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

2018 ◽  
Vol 178 ◽  
pp. 01008
Author(s):  
Panagiotis Kyratsis ◽  
Nikolaos Efkolidis ◽  
Daniel Ghiculescu ◽  
Konstantinos Kakoulis
Keyword(s):  
Thrust Force ◽  
Network Models ◽  
Cutting Parameters ◽  
Drilling Process ◽  
Neural Network Models ◽  
Cutting Velocity ◽  
Artificial Neural ◽  
Hidden Layer ◽  
The One ◽  
Tool Diameter

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.

scholarly journals Radyal Tabanlı Yapay Sinir Ağlarının Kurutma Kinetiğinin Modellenmesinde Kullanımı

2020 ◽  
Vol 8 (2) ◽  
pp. 511
Author(s):  
Adil Koray Yıldız ◽  
Muhammed Taşova ◽  
Hakan Polatcı
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Models ◽  
Drying Kinetics ◽  
Hot Pepper ◽  
Neural Network Models ◽  
Drying Time ◽  
Drying Behavior ◽  
Artificial Neural ◽  
Hidden Layer

Drying method is preferred in agricultural products since it provides advantages in many processes such as increasing the strength of products, transporting and storing. It is necessary to estimate the drying behavior of the products in order to achieve the best drying without reducing the product quality. For this reason, many numerical drying models have been developed to estimate the drying kinetics of the products. Recently, artificial neural networks have been widely used for the development of these models. Artificial neural networks are mathematical models that work in a similar way to natural neuron cells. Radial based artificial neural networks are radial based activation functions in the transition to the hidden layer unlike other networks. In this study, modeling of drying kinetics with radial based networks was investigated. For the experiment, red hot pepper (Capsicum annuum L.) was dipped in boiled water and microwave pretreatments and, then dried in the oven at 65°C. The absorbable moisture values were calculated during the drying period. The radial based artificial neural network models were trained with the drying time values as input and the absorbable moisture values as output. The study was carried out with two data sets including all data and only the average. In trainings with all data, R value of the best model was calculated as 0.9566. R was calculated as 0.9998 with average data.

Intelligent Modeling of Drilling Process: A Knowledge-Based Neural Strategy With Fuzzy Switches

2001 ◽  
Author(s):  
Ye Sheng ◽  
Oliver Nelles ◽  
Masayoshi Tomizuka
Keyword(s):  
Thrust Force ◽  
Network Models ◽  
Drilling Process ◽  
Neural Network Models ◽  
Intelligent Modeling ◽  
Knowledge Based ◽  
Gain Variation ◽  
Proposed Model ◽  
Modeling Strategy ◽  
Wide Operating

Abstract In this paper, an intelligent modeling strategy for thrust force in drilling process is developed. Neural network models and a fuzzy switching strategy are presented to deal with the gain variation problem due to transitions from one drilling stage to another. Gain variation due to drill wear and the related modeling strategy are studied. Simulation and experimental results show that the proposed model works well over a wide operating range.

scholarly journals Analysis of the Machinability of Carbon Fiber Composite Materials in Function of Tool Wear and Cutting Parameters Using the Artificial Neural Network Approach

Materials ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 2747 ◽  
Author(s):  
Norberto Feito ◽  
Ana Muñoz-Sánchez ◽  
Antonio Díaz-Álvarez ◽  
José Antonio Loya
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Composite Materials ◽  
Carbon Fiber ◽  
Tool Wear ◽  
Thrust Force ◽  
Cutting Parameters ◽  
Drilling Process ◽  
Artificial Neural ◽  
The Impact

Local delamination is the most undesirable damage associated with drilling carbon fiber reinforced composite materials (CFRPs). This defect reduces the structural integrity of the material, which affects the residual strength of the assembled components. A positive correlation between delamination extension and thrust force during the drilling process is reported in literature. The abrasive effect of the carbon fibers modifies the geometry of the fresh tool, which increases the thrust force and, in consequence, the induced damage in the workpiece. Using a control system based on an artificial neural network (ANN), an analysis of the influence of the tool wear in the thrust force during the drilling of CFRP laminate to reduce the damage is developed. The spindle speed, feed rate, and drill point angle are also included as input parameters of the study. The training and testing of the ANN model are carried out with experimental drilling tests using uncoated carbide helicoidal tools. The data were trained using error-back propagation-training algorithm (EBPTA). The use of the neural network rapidly provides results of the thrust force evolution in function of the tool wear and cutting parameters. The obtained results can be used by the industry as a guide to control the impact of the wear of the tool in the quality of the finished workpiece.

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

scholarly journals Effect of Cutting Parameters and Tool Geometry on the Performance Analysis of One-Shot Drilling Process of AA2024-T3

Metals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 854
Author(s):  
Muhammad Aamir ◽  
Khaled Giasin ◽  
Majid Tolouei-Rad ◽  
Israr Ud Din ◽  
Muhammad Imran Hanif ◽  
...  
Keyword(s):  
Feed Rate ◽  
Surface Defects ◽  
Thrust Force ◽  
Cutting Tools ◽  
Chip Thickness ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Machining Process ◽  
Tool Geometry ◽  
Drilling Process

Drilling is an important machining process in various manufacturing industries. High-quality holes are possible with the proper selection of tools and cutting parameters. This study investigates the effect of spindle speed, feed rate, and drill diameter on the generated thrust force, the formation of chips, post-machining tool condition, and hole quality. The hole surface defects and the top and bottom edge conditions were also investigated using scan electron microscopy. The drilling tests were carried out on AA2024-T3 alloy under a dry drilling environment using 6 and 10 mm uncoated carbide tools. Analysis of Variance was employed to further evaluate the influence of the input parameters on the analysed outputs. The results show that the thrust force was highly influenced by feed rate and drill size. The high spindle speed resulted in higher surface roughness, while the increase in the feed rate produced more burrs around the edges of the holes. Additionally, the burrs formed at the exit side of holes were larger than those formed at the entry side. The high drill size resulted in greater chip thickness and an increased built-up edge on the cutting tools.

