Name: knowledge based cloud fe simulation of sheet metal forming processes
Uploaded: 2016-12-13T12:30:00
Duration: 11 min 5 s
Description: Watch this Scientific Journal Video about Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes at JoVE.com

The financial support from Innovate UK, Ultra-light Car Bodies (UlCab, reference 101568) and Make it lighter, with less (LightBlank, reference 131818) are gratefully acknowledged. The research leading to these results has received funding from the European Union's Seventh Framework Program (FP7/2007-2013) under grant agreement No. 604240, project title 'An industrial system enabling the use of a patented, lab-proven materials processing technology for Low Cost forming of Lightweight structures for transportation industries (LoCoLite)'. Significant support was also received from the AVIC Centre for Structural Design and Manufacture at Imperial College London, which is funded by Aviation Industry Corporation of China (AVIC).

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The authors have nothing to disclose.

The KBC-FE simulation technique enables advanced simulations to be conducted off site using dedicated modules. It can run functional modules on a cloud environment, that link up nodes from different specializations, to ensure that process simulations are conducted as accurately as possible. The critical aspects in the KBC-FE simulation may involve independency of the FE codes, efficiency of the computation, and accuracy of the functional modules. The realization of each advanced function in a module would rely on the development of a new model and/or a novel experimental technique. For example, the forming limit module is developed based on the new unified forming limit prediction model 11, and the friction tool life prediction module has currently been developed by the implementation of the interactive friction model 20. The KBC-FE simulation technique also offers the function of selective computation, i.e., only the elements fulfilling the selection criteria are selected for further evaluation in the individual modules. For instance, the tool life prediction module automatically selects the elements for which the hard coating tends to breakdown, by ranking the wear rate of all the elements in the 1st forming cycle, thus usually less than 1% of the elements will be selected for further tool life evaluations under multi-cycle loading conditions. In the present research, the tool life prediction after 300 forming cycles can be completed within 5 min.
By conducting the relevant tests and calibrating accordingly, the forming limit model could be applied to forming process simulations to consequently determine the optimal parameters for producing a component from such alloys successfully, and with no incidences of necking. The forming limit prediction model was developed as a cloud module that was independent of the FE software being utilized, and could be applied to any FE software to assess the formability of a material during forming, without complicated subroutines 17. By importing the relevant data into the model, calculations could be carried out to determine whether failure would occur, in regions of the component that the user could specify, saving on computational resources. However, it should be noted that as the stress-strain curves are input into the FE software through a simple look-up table, it may be difficult to fully represent the material properties at various temperatures and strain rates during simulation.&#160;
In the tool life prediction module, the frictional behavior during forming can be predicted by importing the required deformation history data into the verified friction module 20, and then importing the discrete data points calculated by the cloud module for each element back into the FE software. This ensures that the advanced friction module can be used by all FE codes, regardless of their ability to incorporate user-subroutines. Additionally, the module could be run in parallel to further reduce the computation time. The interactive friction/wear model assumed the absence of wear particles during initial sliding, and as a result, it would be reasonable to expect a constant initial value of friction coefficient 0.17 20. Although this model revealed the evolution of friction distribution, the frictional behavior during a forming process is very complicated, and it is difficult to completely integrate the complex frictional behavior from the cloud module into the FE simulation.
As a future technology, the KBC-FE simulation will rely on the development of dedicated and robust internet based FE simulation software packages, which would require a highly profitable, but completely different business model to be established by the software developers. In addition, a dedicated internal network needs to be built within the collaborative parties to ensure data security and the control reliability of the industrial system.

