scholarly journals Experimental estimation and numerical optimization of ‘cylindricity’ error in flow forming of H30 aluminium alloy tubes

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Tarak Nath De ◽  
Bikramjit Podder ◽  
Nirmal Baran Hui ◽  
Chandan Mondal
Keyword(s):  
Reverse Flow ◽  
Empirical Relation ◽  
Speed Ratio ◽  
Al Alloy ◽  
Feed Speed ◽  
Forming Process ◽  
Flow Forming ◽  
Input Parameters ◽  
Experimental Trials ◽  
Cylindricity Error

AbstractThe present article analyses the influence of flow forming input parameters on the development of “cylindricity error” in H30 aluminum alloy seamless tubes fabricated by a single pass reverse flow forming process. Measurement and control of geometrical precision in terms of cylindricity encompassing straightness and roundness are critical for the success of component manufacturing by flow forming. The experimental trials with a predefined range of input parameters conforming to the full factorial design of experiments approach have been performed, and corresponding cylindricity data have been recorded as the outcome. An empirical relation has been established between the input parameters and the cylindricity. It has been established that cylindricity value increases sharply with an increase in axial stagger contributing 39% to the outcome, whereas the percentage contributions of in-feed and feed-speed ratio are found to be less than 1%. The adequacy of the proposed model has further been analyzed and validated through the confirmation tests. In order to obtain better control over the overall process towards achieving higher productivity and accuracy, 2 meta-heuristic optimization algorithms namely, teaching and learning-based algorithm and genetic algorithm have been utilized for optimization of input process parameters to minimize cylindricity error. Both the algorithms predict that a combination of higher feed rate and lower value of axial stagger and in-feed parameters is essential to achieve the lowest cylindricity error in H30 Al alloy. Confirmatory experimental trials have been carried out to validate both the regression model and optimization, and have been found to agree well with the model predictions described herein.

Development of ANFIS Model for Flow Forming of Solution Annealed H30 Aluminium Tubes

2017 ◽  
Vol 261 ◽  
pp. 378-385
Author(s):  
Bikramjit Podder ◽  
Prabas Banerjee ◽  
K. Ramesh Kumar ◽  
Nirmal Baran Hui
Keyword(s):  
Fuzzy Inference ◽  
Speed Ratio ◽  
Internal Diameter ◽  
Feed Speed ◽  
Forming Process ◽  
Flow Forming ◽  
Inference System ◽  
Cold Flow ◽  
Additional Process ◽  
Very High

Modeling of the cold flow forming process for manufacturing of tube shaped solution annealed H30 Aluminum alloy has been considered in this present study. Three inputs (feed-speed ratio, roller in-feed and roller axial stagger) and three outputs viz. spring back, ovality and internal diameter have been considered for the present study.. Adaptive network-based fuzzy inference system (ANFIS) in Matlab platform has used for modeling purposes and its performance is compared with regression model. ANFIS has completely outperformed the regression models. Percentage accuracy in predicting all the three responses are found to be very high with ANFIS models. Prediction of ovality against the test data using regression analysis is found to be extremely erroneous. It indicates that additional process parameters are involved in predicting ovality which are not captured during the experimentation.

A Study of Strain and Strain-Rate Dependent Properties of a Superplastic Zn-Al Alloy Under Biaxial Stresses

1982 ◽  
Vol 104 (1) ◽  
pp. 41-46
Author(s):  
T. C. Hsu ◽  
I. M. Bidhendi
Keyword(s):  
Strain Rate ◽  
Stress Ratio ◽  
Local Stress ◽  
Biaxial Stress ◽  
Al Alloy ◽  
Local Geometry ◽  
Forming Process ◽  
Rate Dependent ◽  
Strain And Strain Rate ◽  
Liquid Behavior

A superplastic Zn-Al alloy in sheet form is formed into a bulge over a circular hole by pneumatic pressure. The geometry, the stress, the strain, and the strain-rate are determined at various points covering the whole specimen and at various stages of the forming process. The complicated shape, and its complicated changes, are represented by introducing an index for the local geometry, called “prolateness,” which is also related to the local stress ratio in a simple way. The biaxial stress is analyzed into a strain-proportional and a strain-rate-proportional component, which represent, respectively, the quasi-solid and the quasi-liquid behavior of the superplastic material.

Numerical Study of the Flow Forming Process of AISI 630 Stainless Steel

2011 ◽  
Vol 264-265 ◽  
pp. 24-29 ◽  
Author(s):  
Seyed Mohammad Ebrahimi ◽  
Seyed Ali Asghar Akbari Mousavi ◽  
Mostafa Soltan Bayazidi ◽  
Mohammad Mastoori
Keyword(s):  
Feeding Rate ◽  
Surface Defects ◽  
Numerical Study ◽  
Internal Diameter ◽  
The Finite Element Method ◽  
Forming Process ◽  
Flow Forming ◽  
Cold Forming ◽  
External Variables ◽  
Experimental Process

Flow forming is one of the cold forming process which is used for hollow symmetrical shapes. In this paper, the forward flow forming process is simulated using the finite element method and its results are compared with the experimental process. The variation of thickness of the sample is examined by the ultrasonic tests for the five locations of the tubes. To simulate the process, the ABAQUS explicit is used. The effects of flow forming variables such as the angle of rollers and rate of feeding of rollers, on the external variables such as internal diameter, thickness of tube and roller forces are considered. The study showed that the roller force and surface defects were reduced with low feeding rate and low rollers attack angles. Moreover, the sample internal diameter increased at low feeding rate and low rollers attack angles. The optimum variables for flow forming process were also obtained.

