Experimental and numerical evaluation of multilayer sheet forming process parameters for light weight structures using innovative methodology

Incremental sheet forming process has developed the interest of researchers in the field of sheet metal forming due to high formability and capability to produce prototypes of new products at low cost and minimum lead time. Research work is going on in various front to enhance the process capabilities so that it can be explored for commercial production. In this article, progress and recent development in the field of incremental forming has been reviewed and presented for the benefit of practicing engineers and industry. The effect of various process parameters on the performance of the process have been summarized in this paper. Moreover, the issues which need attention are discussed towards the conclusion of this paper.

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Investigating the Effect of Process Parameters in Incremental Sheet Forming Process

Advances in Civil and Industrial Engineering - Data-Driven Optimization of Manufacturing Processes ◽

10.4018/978-1-7998-7206-1.ch003 ◽

2021 ◽

pp. 24-36

Author(s):

Manish Oraon ◽

Manish Kumar Roy ◽

Vinay Sharma

Keyword(s):

Surface Roughness ◽

Process Parameters ◽

Sheet Forming ◽

Incremental Sheet Forming ◽

Forming Process ◽

Batch Production ◽

Force Microscopy ◽

Atomic Force ◽

Roughness Analysis ◽

Step Down

Incremental sheet forming (ISF) is an emerging technique of sheet metal working that comes into the picture in the last two decades. The ISF involved the forming of shapes without using the dedicated dies. ISF is suitable for customized products, rapid prototyping, and low batch production. The study aims to investigate the effect of process parameters on the surface roughness. The experiments are conducted on aluminum AA3003-O grade with six parameters, and the trials are performed according to the design of experiment (DOE). The atomic force microscopy (AFM) technique is used for measuring the surface roughness. Analysis of variance (ANOVA) is used for analyzing the effect of process parameters in ISF. The result shows that the step-down size, feed rate of the tool, and wall angle are significant process parameter and their contributions for ISF are 85.86%, 1.12%, and 12.29%, respectively.

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Numerical Simulation on the Effect of Process Parameters for Incremental Sheet Forming

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.97-101.158 ◽

2010 ◽

Vol 97-101 ◽

pp. 158-161 ◽

Cited By ~ 1

Author(s):

Qin Qin ◽

Di Ping Wu ◽

Mi Li ◽

Yong Zang

Keyword(s):

Process Parameters ◽

Rapid Prototype ◽

Sheet Forming ◽

Incremental Sheet Forming ◽

Layered Manufacturing ◽

Forming Process ◽

Step Size ◽

Flexible Technology ◽

Vertical Step ◽

Forming Angle

Incremental sheet forming (ISF), based on the ‘layered manufacturing’ principle of rapid prototype manufacturing technology, is an innovative and highly flexible technology for forming complex shaped parts without the need for costly dies. This paper presents a numerical investigation on the influence of forming process parameters by modeling the forming process. ANSYS/LS-DYNA has been used for the simulation. The results of study show that small vertical step size can improve the accuracy of the forming. Moreover, large forming angle can increase plastic strain and the four screwdown point optimization paths is an effective method to increase the accuracy of the formed sheet.

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Investigation of the influence of incremental sheet forming process parameters using response surface methodology

Metallurgical Research & Technology ◽

10.1051/metal/2021039 ◽

2021 ◽

Vol 118 (4) ◽

pp. 401

Author(s):

Belouettar Karim ◽

Ould Ouali Mohand ◽

Zeroudi Nasereddine ◽

Thibaud Sébastien

Keyword(s):

Response Surface Methodology ◽

Response Surface ◽

Process Parameters ◽

Damage Evolution ◽

Sheet Forming ◽

Thickness Reduction ◽

Forming Process ◽

Influence Of Process Parameters ◽

Forming Angle ◽

Tool Diameter

New methods in metal forming are rapidly developing and several forming processes are used to optimize manufacturing components and to reduce cost production. Single Point Incremental Forming (SPIF) is a metal sheet forming process used for rapid prototyping applications and small batch production. This work is dedicated to the investigation of the profile geometry and thickness evolution of a truncated pyramid. The influence of process parameters during a SPIF process is also studied. A numerical response surface methodology with a Design of Experiments (DOE) is used to improve the thickness reduction and the effects of the springback. A set of 16 tests are performed by varying four parameters: tool diameter, forming angle, sheet thickness, and tool path. The Gurson-Tvergaard-Needleman (GTN) damage model is used to analyze the damage evolution during material deformation. It is found that the model can effectively predict the geometrical profile and thickness with an error of less than 4%. Furthermore, it is noticed that the forming angle is the most influential parameter on the thickness reduction and springback level. Finally, the damage evolution is demonstrated to be sensitive to the forming angle.

