MULTI-PARAMETER ANN MODEL FOR FLAT-END MILLING

2008 ◽  
Vol 32 (3-4) ◽  
pp. 523-536 ◽  
Author(s):  
Hazim El Mounayri ◽  
M. Affan Badar ◽  
Gustavo A. Rengifo
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Cutting Force ◽  
Process Parameters ◽  
End Milling ◽  
Predictive Ability ◽  
Ann Model ◽  
Network Output ◽  
Input Variables ◽  
Artificial Neural Network Ann

The quality, productivity and safety of machining can be significantly improved through the optimization of cutting conditions. The first step in achieving such an objective is the development of accurate and reliable models for predicting the critical process parameters. In this paper, an innovative Artificial Neural Network (ANN) model that predicts both cutting force and surface roughness in end milling is developed and validated. A set of five input variables is selected to represent the machining conditions while twelve quantities representing two key process parameters, namely, cutting force and surface roughness, form the variables of the network output. Full factorial design of experiments is used to generate data for both training and validation. Successful training of the neural network is demonstrated through comparison of simulated and experimental results for four different output variables, namely cutting force, surface roughness, feed marks, and tooth passing frequency. The predictive ability of the model is verified experimentally by comparing simulated output variables with their experimental counterparts. A good agreement is observed.

An AI-Based Model for Predicting Instananeous Cutting Force in End Milling

Manufacturing ◽  
2002 ◽  
Author(s):  
Hazim El-Mounayri ◽  
Vipul Tandon
Keyword(s):  
Cutting Force ◽  
Frequency Domain ◽  
Cutting Forces ◽  
End Milling ◽  
Depth Of Cut ◽  
Feed Speed ◽  
Ann Model ◽  
Force Signal ◽  
Input Variables ◽  
Artificial Neural Network Ann

An Artificial Neural Network (ANN) model is developed to accurately predict the instantaneous cutting forces in flat end milling. A unique frequency domain approach is presented and is seen to simulate instantaneous cutting forces reasonably well. A set of eight input variables is chosen to represent the machining conditions and frequency domain parameters of the cutting force signal are generated. Three input parameters are varied, namely Feed, Speed and Depth of Cut. Four output parameters are suggested as a sufficient set to adequately reproduce the instantaneous cutting forces. Exhaustive experimentation is conducted to collect data (consisting of Fx, Fy, and Fz) to train and validate the model.

Modeling of lime production process using artificial neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Rana Shahramfar ◽  
Elham Heidari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
High Energy ◽  
Ann Model ◽  
Artificial Neural ◽  
Rotary Kilns ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

scholarly journals Modeling the concentration of carbonyl of ethylene propylene diene monomer during the thermal aging using artificial neural network

2021 ◽  
Vol 03 (01) ◽  
pp. 45-52
Author(s):  
Hadjira Maouz ◽  
◽  
Asma Adda ◽  
Salah Hanini ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Thermal Aging ◽  
Training Data ◽  
Ethylene Propylene Diene Monomer ◽  
Ann Model ◽  
Ethylene Propylene ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

The concentration of carbonyl is one of the most important properties contributing to the detection of the thermal aging of polymer ethylene propylene diene monomer (EPDM). In this publication, an artificial neural network (ANN) model was developed to predict concentration of carbenyl during the thermal aging of EPDM using a database consisting of seven input variables. The best fitting training data was obtained with the architecture of (7 inputs neurons, 10 hidden neurons and 1 output neuron). A Levenberg Marquardt learning (LM) algorithm, hyperbolic tangent transfer function were used at the hidden and output layer respectively. The optimal ANN was obtained with a high correlation coefficient R= 0.995 and a very low root mean square error RMSE = 0.0148 mol/l during the generalization phase. The comparison between the experimental and calculated results show that the ANN model is able of predicted the concentration of carbonyl during the thermal aging of ethylene propylene diene monomer

Prediction of Surface Roughness and Coefficient of Friction Using Artificial Neural Network in Tribotesting of Bio-Lubricants

2019 ◽  
Vol 895 ◽  
pp. 52-57 ◽  
Author(s):  
Prasanna Vineeth Bharadwaj ◽  
T.P. Jeevan ◽  
P.S. Suvin ◽  
S.R. Jayaram
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Coefficient Of Friction ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Trained Neural Network ◽  
Lubrication Conditions ◽  
Good Agreement

Tribotesting is necessary to understand the behaviour of the material under various operating lubrication conditions. This paper deals with the training of an artificial neural network (ANN) model with Bio-lubricant properties and machining conditions for prediction of surface roughness and coefficient of friction in Tribotesting by Tool chip Tribometer. Experimental results obtained from Tool chip tribometer for tested bio-lubricants are compared with those obtained by ANN prediction. A good agreement in results recommends that a well trained neural network is competent enough to predict the parameters in Tribotesting process.

