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.

Intelligent Modeling of Thrust Force in Drilling Process

2006 ◽  
Vol 128 (4) ◽  
pp. 846-855 ◽  
Author(s):  
Ye Sheng ◽  
Masayoshi Tomizuka
Keyword(s):  
Thrust Force ◽  
Head Position ◽  
Drilling Process ◽  
Position Information ◽  
Force Prediction ◽  
Intelligent Modeling ◽  
Gain Variation ◽  
Proposed Model ◽  
Modeling Strategy ◽  
Wide Operating

In this paper, an intelligent modeling strategy for thrust force in drilling process is proposed. First of all, neural network (NN) models are developed to model the thrust force in drilling process. Second, drill head position information is included in the NN model to get better force prediction accuracy for entrance and exit drilling stages. Third, a fuzzy switching strategy is proposed to deal with the gain variation problem due to transitions from one drilling stage to another. Finally, gain variation due to drill wear is studied and the related modeling strategy is developed. Simulation and experimental results show that the proposed model works well over a wide operating range.

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.

Pattern Matching Synthesis As an Automated Approach to Mechanism Design

1989 ◽  
Author(s):  
D. A. Hoeltzel ◽  
W.-H. Chieng
Keyword(s):  
Neural Network ◽  
Mechanism Design ◽  
Pattern Matching ◽  
Network Models ◽  
Hopfield Neural Network ◽  
Neural Network Models ◽  
Knowledge Based ◽  
Synthesis Of Mechanisms ◽  
New Knowledge ◽  
Programming Techniques

Abstract A new knowledge-based approach for the synthesis of mechanisms, referred to as Pattern Matching Synthesis, has been developed based on committee machine and Hopfield neural network models of pattern matching applied to coupler curves. Computational tests performed on a dimensionally parameterized four bar mechanism have yielded 15 distinct coupler curve groups (patterns) from a total of 356 generated coupler curves. This innovative approach represents a first step toward the automation of mapping structure-to-function in mechanism design based on the application of artificial intelligence programming techniques.

Identification of Tomato Leaf Disease Detection using Pretrained Deep Convolutional Neural Network Models

2020 ◽  
Vol 21 (4) ◽  
pp. 625-635
Author(s):  
Anandhakrishnan T ◽  
Jaisakthi S.M Murugaiyan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Early Stage ◽  
Computing Time ◽  
Network Models ◽  
Deep Convolutional Neural Network ◽  
Neural Network Models ◽  
Leaf Disease ◽  
Proposed Model ◽  
Deep Cnn

In this paper, we proposed a plant leaf disease identification model based on a Pretrained deep convolutional neural network (Deep CNN). The Deep CNN model is trained using an open dataset with 10 different classes of tomato leaves We observed that overall architectures which can increase the best performance of the model. The proposed model was trained using different training epochs, batch sizes and dropouts. The Xception has attained maximum accuracy compare with all other approaches. After an extensive simulation, the proposed model achieves classification accuracy better. This accuracy of the proposed work is greater than the accuracy of all other Pretrained approaches. The proposed model is also tested with respect to its consistency and reliability. The set of data used for this work was collected from the plant village dataset, including sick and healthy images. Models for detection of plant disease should predict the disease quickly and accurately in the early stage itself so that a proper precautionary measures can be applied to avoid further spread of the diseases. So, to reduce the main issue about the leaf diseases, we can analyze distinct kinds of deep neural network architectures in this research. From the outcomes, Xception has a constantly improving more to enhance the accuracy by increasing the number of epochs, without any indications of overfitting and decreasein quality. And Xception also generated a fine 99.45% precision in less computing time.

scholarly journals A Novel CapsNet based Image Reconstruction and Regression Analysis

2020 ◽  
Vol 2 (3) ◽  
pp. 156-164 ◽  
Author(s):  
Dr. Akey Sungheetha ◽  
Dr. Rajesh Sharma R
Keyword(s):  
Neural Network ◽  
Regression Analysis ◽  
Network Models ◽  
Superior Performance ◽  
Neural Network Models ◽  
Innovative Method ◽  
Proposed Model ◽  
Retrieval Efficiency ◽  
High Level ◽  
Better Than

In the field of image processing, all types of computation models are almost evolved to solve the issues through encoded neurons. However, compared with decoding orientation and regression analysis, still the doors are open due to its complexity. At present technologies uses two steps such as, decoding the intermediate terms and reconstruction using decoded information. The performance in terms of regression analysis is lagging due to the decoded intermediate terms. Conventional neural network models perform better in feature classification and representation, though the performance is reduced while handling high level features. Considering these issues in image classification and regression, the proposed model is designed with capsule network as an innovative method which is suitable to handle high level features. The experimental results of the proposed model are compared with conventional neural network models such as BPNN and CNN to validate the superior performance. The proposed model achieves better retrieval efficiency of 95.4% which is much better than other neural network models.

