Modelling of Drill Bit Temperature and Cutting Force in Drilling Process Using Artificial Neural Networks, pages: 333-340 (original) (raw)

Prediction of Temperature Distribution during Drilling Process Using Artificial Neural Network

World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 2015

Experimental & numeral study of temperature distribution during milling process, is important in milling quality and tools life aspects. In the present study the milling cross-section temperature is determined by using Artificial Neural Networks (ANN) according to the temperature of certain points of the work piece and the point specifications and the milling rotational speed of the blade. In the present work, at first three-dimensional model of the work piece is provided and then by using the Computational Heat Transfer (CHT) simulations, temperature in different nods of the work piece are specified in steady-state conditions. Results obtained from CHT are used for training and testing the ANN approach. Using reverse engineering and setting the desired x, y, z and the milling rotational speed of the blade as input data to the network, the milling surface temperature determined by neural network is presented as output data. The desired points temperature for different milling blade ...

Optimization of cutting parameters on drill bit temperature in drilling by Taguchi method

Measurement, 2013

This study applied artificial neural networks (ANN) to estimate the drill bit temperature and cutting force in drilling process using Firex® coated carbide and uncoated drills. Also, the effects of the different network structures in the modeling the drill bit temperature and cutting force were also investigated. The numbers of neuron in network structure of ANN models are 2-6-2, 2-5-2, 2-3-5-2, 2-5-4-2, 2-3-4-4-2 and 2-2-4-3-2 structures. The best ANN model, the 2-5-2 network structures in predicting the drill bit temperatures were obtained whereas; the 2-2-4-3-2 structures were found in predicting the cutting force. The empirical equations for the best ANN models in the prediction of drill bit temperature and cutting force were developed and the obtained results were confirmed. When the results of mathematical modelling are examined, the computed the drill bit temperature and cutting forces are observed to be apparently within acceptable values.

Multilayer Perceptron Artificial Neural Network (Mlpann) Model to Predict Temperature During Rotary Drilling

Journal of Mines, Metals and Fuels, 2023

In this paper, a multilayer perceptron neural network has been used to represent temperature measurement during rotary drilling of five types of rock samples. To forecast the temperature at various thermocouple depths, the experimentally collected data was standardized. Indicators of model performance was also obtained in order to assess the correctness of the model. One hidden layer and one output layer were employed with MLPANN, which has ten input parameters (bit diameter (DD), Spindle Speed (SS), Penetration Rate (PR), thrust, and torque) and rock properties. Levenberg Marquardt learning algorithm with transfer function of logsig is the most optimal neuron number of 10-16-1 was successfully forecasting the temperature with a correlation of 0.9936 and 0.9941 for training and testing algorithm during drilling after analysis based on the trial-anderror approach to identify the optimum algorithm. Ten input parameters, a logsig sigmoid transfer function, and the trainlm algorithm in this study provide good prediction ability with tolerable accuracy.

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.

Using artificial neural network models for the prediction of thrust force and torque in drilling operation of Al7075

MATEC Web of Conferences, 2018

This study investigates the thrust force (Fz) and torque (Mz) in a drilling process of an Al7075 workpiece using solid carbide tools (Kennametal KC7325), depending on the effects of crucial cutting parameters such as cutting velocity, feed rate and tool diameter of 10mm, 12mm and 14mm. Artificial neural networks (ANN) methodology is used in order to acquire mathematical models for both the thrust force (Fz) and torque (Mz) related to the drilling process. The ANN results showed that the best prediction topology of the network for the thrust force was the one with five neurons in the hidden layer, while for the case of Mz the best network topology for the prediction of the experimental values was the one with six neurons in the hidden layer. Based on the results acquired, the ANN models achieved accuracy of 1,96% and 1,95% for both the thrust force and torque measured, while the R coefficient for the prediction model of the thrust force is 0.99976 and 00.99981 for the torque. As a result they can be considered as very accurate and appropriate for their prediction.

Prediction of thrust forces and hole diameters using artificial neural networks in drilling of AISI D2 tool steel with cemented carbide tools

Academic Platform Journal of Engineering and Science, 2013

In this study, the effects of cutting speed, feed rate and different types of coating materials on thrust force and hole diameter were investigated in drilling of AISI D2 cold work tool steel. In addition, the thrust forces and hole diameters were predicted by artificial neural networks (ANN) using experimental data. Uncoated, TiN, TiAlN monolayer and TiAlN/TiN multi-layer coated cemented carbide drills with diameter of 5 mm were used in drilling experiments. The holes were drilled at different combinations of four cutting speeds (50, 55, 60, 65 m/min), two feed rates (0.063 and 0.08 mm/rev), and fixed depth of cut (7 mm). Experimental results showed that the lowest thrust forces and hole diameters were obtained with TiAlN/TiN multi-layer coated drills. After ANN training, it was found that the R 2 values are very close to 1 for both training and test sets. RMSE values are smaller than 0.03, and mean error values are smaller than 5% for the test set. This case shows that ANN is a powerful method for prediction of thrust forces and hole diameters.

Using of Artificial Neural Networks to Predict Drill Wear in machining processes

In machining operations a hard tool is engaged with work piece along with process. Tool is harder than work piece. However, tool wear occurrence in machining processes is inevitable. Tool wear will results in scraped parts and also it makes tool to weaken and then a tool failer will happen in the end. Therefore, an operator is needed to follow the process and change the tool when it is going to break. But this is a serious problem against automation. To create an automation system, we need to develop a monitoring system to predict tool wear rate by on-line and substitute it with an operator. In this paper by using of a wear model and experimental data and also motor current block diagram ,tool wear rate in drilling process will be predicted .To investigate the results, neural network method is used .The results compared with the real data show that the neural network results have a close fitness with the real data.

A Comparative Study between Regression and Neural Networks for Modeling Al6082-T6 Alloy Drilling

Machines

Apart from experimental research, the development of accurate and efficient models is considerably important in the field of manufacturing processes. Initially, regression models were significantly popular for this purpose, but later, the soft computing models were proven as a viable alternative to the established models. However, the effectiveness of soft computing models can be often dependent on the size of the experimental dataset, and it can be lower compared to that of the regression models for a small-sized dataset. In the present study, it is intended to conduct a comparison of the performance of various neural network models, such as the Multi-layer Perceptron (MLP), the Radial Basis Function Neural Network (RBF-NN), and the Adaptive Neuro-Fuzzy Inference System (ANFIS) models with the performance of a multiple regression model. For the development of the models, data from drilling experiments on an Al6082-T6 workpiece for various process conditions are employed, and the pe...

Methodology of neural network based modeling of machining processes

2010

Machining is the most important and widely used manufacturing process. As machining is very complex process, in recent years neural network based modeling has been preferred modeling of machining processes. This paper outlines and discusses the basic idea and concept of neural network modeling of machining processes. Furthermore this paper discusses the methodology of developing neural network model as well as proposing some guidelines for selecting the network training parameters and network architecture. For illustration purpose, simple neural prediction model for cutting power was developed and validated.

Predicting drill wear using an artificial neural network

International Journal of Advanced Manufacturing Technology, 2006

The present work deals with drill wear monitoring using an artificial neural network. A back propagation neural network (BPNN) has been used to predict the flank wear of high-speed steel (HSS) drill bits for drilling holes on copper work-piece. Experiments have been carried out over a wide range of cutting conditions and the effect of various process parameter like feedrate, spindle speed, and drill diameter on thrust force and torque has been studied. The data thus obtained from the experiments have been used to train a BPNN for wear prediction. The performance of the trained neural network has been tested with the experimental data, and has been found to be satisfactory.