scholarly journals Design of Multi-Stage Roll Die Forming Process for Drum Clutch with Artificial Neural Network

Materials ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 69
Author(s):  
Jae-Hong Kim ◽  
Jae-Chang Ryu ◽  
Woo-Sik Jang ◽  
Joon-Hong Park ◽  
Young-Hoon Moon ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fe Simulation ◽  
Dimensional Accuracy ◽  
Outer Diameter ◽  
Forming Process ◽  
Multi Stage ◽  
Die Set ◽  
Artificial Neural ◽  
Die Forming

The multi-stage roll die forming (RDF) process is a plastic forming process that can manufacture a transmission part with a complex shape, such as a drum clutch, by using a die set with rotational rolls. However, it is difficult to satisfy dimensional accuracy because of spring-back and unfilling. The purpose of this study is to design a multi-stage RDF process for the manufacturing of a drum clutch to improve dimensional accuracy using an artificial neural network (ANN). Finite element (FE) simulation of the multi-stage RDF process is performed to predict the dimensional accuracy according to various clearances for each stage. Moreover, the ANN is used to determine the relationship between the clearance and dimensional accuracy of the drum clutch to reduce the number of FE simulation. The results of the FE simulation and ANN are used to determine the optimal clearance for each stage of the RDF process. Finally, the drum clutch is fabricated using the determined conditions. The experimental results are in good agreement with the results of FE simulation from the aspect of outer diameter, inner diameter, thickness of outer tooth, thickness of inner tooth, and face thickness of tooth.

scholarly journals Artificial neural network for modeling and investigating the effects of forming tool characteristics on the accuracy and formability of thin aluminum alloy blanks when using SPIF

2021 ◽  
Author(s):  
Sherwan Mohammed Najm ◽  
Imre Paniti
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Single Point ◽  
Tool Material ◽  
Deformation Mode ◽  
Geometric Accuracy ◽  
Forming Process ◽  
Tool Shape ◽  
Artificial Neural

AbstractIncremental Sheet Forming (ISF) has attracted attention due to its flexibility as far as its forming process and complexity in the deformation mode are concerned. Single Point Incremental Forming (SPIF) is one of the major types of ISF, which also constitutes the simplest type of ISF. If sufficient quality and accuracy without defects are desired, for the production of an ISF component, optimal parameters of the ISF process should be selected. In order to do that, an initial prediction of formability and geometric accuracy helps researchers select proper parameters when forming components using SPIF. In this process, selected parameters are tool materials and shapes. As evidenced by earlier studies, multiple forming tests with different process parameters have been conducted to experimentally explore such parameters when using SPIF. With regard to the range of these parameters, in the scope of this study, the influence of tool material, tool shape, tool-end corner radius, and tool surface roughness (Ra/Rz) were investigated experimentally on SPIF components: the studied factors include the formability and geometric accuracy of formed parts. In order to produce a well-established study, an appropriate modeling tool was needed. To this end, with the help of adopting the data collected from 108 components formed with the help of SPIF, Artificial Neural Network (ANN) was used to explore and determine proper materials and the geometry of forming tools: thus, ANN was applied to predict the formability and geometric accuracy as output. Process parameters were used as input data for the created ANN relying on actual values obtained from experimental components. In addition, an analytical equation was generated for each output based on the extracted weight and bias of the best network prediction. Compared to the experimental approach, analytical equations enable the researcher to estimate parameter values within a relatively short time and in a practicable way. Also, an estimate of Relative Importance (RI) of SPIF parameters (generated with the help of the partitioning weight method) concerning the expected output is also presented in the study. One of the key findings is that tool characteristics play an essential role in all predictions and fundamentally impact the final products.

Burst Pressure Prediction of Cylindrical Vessels Using Artificial Neural Network

2020 ◽  
Vol 142 (3) ◽  
Author(s):  
Abolfazl Zolfaghari ◽  
Moein Izadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Pressure Vessel ◽  
Pressure Vessels ◽  
Machine Learning Techniques ◽  
Burst Pressure ◽  
Outer Diameter ◽  
Wide Range ◽  
Artificial Neural ◽  
Predictor Model

Abstract Pressure vessel plays an important role in wide range of applications to store gas or liquid substances. In order to design a pressure vessel safely, one of the main factors which has to be considered is selection of proper burst pressure perdition criterion. Due to large range of available materials in manufacturing of the vessels under different working conditions, several criteria to forecast burst pressure of the vessels have been developed and used by designers. Choosing the most proper criterion based on working condition and the material is a vital task to meet design requirements because inappropriate criterion may lead to unsafe vessel or over design. This issue makes not only pressure vessel design more complex but also maintenance planning, especially for designers who do not have enough experience, is a challenging task. Therefore, lack of a burst pressure predictor model, which is able to determine the pressure more accurately for wide range of materials and applications, has been remained unsolved. To evaluate machine learning techniques in prediction of burst pressure of pressure vessels, in this paper, a new model based on artificial neural network (ANN) has been proposed and developed. Input parameters of the model include internal and outer diameter, thickness, ultimate and yield strength; output is burst pressure. The obtained results showed that the constructed model has a good potential to be used as more applicable model compared to current models in design of pressure vessels.

scholarly journals A multi-stage methodology for selecting input variables in ANN forecasting of river flows

2017 ◽  
Vol 19 (1) ◽  
pp. 49-57 ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Atmospheric Circulation ◽  
River Flow ◽  
Neural Network Models ◽  
Prediction Ability ◽  
River Flows ◽  
Multi Stage ◽  
Artificial Neural ◽  
Input Variables

<p>The scientific community has recognized the necessity for more efficiently selected inputs in artificial neural network models (ANNs) in river flows and has worked on this despite some shortcomings. Moreover, there is none or limited inclusion of ANN inputs coupled with atmospheric circulation under various patterns arising from the need of data downscaling for climate change predictions in hydrology domain. This paper presents the results of a novel multi-stage methodology for selecting input variables used in artificial neural network (ANN) models for river flow forecasting. The proposed methodology makes use of data correlations together with a set of crucial statistical indices for optimizing model performance, both in terms of ANN structure (e.g. neurons, momentum rate, learning rate, activation functions, etc), but also in terms of inputs selection. The latter include various previous time steps of daily areal precipitation and temperature data coupled with atmospheric circulation in the form of circulation patterns, observed river flow data and time expressed via functions of sine and cosine. Additionally, the no-linear behavior between river flow and the respective inputs is investigated by the ANN configuration itself and not only by correlation indices (or other equivalent contingency tools). The proposed methodology revealed the river flow of past four days, the precipitation of past three days and the seasonality as robust input variables. However, the temperature of three past days should be considered as an alternative against the seasonality. The produced models forecasting ability was validated by comparing its one-step ahead flow prediction ability to two other approaches (an auto regressive model and a genetic algorithm (GA)-optimized single input ANN).&nbsp;</p>

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