Production and Properties of a 90%Si-Al Alloy for Electronic Packaging Applications

2009 ◽  
Vol 610-613 ◽  
pp. 542-545 ◽  
Author(s):  
Kun Yu ◽  
Chao Li ◽  
Jun Yang ◽  
Zhi Yong Cai
Keyword(s):  
Physical Properties ◽  
Electronic Packaging ◽  
Heat Dissipation ◽  
Thermal Conductivity Coefficient ◽  
Coefficient Of Thermal Expansion ◽  
Spray Forming ◽  
Al Alloy ◽  
Scanning Electronic Microscopy ◽  
Alloy Specimen ◽  
Forming Process

The high silicon content Si-Al alloy is a typical heat dissipation material that used in the electrical packaging field. A spray forming process wais used to produce a 90%Si-Al alloy specimen as a heat dissipation material in the present study. Then the spray formed 90%Si-Al specimens weare hot pressed at 1000°C with different pressure ranged from 2MPa to 10MPa to increase their density. The physical properties of the experimental alloy specimen weare measured. And the microstructure wass are observed by using optical microscopy and scanning electronic microscopy. The results showed that the Spray forming wais suitable forto producinge a 90%Si-Al alloy. With hot pressure of 10MPa, the relative density value of 90%Si-Al reachedcan obtain 94%. The typical physical properties such as the thermal conductivity, coefficient of thermal expansion and electrical conductivity of 90%Si-Al alloy are acceptable as a heat dissipation material for many electronic packaging applications.

The Influnce of Some Parameters on the Quality of Deep Drawing of Cylindrical Cups without a Blank Holder

2015 ◽  
Vol 809-810 ◽  
pp. 259-264
Author(s):  
Dan Chiorescu ◽  
Gheorghe Nagîț ◽  
Oana Dodun
Keyword(s):  
Sheet Metal ◽  
Deep Drawing ◽  
Signal To Noise Ratio ◽  
Fractional Factorial ◽  
Major Influence ◽  
Forming Process ◽  
Sheet Metal Parts ◽  
Product Surface ◽  
Experimental Trials

Deep drawing is one of the most important processes for forming sheet metal parts. Besides its importance as a forming process, cup drawing also serves as a basic test for the sheet metal formability. This article investigates the influence of the die punch clearance, the average velocity in the active stage and the lubrication on the deep drawing quality expressed by the thickness evenness on the finished product surface. In order to minimize the number of experimental trials, a fractional factorial design was developed together with an orthogonal array, thus analyzing the contribution of the three parameters under study to the quality of the deep drawing process. Using TAGUCHI’s signal-to-noise ratio, we determine that ram velocity has a major influence, followed by the clearance between the active elements, while the contribution of lubrication is negligible. The results of the research are useful in developing a sensible design of experiments.

Research on Synchronized Cooling Hot Forming Process of 6016 Aluminum Alloy

2012 ◽  
Vol 452-453 ◽  
pp. 81-85 ◽  
Author(s):  
Ming He Chen ◽  
Y.Y. Cao ◽  
W. Chen ◽  
Guo Liang Chen
Keyword(s):  
Aluminum Alloy ◽  
Process Parameters ◽  
High Strength ◽  
Strengthening Mechanism ◽  
Alloy Sheet ◽  
Hot Forming ◽  
Al Alloy ◽  
Forming Process ◽  
Process Step ◽  
6016 Aluminum Alloy

In order to improve formability of high strength Al-alloy sheet metal, in this paper, it come up with the synchronized cooling hot forming process. Taking the aluminum alloy of 6016 H18 aluminum alloy, which carried out its technology test by Gleeble3500 hot-mechanical simulator. The process parameters such as deformation temperature T, holding time t and cooling rate v is investigated by the orthogonal test and the microstructure is analyzed simultaneously. The results show that the synchronized cooling hot forming process can be applied to 6016 H18 aluminum alloy, it both improves the formability of 6016 H18 aluminum alloy significantly and obtains the high strength after forming, it can meet the purpose of implementing deformation and enhanced in one process step, the proper combination of process parameters are T=450 °C, t=210 s, v=60 °C/s. Strengthening mechanism is which there is a large number of strengthening phase precipitated from matrix in technology process, the strengthening phases are coarser and the dispersed uniformity is a bit worse compared with that of T4 state.

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