Finite Element (FE) simulations have become a powerful tool for optimizing process parameters in the metal forming industry. The reliability of FE simulation results is dependent on the accuracy of the material definition, input in the form of flow stress data or constitutive equations, and the assignment of the boundary conditions, such as the friction coefficient and the heat transfer coefficient. In the past few years, advanced FE simulations have been developed via the implementation of user-defined subroutines, which have significantly broadened the capability of FE software.
The use of such advanced FE simulations in the design of forming processes for structural components has been investigated by both the aviation and automotive industries, with the intention of producing lightweight structures that reduces operating costs and CO2 emissions. Particular focus has been placed on the replacement of steel components with lower density materials, such as aluminum alloys and magnesium alloys. However, these alloys, especially the stronger variants, offer limited formability at room temperature and thus complex-shaped components cannot be manufactured using the conventional cold stamping process. Therefore, advanced high temperature forming technologies, such as warm aluminum forming 1-4, hot stamping of aluminum alloys 5-9 and hot stamping of high strength steels 10, have been developed over the past decades to enable complex-shaped components to be formed. In general, high temperature forming processes involve significant temperature variations, strain rate and loading path changes 11, which would, for instance, cause inevitable viscoplastic and loading history dependent responses from the work piece materials. These are intrinsic features of high temperature forming processes and may be difficult to represent using conventional FE simulation techniques. Another desirable feature would be the ability to predict the tool life over multiple forming cycles in such processes, since they require low friction characteristics achieved through coatings that degrade with each forming operation. To represent all these features via the implementation of user-defined subroutines would be computationally very expensive. Moreover, the development and implementation of multiple subroutines would require excessive multi-disciplinary knowledge from an engineer conducting the simulations.
In the present work, a novel Knowledge Based Cloud FE (KBC-FE) simulation technique is proposed, based on the application of modules on a cloud computing environment, that enables an efficient and effective method of modeling advanced forming features in conjunction with conventional FE simulations. In this technique, data from the FE software is processed at each cloud module, and then imported back into the FE software in the relevant consistent format, for further processing and analysis. The development of these modules and their implementation in the KBC-FE is detailed.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>AA6082-T6</td>
			<td>AMAG</td>
			<td></td>
			<td>Material</td>
		</tr>
		<tr>
			<td>AA5754-H111</td>
			<td>AMAG</td>
			<td></td>
			<td>Material</td>
		</tr>
		<tr>
			<td>&#160;1000 kN high-speed press</td>
			<td>ESH</td>
			<td></td>
			<td>Forming press</td>
		</tr>
		<tr>
			<td>ARGUS</td>
			<td>GOM</td>
			<td></td>
			<td>Optical forming analysis</td>
		</tr>
		<tr>
			<td>PAM-STAMP 2015</td>
			<td>ESI</td>
			<td></td>
			<td>FE simulation software</td>
		</tr>
		<tr>
			<td>Matlab&#160;</td>
			<td>MathWorks</td>
			<td></td>
			<td>Numerical calculation software</td>
		</tr>
		<tr>
			<td>Gleeble 3800</td>
			<td>DSI</td>
			<td></td>
			<td>Uniaxial tensile test</td>
		</tr>
		<tr>
			<td>High Temperature Tribometer (THT)</td>
			<td>Anton Paar</td>
			<td></td>
			<td>Friction property test</td>
		</tr>
		<tr>
			<td>NewViewTM 7100</td>
			<td>ZYGO</td>
			<td></td>
			<td>Surface profilometer</td>
		</tr>
		<tr>
			<td>&#160;Magnetron sputtering equipment</td>
			<td></td>
			<td></td>
			<td>Coating deposition</td>
		</tr>
		<tr>
			<td>Microhardness tester</td>
			<td>Wolpert Wilson Instruments</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Nano-hardness indenter&#160;</td>
			<td>MTS</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

knowledge based cloud fe simulation of sheet metal forming processes

The use of Finite Element (FE) simulation software to adequately predict the outcome of sheet metal forming processes is crucial to enhancing the efficiency and lowering the development time of such processes, whilst reducing costs involved in trial-and-error prototyping. Recent focus on the substitution of steel components with aluminum alloy alternatives in the automotive and aerospace sectors has increased the need to simulate the forming behavior of such alloys for ever more complex component geometries. However these alloys, and in particular their high strength variants, exhibit limited formability at room temperature, and high temperature manufacturing technologies have been developed to form them. Consequently, advanced constitutive models are required to reflect the associated temperature and strain rate effects. Simulating such behavior is computationally very expensive using conventional FE simulation techniques.
This paper presents a novel Knowledge Based Cloud FE (KBC-FE) simulation technique that combines advanced material and friction models with conventional FE simulations in an efficient manner thus enhancing the capability of commercial simulation software packages. The application of these methods is demonstrated through two example case studies, namely: the prediction of a material's forming limit under hot stamping conditions, and the tool life prediction under multi-cycle loading conditions.