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Optimization in single point incremental forming of Inconel 718 through response surface methodology

Transactions of the Canadian Society for Mechanical Engineering ◽

10.1139/tcsme-2019-0003 ◽

2020 ◽

Vol 44 (1) ◽

pp. 148-160

Author(s):

S. Pratheesh Kumar ◽

S. Elangovan

Keyword(s):

Response Surface Methodology ◽

Response Surface ◽

Process Parameters ◽

Inconel 718 ◽

Single Point ◽

Optimization Technique ◽

Sheet Forming ◽

Incremental Sheet Forming ◽

Forming Process ◽

Profile Accuracy

Incremental sheet forming is a flexible and versatile process with a promising future in the batch production and prototyping sectors. With decreased design time and negligible production time, incremental sheet forming provides reliability, flexibility, and quality, while being an economical option in contrast to the traditional forming process. In this paper, Inconel 718, a material that has extensive use in aircraft engines, is considered for experimental work to obtain the optimum combination of process parameters. Response surface methodology is used to optimize the process parameters, in particular feed rate, step depth, and lubricant viscosity. The output responses are surface roughness, profile accuracy, and wall thickness. Analysis of variance (ANOVA) is performed using the experimental results to predict the statistical influence of the process parameters. The optimal combination of process parameters is further predicted using a numerical optimization technique to achieve better profile accuracy and surface finish. The results obtained are experimentally validated and are in good agreement with the predicted values.

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Effects of process parameters on force reduction and temperature variation during ultrasonic assisted incremental sheet forming process

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-018-1886-0 ◽

2018 ◽

Vol 97 (1-4) ◽

pp. 13-24 ◽

Cited By ~ 10

Author(s):

Yangyang Long ◽

Yanle Li ◽

Jie Sun ◽

Igor Ille ◽

Jianfeng Li ◽

...

Keyword(s):

Temperature Variation ◽

Process Parameters ◽

Sheet Forming ◽

Incremental Sheet Forming ◽

Forming Process ◽

Ultrasonic Assisted ◽

Force Reduction

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Taguchi Optimization of Process Parameters for Forming Time in Incremental Sheet Forming Process

Materials Science Forum ◽

10.4028/www.scientific.net/msf.773-774.137 ◽

2013 ◽

Vol 773-774 ◽

pp. 137-143 ◽

Cited By ~ 3

Author(s):

Zhao Bing Liu ◽

Yan Le Li ◽

W.J.T. Bill Daniel ◽

Paul Meehan

Keyword(s):

Process Parameters ◽

Feed Rate ◽

Tool Path ◽

Sheet Thickness ◽

Sheet Forming ◽

Analysis Tool ◽

Forming Process ◽

Rate Sheet ◽

Step Over ◽

Tool Diameter

ncremental sheet forming (ISF) is a new promising technology due to its flexibility and low-cost tooling properties compared with conventional forming processes. However, it is only suitable for small-batch production because of its incremental feature inducing relative long forming time. Presently, widespread usage of the process is restricted by a lack of predictive understanding of the process due to its complexity. In this paper, the aspect of forming time is studied by investigating the effects of four distinctive process parameters (step over, feed rate, sheet thickness and tool diameter). An effective analysis tool, Taguchi method together with design of experiment (DOE) and analysis of variance (ANVOA) is utilized to study the effects of the four process parameters on forming time and further to optimize parameter combinations in order to minimize forming time. Using these techniques, experimental results show that the step over of spiral tool path is the most important process parameter affecting forming time followed by feed rate. Sheet thickness and tool diameter have little effect on forming time. The comparison between the prediction of optimized parameter combination and the confirmation test result has further demonstrated the effectiveness of the proposed method. It is worth noting that the results of this study will indicate a further direction on how to optimize process parameters to find a balance between forming efficiency (forming time) and forming quality (forming accuracy and surface roughness).

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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

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-021-06712-4 ◽

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.

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