique
Journal of Manufacturing Processes, 2016
Tool wear is an important limitation to machining productivity and part quality. In this paper, remaining useful life (RUL) prediction of tools is demonstrated based on the machine spindle power values using the neural network (NN) technique. End milling tests were performed on a stainless steel workpiece at different spindle speeds and spindle power was recorded. The NN curve fitting approach with different MATLAB TM training functions was applied to the root mean square power (P rms) values. Sample P rms growth curves were generated to take into account uncertainty. The P rms value in the time domain was found to be sensitive to tool wear. Results show a good agreement between the predicted and true RUL of tools. The proposed method takes into account the uncertainty in tool life and the percentage increase in nominal P rms value during the RUL prediction. Using MATLAB TM on an Intel i7 processor, the computation takes 0.5 s Thus, the method is computationally inexpensive and can be incorporated for real time RUL predictions during machining.
Applicability of ANN models and Taguchi method for the determination of tool life in turning
MATEC web of conferences, 2017
Tool life is an important parameter in machining processes, affecting directly the quality of machined components and the process cost. It is already shown that various parameters can affect tool life such as process parameters, i.e. depth of cut, cutting speed and feed, or material properties of cutting tool and workpiece. The determination of the effect of each parameter on tool life is of crucial importance when designing the manufacturing process of a product in order to select suitable process parameter values and tool types. Several empirical formulas for the determination of tool life exist in the relevant literature; especially in the case of CBN cutting tools for turning, a cubic polynomial formula was proposed to model the relationship between tool life and cutting speed. The determination of the polynomial parameters was performed by conducting cutting experiments for several cutting speeds, without the aid of a design of experiments (DoE) method in order to model properly this non-linear relationship. In this paper, the feasibility of determining this non-linear relationship by conducting experiments designed by Taguchi method and using artificial neural networks (ANN) is investigated for several cases and conclusions on the applicability of this approach are presented.