Prediction of the Mechanical Properties of Titanium Alloy Castings Based on a Back-Propagation Neural Network

Transformation point of titanium alloy is an important material parameter in hot forming and heat treatment for titanium alloy. Used the transformation points of TC6 titanium alloy determined by metallographic method as desired outputs, a model for forecasting the transformation point of TC6 titanium alloy was established using MATLAB neural network toolbox under the guidance of back propagation neural network. Used the established model to forecast the transformation point of TC6 titanium alloy and analyze the effects of the contents of nine elements on the transformation point of TC6 titanium alloy, and the results show that the transformation point of TC6 titanium alloy increases with enhancement of N, O, C and Al contents, while decreases with the enhancement of Cr, Si, Mo, Fe and H contents.

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Modeling of Correlation between Heat Treatment and Mechanical Properties of Ti–6Al–4V Alloy Using Feed Forward Back Propagation Neural Network

ISIJ International ◽

10.2355/isijinternational.50.1689 ◽

2010 ◽

Vol 50 (11) ◽

pp. 1689-1694 ◽

Cited By ~ 2

Author(s):

Junaidi Syarif ◽

Yan Pratama Detak ◽

Rizauddin Ramli

Keyword(s):

Neural Network ◽

Mechanical Properties ◽

Heat Treatment ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Feed Forward ◽

Feed Forward Back Propagation

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Effects of Servo Tensile Test Parameters on Mechanical Properties of Medium-Mn Steel

Materials ◽

10.3390/ma12223793 ◽

2019 ◽

Vol 12 (22) ◽

pp. 3793

Author(s):

Xuemin Chi ◽

Shuo Han

Keyword(s):

Neural Network ◽

Mechanical Properties ◽

Back Propagation ◽

High Strength ◽

Total Elongation ◽

Back Propagation Neural Network ◽

Analytical Models ◽

Medium Mn Steel ◽

Supplemental Experiment ◽

Mn Steel

As a new type of third-generation automotive steel with high strength and plasticity, medium-Mn steel (MMnS) has been widely used in automotive industries for its excellent properties. In recent years, servo stamping technology for high-strength metal forming is a hot topic due to its good performance in forming under complex processing conditions, and servo parameters determine the forming quality. In this paper, experiments considering tensile speed and position where speed changes (PSC) were carried out on MMnS to investigate the influences of tensile parameters on mechanical properties including strength and total elongation (TE). The results show that PSC does not significantly impact total elongation. Initial tensile speed (ITS) and final tensile speed (FTS) significantly impact the total elongation. The interaction between all tensile parameters can impact total elongation. Two artificial neural networks, back propagation neural network (BPNN) and radial basis function neural network (RBFNN), were used to establish analytical models. The results of supplemental experiment and residual analysis were conducted to verify the accuracy of the analytical models. The BPNN has a better performance and the analytical model shows that with the increase of PSC, it has a slight impact on the changes of optimal and minimum total elongation, but the combinations of tensile parameters to obtain total elongations higher than 40% change significantly.

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Application of a back-propagation neural network for mechanical properties prediction of ferromagnetic materials by magnetic Barkhausen noise technique

Insight - Non-Destructive Testing and Condition Monitoring ◽

10.1784/insi.2019.61.2.95 ◽

2019 ◽

Vol 61 (2) ◽

pp. 95-99 ◽

Cited By ~ 1

Author(s):

Yanyan Zhang ◽

Wenbo Liu ◽

Kaiyu Li ◽

Ping Wang ◽

Cheng Hang ◽

...

Keyword(s):

Neural Network ◽

Mechanical Properties ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Ferromagnetic Materials ◽

Barkhausen Noise ◽

Magnetic Barkhausen Noise ◽

Mechanical Properties Prediction

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Economic benefit of shale gas exploitation based on back propagation neural network

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189279 ◽

2020 ◽

Vol 39 (6) ◽

pp. 8823-8830

Author(s):

Jiafeng Li ◽

Hui Hu ◽

Xiang Li ◽

Qian Jin ◽

Tianhao Huang

Keyword(s):

Neural Network ◽

Shale Gas ◽

Bp Neural Network ◽

Large Scale ◽

Linear Prediction ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Economic Benefits ◽

Gas Well ◽

Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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An Effective Customer Churn Prediction Model using Adaptive Gain with Back Propagation Neural Network in Cloud Computing Environment

Journal of Research on the Lepidoptera ◽

10.36872/lepi/v51i1/301034 ◽

2020 ◽

Vol 51 (1) ◽

pp. 386-399

Author(s):

JEYAKARTHIC M

Keyword(s):

Neural Network ◽

Cloud Computing ◽

Prediction Model ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Churn Prediction ◽

Computing Environment ◽

Cloud Computing Environment ◽

Customer Churn ◽

Customer Churn Prediction

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An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666191122113801 ◽

2019 ◽

Vol 13 ◽

Author(s):

Shikha Bhardwaj ◽

Gitanjali Pandove ◽

Pawan Kumar Dahiya

Keyword(s):

Neural Network ◽

Deep Learning ◽

Image Retrieval ◽

Hybrid System ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Retrieval Accuracy ◽

Color Descriptor ◽

Benchmark Datasets ◽

Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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