Optimization of Process Parameters for Cutting Force for Aluminium 7010 in CNC End Milling Process

2018 ◽  
Vol 10 (6) ◽  
Author(s):  
M. Kishanth ◽  
P. Rajkamal ◽  
D. Karthikeyan ◽  
K. Anand
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Process Parameters ◽  
End Milling ◽  
Milling Process ◽  
Machining Process ◽  
Depth Of Cut ◽  
Optimization Of Process Parameters ◽  
Cnc End Milling ◽  
End Milling Process

In this paper CNC end milling process have been optimized in cutting force and surface roughness based on the three process parameters (i.e.) speed, feed rate and depth of cut. Since the end milling process is used for abrading the wear caused is very high, in order to reduce the wear caused by high cutting force and to decrease the surface roughness, the optimization is much needed for this process. Especially for materials like aluminium 7010, this kind of study is important for further improvement in machining process and also it will improve the stability of the machine.

Prediction of collapsibility of moulds and cores of CO2 sands using a neural network

2003 ◽  
Vol 217 (4) ◽  
pp. 475-487 ◽  
Author(s):  
R K Ohdar ◽  
P T Pushp
Keyword(s):  
Neural Network ◽  
Test Data ◽  
Process Parameters ◽  
Coal Dust ◽  
Back Propagation ◽  
Maximum Error ◽  
Ann Model ◽  
Input Process ◽  
Artificial Neural Network Ann ◽  
Hidden Neurons

The CO2 process of making sand moulds and cores is a well-established process and suitable for all types of foundry. However, the collapsibility of CO2 sand is quite poor. A variety of additives are used to improve collapsibility of CO2 sands. Several other process parameters also affect collapsibility of CO2 sands. In the present investigation an attempt has been made to use an artificial neural network (ANN) model for prediction of the collapsibility of CO2 sand. Experiments were conducted with various input process parameters, such as binder content, gassing time, temperature and additive content using three different additives, namely coal dust, dextrin and alumina. The objective of the experiments was to generate basic data to train a back-propagation ANN model and finally predict collapsibility in terms of retained compressive strength of CO2 sands for the test data. A three-layer neural network model with six input neurons corresponding to six input process parameters, one output neuron corresponding to collapsibility and 19 hidden neurons has been suggested, which gives a maximum error of 2 per cent in prediction of test data. Results indicate that prediction of the collapsibility of CO2 sand with an ANN model is feasible. Predicted values match experimental values quite closely.

Sedimentation Load Analysis Using ANN and GA

2011 ◽  
Vol 110-116 ◽  
pp. 2693-2698 ◽  
Author(s):  
Dillip Kumar Ghose ◽  
P.C. Swain ◽  
Sudhansu Sekhar Panda
Keyword(s):  
Neural Network ◽  
Process Parameters ◽  
Water Discharge ◽  
Load Condition ◽  
Optimal Level ◽  
Daily Basis ◽  
Ann Model ◽  
Artificial Neural Network Ann ◽  
Trained Neural Network ◽  
The Given

Artificial Neural Network (ANN) model is used to predict the suspended sediment load for the survey data collected on daily basis in the river Mahanadi. Genetic algorithm has been used to find the optimal level of process parameters such as water discharge and temperature for a minimum sedimentation load condition. Optimal level of process parameters obtained from the GA has been used in a trained neural network to obtain the sedimentation load condition. A comparative analysis is then made between GA and ANN for achieving minimum sedimentation load with the given process parameters.

scholarly journals DEVELOPMENT OF NUMERICAL MODELS FOR THE PREDICTION OF TEMPERATURE AND SURFACE ROUGHNESS DURING THE MACHINING OPERATION OF TITANIUM ALLOY (Ti6Al14V)

