Feature Extraction and Optimum Part Deposition Orientation for FDM

2015 ◽  
Vol 793 ◽  
pp. 642-646 ◽  
Author(s):  
Khairul Fauzi Karim ◽  
D. Hazry ◽  
A.H. Zulkifli ◽  
S. Faiz Ahmed ◽  
Zuradzman Mohamad Razlan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Fused Deposition Modeling ◽  
Support Structure ◽  
Fused Deposition ◽  
Deposition Modeling ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Selection Of

Support generation is an essential for Fused Deposition Modeling (FDM) process which is dependent on part deposition orientation. Various part deposition orientation result in formation of different support and non-support features. Present work focuses on extracting the support features containing Externally-Supported Features (ESF) which are able to determine the volume and number of support structure. The methodology proposed in this work uses these information as an input for Artificial Neural Network (ANN) in order to automate the selection of optimum part deposition orientation. The results produced in present methodology can be predicted and are in agreement with the results published earlier.

Knowledge-Based Design of Artificial Neural Network Topology for Additive Manufacturing Process Modeling: A New Approach and Case Study for Fused Deposition Modeling

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Hari P. N. Nagarajan ◽  
Hossein Mokhtarian ◽  
Hesam Jafarian ◽  
Saoussen Dimassi ◽  
Shahriar Bakrani-Balani ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Additive Manufacturing ◽  
Fused Deposition Modeling ◽  
Expert Knowledge ◽  
Fused Deposition ◽  
Knowledge Based ◽  
Deposition Modeling ◽  
Artificial Neural

Additive manufacturing (AM) continues to rise in popularity due to its various advantages over traditional manufacturing processes. AM interests industry, but achieving repeatable production quality remains problematic for many AM technologies. Thus, modeling different process variables in AM using machine learning can be highly beneficial in creating useful knowledge of the process. Such developed artificial neural network (ANN) models would aid designers and manufacturers to make informed decisions about their products and processes. However, it is challenging to define an appropriate ANN topology that captures the AM system behavior. Toward that goal, an approach combining dimensional analysis conceptual modeling (DACM) and classical ANNs is proposed to create a new type of knowledge-based ANN (KB-ANN). This approach integrates existing literature and expert knowledge of the AM process to define a topology for the KB-ANN model. The proposed KB-ANN is a hybrid learning network that encompasses topological zones derived from knowledge of the process and other zones where missing knowledge is modeled using classical ANNs. The usefulness of the method is demonstrated using a case study to model wall thickness, part height, and total part mass in a fused deposition modeling (FDM) process. The KB-ANN-based model for FDM has the same performance with better generalization capabilities using fewer weights trained, when compared to a classical ANN.

Knowledge-Based Optimization of Artificial Neural Network Topology for Process Modeling of Fused Deposition Modeling

2018 ◽  
Author(s):  
Hari P. N. Nagarajan ◽  
Hesam Jafarian ◽  
Azarakhsh Hamedi ◽  
Hossein Mokhtarian ◽  
Romaric Prod'hon ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fused Deposition Modeling ◽  
Performance Metrics ◽  
Expert Knowledge ◽  
Production Quality ◽  
Fused Deposition ◽  
Knowledge Based ◽  
Deposition Modeling ◽  
Artificial Neural

Additive manufacturing (AM) continues to rise in popularity due to its various advantages over traditional manufacturing processes. AM interests industry, but achieving repeatable production quality remains problematic for many AM technologies. Thus, modeling the influence of process variables on the production quality in AM can be highly beneficial in creating useful knowledge of the process and product. An approach combining dimensional analysis conceptual modeling, mutual information based analysis, experimental sampling, factors selection, and modeling based on Knowledge-Based Artificial Neural Network (KB-ANN) is proposed for Fused Deposition Modeling (FDM) process. KB-ANN reduces the excessive amount of training samples required in traditional neural networks. The developed KB-ANN’s topology for FDM, integrates existing literature and expert knowledge of the process. The KB-ANN is compared to conventional ANN using prescribed performance metrics. This research presents a methodology to concurrently perform experiments, classify influential factors, limit the effect of noise in the modeled system, and model using KB-ANN. This research can contribute to the qualification efforts of AM technologies.

scholarly journals A Comparison of Emotional Neural Network (ENN) and Artificial Neural Network (ANN) Approach for Rainfall-Runoff Modelling

2019 ◽  
Vol 5 (10) ◽  
pp. 2120-2130 ◽  
Author(s):  
Suraj Kumar ◽  
Thendiyath Roshni ◽  
Dar Himayoun
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Sets ◽  
Rainfall Runoff ◽  
Data Set ◽  
Discharge Data ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Emotional Neural Network ◽  
Selection Of

Reliable method of rainfall-runoff modeling is a prerequisite for proper management and mitigation of extreme events such as floods. The objective of this paper is to contrasts the hydrological execution of Emotional Neural Network (ENN) and Artificial Neural Network (ANN) for modelling rainfall-runoff in the Sone Command, Bihar as this area experiences flood due to heavy rainfall. ENN is a modified version of ANN as it includes neural parameters which enhance the network learning process. Selection of inputs is a crucial task for rainfall-runoff model. This paper utilizes cross correlation analysis for the selection of potential predictors. Three sets of input data: Set 1, Set 2 and Set 3 have been prepared using weather and discharge data of 2 raingauge stations and 1 discharge station located in the command for the period 1986-2014.  Principal Component Analysis (PCA) has then been performed on the selected data sets for selection of data sets showing principal tendencies.  The data sets obtained after PCA have then been used in the model development of ENN and ANN models. Performance indices were performed for the developed model for three data sets. The results obtained from Set 2 showed that ENN with R= 0.933, R2 = 0.870, Nash Sutcliffe = 0.8689, RMSE = 276.1359 and Relative Peak Error = 0.00879 outperforms ANN in simulating the discharge. Therefore, ENN model is suggested as a better model for rainfall-runoff discharge in the Sone command, Bihar.

