Prediction of forming temperature in electrically-assisted double-sided incremental forming using a neural network

2021 ◽  
pp. 117486
Author(s):  
Zilin Jiang ◽  
Kornel F. Ehmann ◽  
Jian Cao
Keyword(s):  
Neural Network ◽  
Incremental Forming ◽  
Electrically Assisted ◽  
Forming Temperature

Effect of process parameters on formability in two-point incremental forming-machining of planar and twisted AA5083 blades

2022 ◽  
pp. 095440542110697
Author(s):  
Hossein Ghorbani-Menghari ◽  
Mehrdad Azadipour ◽  
Mehran Ghasempour-Mouziraji ◽  
Young Hoon Moon ◽  
Ji Hoon Kim
Keyword(s):  
Tool Path ◽  
Incremental Forming ◽  
Dimensional Accuracy ◽  
Numerical Control ◽  
Machining Process ◽  
Curved Surface ◽  
Forming Process ◽  
Forming Temperature ◽  
Feature Based ◽  
Tool Diameter

The deformation machining process (DMP) involves machining and incremental forming of thin structures. It can be applied for manufacturing products such as curved-surface blades without using 5-axis computerised numerical control machines. This work presents the effect of tool diameter and forming temperature on spring-back and dimensional accuracy of a simple fabricated part. The results of the first phase of the study are utilised to design the fabrication process of a curved surface blade. A feature-based algorithm is used to design the tool path for the forming process. The dimensional accuracy of the final product is improved through warm forming, two-point incremental forming, and extension of the bending zone to the outside of the product edges. The results show that DMP can be used to fabricate complex curved-surface workpieces with acceptable dimensional accuracy.

Improving the Accuracy of Parts Manufactured by Single Point Incremental Forming

2013 ◽  
Vol 332 ◽  
pp. 443-448 ◽  
Author(s):  
Crina Radu ◽  
Ion Cristea ◽  
Eugen Herghelegiu ◽  
Stefan Tabacu
Keyword(s):  
Neural Network ◽  
Stainless Steel ◽  
Incremental Forming ◽  
Single Point ◽  
Optimization Methods ◽  
Single Point Incremental Forming ◽  
Surface Method ◽  
Good Ability ◽  
The Neural Network ◽  
Network Method

The aim of this paper is to enrich the knowledge related to the single point incremental forming (SPIF) process by evaluating the efficiency of two optimization methods - the response surface method and the neural network method - to improve the accuracy of manufactured parts by prescribing a proper combination of the process parameters. The analysis is performed for a double frustum of pyramid made by stainless steel. It was found a good ability of prediction of both methods, demonstrating their suitability for physical implementation in solving problems associated to the SPIF process.

The Effects of Polarity and Current Path in Electrically Assisted Single Point Incremental Forming of 2024-T3 Aluminum

2017 ◽  
Author(s):  
Tyler J. Grimm ◽  
Ihab Ragai ◽  
John T. Roth
Keyword(s):  
Incremental Forming ◽  
Single Point ◽  
Negative Electrode ◽  
Electrical Current ◽  
Single Point Incremental Forming ◽  
Direct Electrical Current ◽  
Electrically Assisted ◽  
Current Path ◽  
Two Parameters ◽  
Springback Reduction

Electrically assisted incremental sheet forming (EAIF) is a novel addition to the incremental forming (IF) method. One variation of this approach applies direct electrical current during forming. Many improvements over tradition IF can be seen by utilizing this method, to include greater part accuracy, reduced forming force, and greater formability. In order to maximize the effects of electrically assisted incremental forming, all parameters of the method must be investigated, including the polarity of the current passing through the part and the path that the applied current takes. The effects of altering these two parameters is the primary investigation in this research. It was determined that, in order to optimize springback reduction and formability during electrically assisted single point incremental forming, the tool should be assigned the positive electrode and the center of the workpiece should be assigned the negative electrode. Additionally, the mechanism behind the spalling effect inherent to EAIF is discussed.

scholarly journals Numerical Simulation and Experiment of Electrically-Assisted Incremental Forming of Thin TC4 Titanium Alloy Sheet