2020 ◽  
Vol 60 (5) ◽  
pp. 369-390
Author(s):  
Ilesanmi Daniyan ◽  
Isaac Tlhabadira ◽  
Khumbulani Mpofu ◽  
Adefemi Adeodu
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Titanium Alloy ◽  
Response Surface ◽  
Process Parameters ◽  
Numerical Models ◽  
Physical Experiments ◽  
Artificial Neural Network Ann

Temperature and surface roughness are important factors, which determine the degree of machinability and the performance of both the cutting tool and the work piece material. In this study, numerical models obtained from the Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques were used for predicting the magnitude of the temperature and surface roughness during the machining operation of titanium alloy (Ti6Al4V). The design of the numerical experiment was carried out using the Response Surface Methodology (RSM) for the combination of the process parameters while the Artificial Neural Network (ANN) with 3 input layers, 10 sigmoid hidden neurons and 3 linear output neurons were employed for the prediction of the values of temperature. The ANN was iteratively trained using the Levenberg-Marquardt backpropagation algorithm. The physical experiments were carried out using a DMU80monoBLOCK Deckel Maho 5-axis CNC milling machine with a maximum spindle speed of 18 000 rpm. A carbide-cutting insert (RCKT1204MO-PM S40T) was used for the machining operation. A professional infrared video thermometer with an LCD display and camera function (MT 696) with infrared temperature range of −50−1000 °C, was employed for the temperature measurement while the surface roughness of the work pieces were measured using the Mitutoyo SJ – 201, surface roughness machine. The results obtained indicate that there is high degree of agreement between the values of temperature and surface roughness measured from the physical experiments and the predicted values obtained using the ANN and RSM. This signifies that the developed RSM and ANN models are highly suitable for predictive purposes. This work can find application in the production and manufacturing industries especially for the control, optimization and process monitoring of process parameters.

scholarly journals Surface Roughness Analysis and Prediction with an Artificial Neural Network Model for Dry Milling of Co–Cr Biomedical Alloys

Materials ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6361
Author(s):  
Manuela-Roxana Dijmărescu ◽  
Bogdan Felician Abaza ◽  
Ionelia Voiculescu ◽  
Maria-Cristina Dijmărescu ◽  
Ion Ciocan
Keyword(s):  
Surface Roughness ◽  
Prediction Models ◽  
End Milling ◽  
Design Method ◽  
Cutting Speed ◽  
Process Time ◽  
Depth Of Cut ◽  
Ann Model ◽  
Roughness Analysis ◽  
Input Variables

The aim of this paper is to conduct an experimental study in order to obtain a roughness (Ra) prediction model for dry end-milling (with an AlTiCrSiN PVD-coated tool) of the Co–28Cr–6Mo and Co–20Cr–15W–10Ni biomedical alloys, a model that can contribute to more quickly obtaining the desired surface quality and shortening the manufacturing process time. An experimental plan based on the central composite design method was adopted to determine the influence of the axial depth of cut, feed per tooth and cutting speed process parameters (input variables) on the Ra surface roughness (response variable) which was recorded after machining for both alloys. To develop the prediction models, statistical techniques were used first and three prediction equations were obtained for each alloy, the best results being achieved using response surface methodology. However, for obtaining a higher accuracy of prediction, ANN models were developed with the help of an application made in LabView for roughness (Ra) prediction. The primary results of this research consist of the Co–28Cr–6Mo and Co–20Cr–15W–10Ni prediction models and the developed application. The modeling results show that the ANN model can predict the surface roughness with high accuracy for the considered Co–Cr alloys.

Monitoring-System Development of Gillnet Using Artificial Neural Network

2020 ◽  
Author(s):  
Chungkuk Jin ◽  
HanSung Kim ◽  
JeongYong Park ◽  
MooHyun Kim ◽  
Kiseon Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Big Data ◽  
Learning Algorithm ◽  
System Development ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

Abstract This paper presents a method for detecting damage to a gillnet based on sensor fusion and the Artificial Neural Network (ANN) model. Time-domain numerical simulations of a slender gillnet were performed under various wave conditions and failure and non-failure scenarios to collect big data used in the ANN model. In training, based on the results of global performance analyses, sea states, accelerations of the net assembly, and displacements of the location buoy were selected as the input variables. The backpropagation learning algorithm was employed in training to maximize damage-detection performance. The output of the ANN model was the identification of the particular location of the damaged net. In testing, big data, which were not used in training, were utilized. Well-trained ANN models detected damage to the net even at sea states that were not included in training with high accuracy.

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