scholarly journals APPLICATION OF ARTIFICIAL NEURAL NETWORK IN THE PROCESS OF SELECTION OF ORGANIC COATINGS

2019 ◽  
Vol 9 (4) ◽  
pp. 18-21
Author(s):  
Artur Popko ◽  
Konrad Gauda
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Process ◽  
Selection Process ◽  
Organic Coatings ◽  
Coating System ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
System Selection ◽  
Selection Of

The structure of the artificial neural network (ANN) to support the selection of organic coatings was developed and verified, and its learning process was carried out. A simulation of the operation of the network was also carried out, which showed that programming of the coating system selection process can be much faster and more accurate, which is important for a system used in industrial conditions.

Development of Quran Reciter Identification System Using MFCC and Neural Network

2016 ◽  
Vol 1 (1) ◽  
pp. 168 ◽  
Author(s):  
Tayseer Mohammed Hasan Asda ◽  
Teddy Surya Gunawan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Identification System ◽  
Successful Match ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Mel Frequency Cepstral Coefficient

Currently, the Quran is recited by so many reciters with different ways and voices.  Some people like to listen to this reciter and others like to listen to other reciters. Sometimes we hear a very nice recitation of al-Quran and want to know who the reciter is. Therefore, this paper is about  the development of Quran reciter recognition and identification system based on Mel Frequency Cepstral Coefficient (MFCC) feature extraction and artificial neural network (ANN). From every speech, characteristics from the utterances will be extracted through neural network model. In this paper a database of five Quran reciters is created and used in training and testing. The feature vector will be fed into Neural Network back propagation learning algorithm for training and identification processes of different speakers. Consequently,  91.2%  of the successful match between targets and input occurred with certain number of hidden layers  which shows how efficient are Mel Frequency Cepstral Coefficient (MFCC) feature extraction  and artificial neural network (ANN) in identifying the reciter voice perfectly.

scholarly journals Identification of Venation Type Based on Venation Density using Digital Image Processing

2018 ◽  
Vol 12 (2) ◽  
pp. 87
Author(s):  
Agus Ambarwari ◽  
Yeni Herdiyeni ◽  
Irman Hermadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Leaf Venation ◽  
Leaf Vein ◽  
Vein Density ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Relationship Of ◽  
Leaf Vein Density

Leaf venation is one biometric feature of leaves that have an important role in growth processes of the plant, and to determine the relationship of the plant physiology and the environment in which plants grow. At every different environment, plants have different types of leaf venation. It can be seen from the level of the leaf vein density. In this study, the feature of leaf vein density was used to identify the leaves based on venation type. The venation density features obtained from segmentation, vein detection, and density feature extraction of leaf venation. Identification of the venation type was made using the artificial neural network (ANN). The results of this study indicate that the proposed method can classify the leaf correctly image based on the venation type. On the dataset with 324 samples, the accuracy of 82.71% was obtained. This shows that the leaf vein density features allow use as a plant identifier.Keywords: leaf vein density, vein detection, density feature extraction, artificial neural network

scholarly journals Screening of Tuberculosis Using Artificial Neural Network

2020 ◽  
Vol 3 (11) ◽  
pp. 145-149
Author(s):  
A. Anand Kumar ◽  
T. Mani ◽  
S. Gokulnath ◽  
S. K. Kabilesh ◽  
K. Dinakaran ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Bacterial Disease ◽  
Image Processing Techniques ◽  
Neural Organization ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Processing Techniques

Tuberculosis is an infectious bacterial disease that most commonly affects the lungs. This paper reviews, screening of tuberculosis in chest radiograph images using an artificial neural network (ANN). Implementing image processing techniques having segmentation, feature extraction from chest radiographs, at that point building up a fake neural organization for programmed characterization dependent on back proliferation calculation to group tuberculosis accurately. The performance was evaluated using SVM and ANN classifiers regarding exactness, review, and precision. The trial results Confirm the effectiveness of the proposed strategy that gives great Classification proficiency.

scholarly journals THE IMPLEMENTATION OF EXTRACTION FEATURE USING GLCM AND BACK-PROPAGATION ARTIFICIAL NEURAL NETWORK TO CLASIFY LOMBOK SONGKET WOVEN CLOTH

2020 ◽  
Vol 17 (2) ◽  
pp. 131-136
Author(s):  
Bahtiar Imran ◽  
Muhamad Masjun Efendi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Extraction Method ◽  
Back Propagation ◽  
Training Data ◽  
Feature Extraction Method ◽  
Good Classification ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The aimed of this study was to apply the feature extraction method of GLCM and Back-propagation Artificial Neural Network (ANN) to classify Lombok's typical Songket woven cloth by classifying based on the texture of the Songket woven cloth. Songket woven cloth in Lombok in terms of weaving and texture are vary from region to region. For example the songket woven cloth in Pringgasela Village, Sukarara Village and Sade Village has differences in texture and motifs. For this reason, this study focuses on classifying Lombok's typical Songket woven cloth by performing feature extraction on woven cloth using the GLCM method and the classification method uses Back-propagation Artificial Neural Network (ANN). For data collection, the data was taken directly from the Songket weaving centers in Pringgasela, Sade and Sukarara. In the classification stage the training data used were 64 data and 11 test data. Then the epoch used was 41 iterations with a time of 0:00:04, with neurons 80 and 100. The use of neurons 80 generated 18% which was successful in the classification. While using 100 neurons generated 100% successful which was can be classified. Based on the classification results obtained, the use of 100 neurons gained good classification results.

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