KBC-FE Simulation for Necking Prediction
In a hot stamping process, the use of a shape-optimized blank will not only save material cost but also help to reduce the presence of defects, such as necking, cracking, and wrinkling. The initial blank shape affects the material flow significantly during forming, and hence a sensible design of the blank shape is critical to the success of the hot stamping process and quality of the final products. To reduce the efforts of trial-and-error experiments to determine the optimal blank geometry, KBC-FE simulation was proven to be a highly efficient and effective method for minimizing the areas with necking. Using this technique, each simulation takes approximately 2 hours, while the parallel cloud module computation for necking prediction is completed within 4 hours.
Figure 4 shows the evolution of the blank shape used in the hot stamping, an example of automotive door inner component. The initial blank shape, adopted from a conventional cold stamping process, was first used in the KBC-FE simulation. Experimental results in Figure 4(a) show that large failure (cracking or necking) areas are visible after the hot stamping. After one iteration of the blank shape optimization, it can be seen in Figure 4(b) that an almost fully successful panel is formed with much less necking, compared to using the initial blank shape. It can be seen that there is still an indication of necking at the pockets in the top right and left corners of the panel. After further optimization in Figure 4(c), the optimized blank shape was finally obtained with no visible necking on the panel. The optimized blank shape determined by the KBC-FE simulation was verified experimentally through hot stamping trials conducted on a fully automated production line offered by a production system manufacturer.
KBC-FE Simulation for Tool Life Prediction
Conventional FE simulations of metal forming processes are performed for a single cycle. However, in a production environment, multiple forming cycles are performed on a given tool, where it is found that an increase in the number of forming cycles results in an increased variation between the formed components. This variation during multi-cycle tool loading is the result of changing surface topography. For example, the multi-cycle loading of forming tools with functional coatings will lead to a coating thickness reduction due to wear. Moreover, the breakdown of the coating will also be influenced by forming parameters, such as the load/pressure, forming speeds, etc. The KBC-FE technique enables the simulation of sheet metal forming processes under multi-cycle loading conditions, which is essential for the in-service life prediction of forming tools with advanced functional coatings.
To investigate the effects of blank holding force on the tool life, blank holding force values of 5, 20, and 50 kN were examined for a constant forming speed of 250 mm/s. Figure 5 shows the remaining tool coating thickness distribution with different blank holding forces after 300 forming cycles. It clearly indicates that the remaining coating thickness decreases with an increase in the blank holding force.
Figure 6 shows the pressure and remaining coating thickness distribution with blank holding forces of 5, 20, and 50 kN, respectively, along the curvilinear distance of the die after 300 forming cycles. Since the region A-B represents the die entrance region during the U-shape bending process, the pressure and the relative wear distance in this region were much higher than other regions of the die. Consequently, the wear of the coating mainly occurred in this area. There are two peak values of coating thickness reduction at 20 kN and 50 kN that correspond to the two peaks under the pressure. Meanwhile, the remaining coating thickness decreases with the increase of blank holding force. The lowest remaining coating thicknesses with blank holding forces of 5, 20, and 50 kN, were 0.905, 0.570, and 0.403 microns, respectively, where the initial coating thickness was 2.1 microns.
<img alt="Figure 1" src="/files/ftp_upload/53957/53957fig1.jpg" /> 
Figure 1: Comparison between experimental and predicted forming limit strains at different temperatures. The forming limit strains increase as temperature rises, at a constant speed of 250 mm/s, or equivalently, a strain rate of 6.26 s-1. <a href="https://www.jove.com/files/ftp_upload/53957/53957fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/53957/53957fig2.jpg" /> 
Figure 2: Schematic chart for knowledge based cloud FE simulation of a sheet metal forming process. Commercial FE simulation software, is used to run the simulation and export the results required for the individual modules. The modules, e.g., formability, heat transfer, post-forming strength (microstructure), tool life prediction, tool design, etc., work simultaneously and independently in the cloud, hence enabling the integration of cutting edge knowledge from multiple sources into FE simulations. <a href="https://www.jove.com/files/ftp_upload/53957/53957fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/53957/53957fig3.jpg" /> 
Figure 3: Geometry of the work piece and tools for the U-shape bending simulation. The tools, i.e., punch, blank holder and die, are modeled using rigid elements. Shell elements are used for the work piece (blank) elements. <a href="https://www.jove.com/files/ftp_upload/53957/53957fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/53957/53957fig4.jpg" /> 
Figure 4: Evolution of blank shape for hot stamping of a door inner panel (displayed in FE simulation). Left: The figures in green frames represent blank shapes at each optimization stage, and the ones in red frames correspond to the blank shape before its optimization. Right: Necking prediction results at each optimization stage. (a) Initial results with large failure (cracking/necking shown in red color), (b) Reduced failure with some necking after first stage of optimization, (c) Final optimized blank shape with no visible necking. <a href="https://www.jove.com/files/ftp_upload/53957/53957fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/53957/53957fig5.jpg" /> 
Figure 5: The remaining coating thickness distribution (displayed in FE simulation) with blank holding forces of: (a) 5 kN, (b) 20 kN, and (c) 50 kN, after 300 forming cycles at a constant stamping speed of 250 mm/s. <a href="https://www.jove.com/files/ftp_upload/53957/53957fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" src="/files/ftp_upload/53957/53957fig6.jpg" /> 
Figure 6: Prediction of contact pressure and remaining coating thickness with blank holding forces of: (a) 5 kN, (b) 20 kN, and (c) 50 kN, along the curvilinear distance of the die at a constant stamping speed of 250 mm/s. <a href="https://www.jove.com/files/ftp_upload/53957/53957fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes at JoVE.com

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

The following paper presents a novel FE simulation technique (KBC-FE), which reduces computational cost by performing simulations on a cloud computing environment, through the application of individual modules. Moreover, it establishes a seamless collaborative network between world leading scientists, enabling the integration of cutting edge knowledge modules into FE simulations.

The use of Finite Element (FE) simulation software to adequately predict the outcome of sheet metal forming processes is crucial to enhancing the ...

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