scholarly journals PSIII-5 Comparison of Bivariate Machine Learning and Linear Model for Genomic Prediction with Different Heritability, QTL and SNP Panel Scenarios

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 230-231
Author(s):  
Sunday O Peters ◽  
Mahmut Sinecan ◽  
Kadir Kizilkaya ◽  
Milt Thomas
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Normal Distribution ◽  
Genomic Prediction ◽  
Network Models ◽  
Bivariate Normal Distribution ◽  
Snp Markers ◽  
Neural Network Models ◽  
Bivariate Normal ◽  
Artificial Neural

Abstract This simulation study used actual SNP genotypes on the first chromosome of Brangus beef cattle to simulate 0.50 genetically correlated two traits with heritabilities of 0.25 and 0.50 determined either by 50, 100, 250 or 500 QTL and then aimed to compare the accuracies of genomic prediction from bivariate linear and artificial neural network with 1 to 10 neurons models based on G genomic relationship matrix. QTL effects of 50, 100, 250 and 500 SNPs from the 3361 SNPs of 719 animals were sampled from a bivariate normal distribution. In each QTL scenario, the breeding values (Σgijβj) of animal i for two traits were generated by using genotype (gij) of animal i at QTL j and the effects (βj) of QTL j from a bivariate normal distribution. Phenotypic values of animal i for traits were generated by adding residuals from a bivariate normal distribution to the breeding values of animal i. Genomic predictions for traits were carried out by bivariate Feed Forward MultiLayer Perceptron ANN-1–10 neurons and linear (GBLUP) models. Three sets of SNP panels were used for genomic prediction: only QTL genotypes (Panel1), all SNP markers, including the QTL (Panel2), and all SNP markers, excluding the QTL (Panel3). Correlations from 10-fold cross validation for traits were used to assess predictive ability of bivariate linear (GBLUP) and artificial neural network models based on 4 QTL scenarios with 3 Panels of SNP panels. Table 1 shows that the trait with high heritability (0.50) resulted in higher correlation than the trait with low heritability (0.25) in bivariate linear (GBLUP) and artificial neural network models. However, bivariate linear (GBLUP) model produced higher correlation than bivariate neural network. Panel1 performed the best correlations for all QTL scenarios, then Panel2 including QTL and SNP markers resulted in better prediction than Panel3.

scholarly journals A Force Sensorless Method for CFRP/Ti Stack Interface Detection during Robotic Orbital Drilling Operations

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Qiang Fang ◽  
Ze-Min Pan ◽  
Bing Han ◽  
Shao-Hua Fei ◽  
Guan-Hua Xu ◽  
...  
Keyword(s):  
Cutting Force ◽  
Thrust Force ◽  
Cutting Parameters ◽  
Force Model ◽  
Drilling Process ◽  
Reinforced Plastics ◽  
Orbital Drilling ◽  
Drilling Parameters ◽  
Drilling Operations ◽  
Machining Properties

Drilling carbon fiber reinforced plastics and titanium (CFRP/Ti) stacks is one of the most important activities in aircraft assembly. It is favorable to use different drilling parameters for each layer due to their dissimilar machining properties. However, large aircraft parts with changing profiles lead to variation of thickness along the profiles, which makes it challenging to adapt the cutting parameters for different materials being drilled. This paper proposes a force sensorless method based on cutting force observer for monitoring the thrust force and identifying the drilling material during the drilling process. The cutting force observer, which is the combination of an adaptive disturbance observer and friction force model, is used to estimate the thrust force. An in-process algorithm is developed to monitor the variation of the thrust force for detecting the stack interface between the CFRP and titanium materials. Robotic orbital drilling experiments have been conducted on CFRP/Ti stacks. The estimate error of the cutting force observer was less than 13%, and the stack interface was detected in 0.25 s (or 0.05 mm) before or after the tool transited it. The results show that the proposed method can successfully detect the CFRP/Ti stack interface for the cutting parameters adaptation.

Automatic Electrocardiographic Analysis Using Artificial Neural Network Models

2011 ◽  
Vol 403-408 ◽  
pp. 3587-3593
Author(s):  
T.V.K. Hanumantha Rao ◽  
Saurabh Mishra ◽  
Sudhir Kumar Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Network Models ◽  
Neural Network Models ◽  
Radial Basis ◽  
Artificial Neural ◽  
Artificial Neural Network Models ◽  
Self Organizing

In this paper, the artificial neural network method was used for Electrocardiogram (ECG) pattern analysis. The analysis of the ECG can benefit from the wide availability of computing technology as far as features and performances as well. This paper presents some results achieved by carrying out the classification tasks by integrating the most common features of ECG analysis. Four types of ECG patterns were chosen from the MIT-BIH database to be recognized, including normal sinus rhythm, long term atrial fibrillation, sudden cardiac death and congestive heart failure. The R-R interval features were performed as the characteristic representation of the original ECG signals to be fed into the neural network models. Two types of artificial neural network models, SOM (Self- Organizing maps) and RBF (Radial Basis Function) networks were separately trained and tested for ECG pattern recognition and experimental results of the different models have been compared. The trade-off between the time consuming training of artificial neural networks and their performance is also explored. The Radial Basis Function network exhibited the best performance and reached an overall accuracy of 93% and the Kohonen Self- Organizing map network reached an overall accuracy of 87.5%.

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