A Novel Hybrid Model Using RBF and PSO for Net Asset Value Prediction

2018 ◽  
pp. 54-72
Author(s):  
C. M. Anish ◽  
Babita Majhi ◽  
Ritanjali Majhi
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Performance Measure ◽  
Neural Network Models ◽  
Proposed Model ◽  
Derivative Free ◽  
Asset Value ◽  
Net Asset Value ◽  
Small Investors ◽  
The Cost

Net asset value (NAV) prediction is an important area of research as small investors are doing investment in there, Literature survey reveals that very little work has been done in this field. The reported literature mainly used various neural network models for NAV prediction. But the derivative based learning algorithms of these reported models have the problem of trapping into the local solution. Hence in chapter derivative free algorithm, particle swarm optimization is used to update the parameters of radial basis function neural network for prediction of NAV. The positions of particles represent the centers, spreads and weights of the RBF model and the minimum MSE is used as the cost function. The convergence characteristics are obtained to show the performance of the model during training phase. The MAPE and RMSE value are calculated during testing phase to show the performance of the proposed RBF-PSO model. These performance measure exhibits that the proposed model is better model in comparison to MLANN, FLANN and RBFNN models.

A Novel Hybrid Model Using RBF and PSO for Net Asset Value Prediction

2018 ◽  
pp. 1031-1049 ◽  
Author(s):  
C. M. Anish ◽  
Babita Majhi ◽  
Ritanjali Majhi
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Performance Measure ◽  
Neural Network Models ◽  
Proposed Model ◽  
Derivative Free ◽  
Asset Value ◽  
Net Asset Value ◽  
Small Investors ◽  
The Cost

Net asset value (NAV) prediction is an important area of research as small investors are doing investment in there, Literature survey reveals that very little work has been done in this field. The reported literature mainly used various neural network models for NAV prediction. But the derivative based learning algorithms of these reported models have the problem of trapping into the local solution. Hence in chapter derivative free algorithm, particle swarm optimization is used to update the parameters of radial basis function neural network for prediction of NAV. The positions of particles represent the centers, spreads and weights of the RBF model and the minimum MSE is used as the cost function. The convergence characteristics are obtained to show the performance of the model during training phase. The MAPE and RMSE value are calculated during testing phase to show the performance of the proposed RBF-PSO model. These performance measure exhibits that the proposed model is better model in comparison to MLANN, FLANN and RBFNN models.

scholarly journals Disease Identification in Chilli Leaves using Machine Learning Techniques

2019 ◽  
Vol 9 (1S3) ◽  
pp. 325-329
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Detection System ◽  
Network Models ◽  
Detection Algorithm ◽  
Machine Learning Techniques ◽  
Neural Network Models ◽  
The Past ◽  
Learning Techniques ◽  
Proposed Model

Crop diseases reduce the yield of the crop or may even kill it. Over the past two years, as per the I.C.A.R, the production of chilies in the state of Goa has reduced drastically due to the presence of virus. Most of the plants flower very less or stop flowering completely. In rare cases when a plant manages to flower, the yield is substantially low. Proposed model detects the presence of disease in crops by examining the symptoms. The model uses an object detection algorithm and supervised image recognition and feature extraction using convolutional neural network to classify crops as infected or healthy. Google machine learning libraries, TensorFlow and Keras are used to build neural network models. An Android application is developed around the model for the ease of using the disease detection system.

Multi-task learning model for aspect term extraction and aspect polarity classification based on dual-labels

2020 ◽  
Vol 39 (3) ◽  
pp. 2763-2774
Author(s):  
Biqing Zeng ◽  
Feng Zeng ◽  
Heng Yang ◽  
Wu Zhou ◽  
Ruyang Xu
Keyword(s):  
Language Processing ◽  
Network Models ◽  
Learning Model ◽  
Neural Network Models ◽  
Task Learning ◽  
Term Extraction ◽  
Proposed Model ◽  
Great Performance ◽  
Benchmark Datasets ◽  
Polarity Classification

Aspect-based sentiment analysis (ABSA) is a hot and significant task of natural language processing, which is composed of two subtasks, the aspect term extraction (ATE) and aspect polarity classification (APC). Previous researches generally studied two subtasks independently and designed neural network models for ATE and APC respectively. However, it integrates various manual features into the model, which will consume plenty of computing resources and labor. Moreover, the quality of the ATE results will affect the performance of APC. This paper proposes a multi-task learning model based on dual auxiliary labels for ATE and APC. In this paper, general IOB labels, and sentimental IOB labels are equipped to efficiently solve both ATE and APC tasks without manual features adopted. Experiments are conducted on two general ABSA benchmark datasets of SemEval-2014. The experimental results reveal that the proposed model is of great performance and efficient for both ATE and APC tasks compared to the main baseline models.

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