Materials ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1335
Author(s):  
Bo Jiang ◽  
Wenbing Yang ◽  
Ziyang Zhang ◽  
Xifeng Li ◽  
Xueping Ren ◽  
...  
Keyword(s):  
Titanium Alloy ◽  
Incremental Forming ◽  
Simulation Method ◽  
Alloy Sheet ◽  
Cycle Period ◽  
Forming Process ◽  
Mechanical Coupling ◽  
Tc4 Titanium Alloy ◽  
Electrically Assisted ◽  
Alloy Part

In order to integrally manufacture the large TC4 titanium alloy part, an electrically-assisted incremental forming process is cleverly proposed to solve the traditional hot forming disadvantages of expensive heating furnaces and long cycle period. The two-step simulation method including thermal-electricity coupling simulation and thermo-mechanical coupling simulation was selected to predict the temperature variations and the sheet deformation behaviors. The electrically-assisted incremental forming experiment of thin TC4 titanium alloy sheet was performed. The highest prediction error is 6% for springback angles. The thrice forming at 10.9 A/mm2 satisfies the precision requirement of the designed part. Therefore, the two-step simulation method can effectively calculate the electrically-assisted incremental process. The electrically-assisted incremental forming technique is very promising for the integral producing large titanium alloy part.

scholarly journals A Model Developed for Predicting Uniformity of Kyphoplasty Balloon Wall Thickness Based on the Orthogonal Test

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Guanghui Dai ◽  
Qingqing Zhang ◽  
Guobao Jin
Keyword(s):  
Neural Network ◽  
Wall Thickness ◽  
Thickness Distribution ◽  
Orthogonal Test ◽  
Ann Model ◽  
Molding Process ◽  
Wall Thickness Distribution ◽  
Blow Molding ◽  
Forming Temperature ◽  
Stretch Blow Molding

In order to optimize the wall thickness distribution of medical balloon, kyphoplasty balloon was chosen as the research object, the uniformity of wall thickness distribution was taken as the evaluation index, and the influence of stretch blow molding process on the uniformity of kyphoplasty balloon was investigated. In this paper, 16 sets of orthogonal test schemes were studied by selecting four main parameters such as forming temperature, forming pressure, stretching distance, and holding time of stretch blow molding process based on the L16(44) Taguchi method orthogonal table. The statistical analysis showed that the forming temperature was an utmost parameter on the uniformity, while an optimal scheme was obtained and an optimal balloon with the uniformity of 95.86% was formed under the scheme. To further quantify the relationship between the uniformity and the parameters, artificial neural network (ANN) and nonlinear regression (NLR) models were developed to predict the uniformity of the balloon based on orthogonal test results. A feed-forward neural network based on backpropagation (BP) was made up of 4 input neurons, 11 hidden neurons, and one output neuron, an objective function of the NLR model was developed using second-order polynomial, and the BFGS method was used to solve the function. Adequacy of models was tested using hypothesis tests, and their performances were evaluated using the R2 value. Results show that both predictive models can be used for predicting the uniformity of the balloon with a higher reliability. However, the NLR model showed a slightly better performance than the ANN model.

A Numerical Study on Rotational Tube Flaring Process

2018 ◽  
Author(s):  
Chetan P. Nikhare
Keyword(s):  
Incremental Forming ◽  
Numerical Study ◽  
Advanced Manufacturing ◽  
Metal Part ◽  
Fuel Prices ◽  
Electrically Assisted ◽  
Tube Flaring ◽  
Conical Tool ◽  
Electrically Assisted Manufacturing ◽  
Explosive Forming

A substantial increase in demand on the sheet metal part usage in aerospace and automotive industries is due to the increase in the sale of these products to ease the transportation. However, due to increase in the fuel prices and further environmental regulation had left no choice but to manufacture more fuel efficient and inexpensive vehicles. These heavy demands force researchers to think outside the box. There are many advanced manufacturing processes to produce optimized part are single and double point incremental forming, Reuleaux forming, hydroforming, explosive forming, electrically assisted manufacturing. In this project, a numerical study on rotational tube flaring process will be studied. Tube flaring is one of the most commonly used processes under tube forming. In this process, a conical tool contacts and forces the end of the tube while another end of the tube is fixed. This is called conventional flaring process. In contrast to this process, a tool rotational technique was utilized for this work. The rotation and the feed of the tool will be analyzed to have the best formability of the tool. The strain path and failure will be analyzed. The strain and thinning pattern will be discussed.

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