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 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.

An Experimental Study on Cutting Forces in the Threading and the Side Cut Turning With Coated and Uncoated Grades

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
Firat Kafkas
Keyword(s):  
Cutting Force ◽  
Cutting Forces ◽  
Three Dimensional ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Dynamic Force ◽  
Low Alloy Steels ◽  
Workpiece Material ◽  
Cutting Force Components ◽  
Force Components

The objective of this study is to obtain the cutting force components on the threading insert. The cutting force data used in the analysis are measured by a three-dimensional dynamic force dynamometer. The AISI 4140 and AISI 4340 low alloy steels are selected for the experiment on the threading and the side cut turning. The inserts used for testing is the TiAlN coated and uncoated grades. LT22NR35ISO type insert is used in the experiment. During the experiments, no cutting fluid and a constant spindle speed is used. The thread pitch and the depth of cut were kept fixed at 3.5 mm and 0.05 mm for the radial feed per pass, respectively. The study emphasizes on the effects on the workpiece material and the cutting tool grade of the cutting force components that occur during the threading. Also, these results are compared with the findings that are obtained during the side cut turning. It is determined that the measured primary cutting and radial forces during the threading are approximately three times bigger than those during the side cut turning, although feed forces during the threading are approximately 30 times lower compared with the side cut turning. The TiAlN coated WC/Co grade shows the best performance with respect to the cutting force components. The specific cutting forces are determined in order to understand the interference of chips that occur during the threading. With the increase in the cumulative radial feed, the corresponding specific cutting forces become higher. It is reasoned that the difference in the specific cutting forces results from the alteration of the interference of the flowing chips. The specific cutting forces decrease in the beginning of the threading and then increases with the cumulative radial feed. The results show that the interference of the chip flow influences the threading force components to a very large extent.

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.

Artificial neural networks back propagation algorithm for cutting force components predictions

2013 ◽  
Vol 14 (6) ◽  
pp. 431-439 ◽  
Author(s):  
Issam Hanafi ◽  
Francisco Mata Cabrera ◽  
Abdellatif Khamlichi ◽  
Ignacio Garrido ◽  
José Tejero Manzanares
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cutting Force ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Cutting Force Components ◽  
Artificial Neural ◽  
Force Components
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