Cutting force prediction in ultrasonic-assisted milling of Ti–6Al–4V with different machining conditions using artificial neural network

2020 ◽  
pp. 1-12
Author(s):  
Ramazan Hakkı Namlu ◽  
Cihan Turhan ◽  
Bahram Lotfi Sadigh ◽  
S. Engin Kılıç
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cutting Force ◽  
Cutting Forces ◽  
Metal Cutting ◽  
Weight Ratio ◽  
Machining Process ◽  
Ultrasonic Assisted ◽  
Artificial Neural ◽  
Conventional Machining

Abstract Ti–6Al–4V alloy has superior material properties such as high strength-to-weight ratio, good corrosion resistance, and excellent fracture toughness. Therefore, it is widely used in aerospace, medical, and automotive industries where machining is an essential process for these industries. However, machining of Ti–6Al–4V is a material with extremely low machinability characteristics; thus, conventional machining methods are not appropriate to machine such materials. Ultrasonic-assisted machining (UAM) is a novel hybrid machining method which has numerous advantages over conventional machining processes. In addition, minimum quantity lubrication (MQL) is an alternative type of metal cutting fluid application that is being used instead of conventional lubrication in machining. One of the parameters which could be used to measure the performance of the machining process is the amount of cutting force. Nevertheless, there is a number of limited studies to compare the changes in cutting forces by using UAM and MQL together which are time-consuming and not cost-effective. Artificial neural network (ANN) is an alternative method that may eliminate the limitations mentioned above by estimating the outputs with the limited number of data. In this study, a model was developed and coded in Python programming environment in order to predict cutting forces using ANN. The results showed that experimental cutting forces were estimated with a successful prediction rate of 0.99 with mean absolute percentage error and mean squared error of 1.85% and 13.1, respectively. Moreover, considering too limited experimental data, ANN provided acceptable results in a cost- and time-effective way.

scholarly journals Prediction of Cutting Force in Turning Process by Using Artificial Neural Network

2020 ◽  
Vol 16 (2) ◽  
pp. 34-46
Author(s):  
Marwa Qasim Ibraheem
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Cutting Force ◽  
Cutting Forces ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Turning Operation ◽  
Artificial Neural

        Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samples of experimental data were used, including nineteen to train the network. Moreover six other experimental tests were implemented to test the network. The study concludes that ANN was a dependable and precise method for predicting machining parameters in CNC turning operation.

scholarly journals Comparative Characteristics of Ductile Iron and Austempered Ductile Iron Modeled by Neural Network

Materials ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 2864 ◽  
Author(s):  
Borislav Savkovic ◽  
Pavel Kovac ◽  
Branislav Dudic ◽  
Michal Gregus ◽  
Dragan Rodic ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cutting Force ◽  
Ductile Iron ◽  
Cutting Forces ◽  
Austempered Ductile Iron ◽  
Depth Of Cut ◽  
Cutting Force Components ◽  
Artificial Neural ◽  
Force Components

Experimental research of cutting force components during dry face milling operations are presented in the paper. The study was provided when milling of ductile cast iron alloyed with copper and its austempered ductile iron after the proper austempering process. In the study, virtual instrumentation designed for cutting forces components monitoring was used. During the research, orthogonal cutting forces components versus time were monitored and relationship of cutting forces components versus speed, feed and depth of cut were determined by artificial neural network and response surface methodology. An analysis was made regarding the consistency of the measured cutting forces and the values obtained from the model supported by an artificial neural network for the investigated interval of the cutting regime. Based on the results, an analysis of the feasibility of the application of austempered ductile iron in the industrial sector with the aspect of machinability as well as the application of the models based on artificial intelligence, was given. At the end of the presentation, the influence of the aforementioned cutting regimes on cutting force components is presented as well.

scholarly journals Modelling of Rotary EDM Process Parameters of Inconel 718 Using Artificial Neural Networks

Mechanika ◽  
2020 ◽  
Vol 26 (6) ◽  
pp. 540-544
Author(s):  
Jayaraj JEEVAMALAR ◽  
Sundaresan RAMABALAN ◽  
Chinnamuthu SENTHILKUMAR
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Inconel 718 ◽  
Machining Process ◽  
Artificial Neural ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Matlab Programming ◽  
The Relationship

Modelling is used for correlating the relationship between the input process parameters and the output responses during the machining process. To characterize real-world systems of considerable complexity, an Artificial Neural Network (ANN) model is regularly used to replace the mathematical approximation of the relationship. This paper explains the methodological procedure and the outcome of the ANN modeling process for Electrical Discharge Drilling of Inconel 718 superalloy and hollow tubular copper as tool electrode. The most important process parameters in this work are peak current, pulse on time and pulse off time with machining performances of material removal rate and surface roughness. The experiments were performed by L20 Orthogonal Array. In such conditions, an Artificial Neural Network model is developed using MATLAB programming on the Feed Forward Back Propagation technique was used to predict the responses. The experimental data were separated into three parts to train, test the network and validate the model. The developed model has been confirmed experimentally for training and testing in considering the number of iterations and mean square error convergence criteria. The developed model results are to approximate the responses fairly exactly. The model has the mean correlation coefficient of 0.96558. Results revealed that the proposed model can be used for the prediction of the complex EDM drilling process.

Forecasting of Cutting Forces in Dental Adjustment of Ceramic Prostheses Using an Artificial Neural Network

2010 ◽  
Vol 152-153 ◽  
pp. 1687-1690
Author(s):  
Jian Hui Peng ◽  
Xiao Fei Song ◽  
Ling Yin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cutting Forces ◽  
High Speed ◽  
Cutting Process ◽  
Computer Assisted ◽  
Ann Model ◽  
Operational Parameters ◽  
Artificial Neural

Intraoral adjustment of ceramic prostheses involving cutting process is a central procedure in restorative dentistry because the quality of ceramic prostheses depends on the cutting process. In this paper, an artificial neural network (ANN) model was developed for the first time to forecast the dynamic forces in dental cutting process as functions of clinical operational parameters. The predicted force values were compared with the measured values in in vitro dental cutting of porcelain prostheses obtained using a novel two-degrees-of-freedom computer-assisted testing apparatus with a high-speed dental handpiece and diamond burs. The results indicate that there existed nonlinear relationships between the cutting forces and clinical operational parameters. It is found that the ANN-forecasted forces were in good agreement with the experiment-measured values. This indicates that the established ANN model can provide insights into the force-related process assessment and forecast for clinical dental cutting of ceramic prostheses.

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