T h e I n t e r n a t i o n a l J o u r n a l o f F o r g i n g B u s i n e s s & Te c h n o l o g y

February 2013Vol. 5, No 1www.FORGEmag.com

Computer Optimization of Computer Optimization of Forging ProcessesForging Processes

Viking Forge: Expanding for the Future Forge Fair 2013 Preview Forging Copper and its Alloys

February 2013 5

Departments & ColumnsEditor’s Page .................................................................. 6

FIA’s Public Policy Watch ............................................ 8

Guest Column ..............................................................10

News ..............................................................................12

Products ........................................................................40

Classifi ed .......................................................................42

Ad Index ........................................................................46

FEATURESCONTENTS

COVER STORY p. 35

17 23 29 35

17232935

Viking Forge: Expanding for Future GrowthRunning at full capacity, Viking is currently undergoing an $8 million expansion that will update the entire opera-tion and leave room for future growth.

Forge Fair 2013 PreviewSponsored by FIA, this triennial event will be held March 26-28 in Columbus, Ohio. Th e full spectrum of forging operating needs will be covered with vendor displays featuring innovations in all operating areas.

Forging Materials: Copper AlloysTh is article concludes FORGE’s exclusive series on forging materials. Applications of forged copper components, forging issues and special considerations for forgers who handle copper alloys are also discussed.

COVER STORYComputerized Optimization of Closed-, Open-Die Forging ProcessesNumerical simulation has become essential in most forging operations. Th e trial-and-error method has been replaced by sophisticated simulation soft ware that can accommodate the whole manufacturing process.

February 2013 35

ramatic improvements in computer hardware

capacity, as well as the constant development of

more efficient algorithms, have made it possible

to simulate the most complex forged parts

within a very short computation time. A case in point is the

simulation package FORGE 2011, developed by the Centre

de Mise en Forme des Materiaux (CEMEF) laboratory of

Mines ParisTech and distributed by Transvalor.

The program is able to give accurate simulation results for

a variety of closed-die parts – such as crankshafts, knuckles,

axles, connecting rods or camshafts – within a few hours on

high-performance computing systems such as clusters or

multi-core computers. Common processes such as open-die

forging, ring rolling, spinning or cold forging are simulated

on a daily basis by numerous users around the world.

Although trial-and-error techniques have had a long

run, their replacement by numerical techniques has thus

far brought little change in methodology. Instead of a

real trial, a numerical one is made on the computer, and

the user runs a number of different simulations until an

acceptable result is produced. These numerical input data

are then translated into real tool machining and process

parameters in order to make the “first time right” real trial,

which validates simulation results. Although this virtual

technique is faster and more cost effective, it still requires

extensive human involvement in data preparation and,

most importantly, in result review and analysis.

Moreover, a lot of experience and know-how of

the forming process are needed in order to design the

necessary changes that will improve the process.

MAES AlgorithmIn order to partly overcome this weakness and improve

simulation benefits, different optimization algorithms

are available. FORGE 2011 by Transvalor embeds a Meta-

model Assisted Evolution Strategy (MAES) algorithm,

which is described by the flow chart in Figure 1.

Instead of the user having to manually define

parameters to reach his objectives, the MAES algorithm,

coupled with the Finite Element software, generates

sets of parameters to reach the objective(s) under given

constraints. The relevant parameters, the variations they

may have within a specific range and the constraints

simulation results must comply with are defined by the

user in the optimization preparation stage.

Optimization ExamplesCrankshaftThe fi rst example shows the benefi t of using automatic

optimization in conjunction with forging simulation. The

forging sequence of this crankshaft is described in Figure 2.

In this example, the objective is to minimize the cut weight

of the initial billet, which in this case is 355 pounds. The

parameters of the optimization apply to the rolled billet and

relate to its length and large diameters. The constraint applies

D

Michel Pereme, Richard Ducloux, Patrice Lasne, Stéphane Marie, Andres Rodriguez, Julien Barlier, Mickaël Barbelet; Transvalor, FranceLionel Fourment, Jean Loup Chenot; ParisTech, CEMEF, France

In recent years, numerical simulation has been used widely by the metal-forging industry and has become essential in most forging operations. The traditional, time-consuming and costly trial-and-error method has been replaced by increasingly sophisticated simulation software that can accommodate the whole manufacturing process from shearing to multistage forging, flash trimming and through to quenching.

Initialization

Function evaluation

Optimum

OK? Selection Recombination Mutation

Select best individuals Fit metamodel

N

Y

COMPUTERIZED COMPUTERIZED Optimization of Closed-, Optimization of Closed-, Open-Die Forging ProcessesOpen-Die Forging Processes

Figure 1. Overview of the MAES algorithm

36 February 2013

❱❱❱ Computerized Optimization

to the fi nisher stage, where a complete fi lling of the dies is required.

The result of this automatic optimization is shown in Figure 3.

The flash pattern in the finisher stage using the initial rolled billet

(355 pounds) is shown in red. The flash pattern of the optimized

rolled billet (311 pounds) is shown in blue.

Open-Die ForgingThe second example relates to the optimization of an open-die

forging process as described in Figure 4. The material to be forged

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is stainless steel, and it is forged in four passes.

In this case, the objective is to improve the microstructure

of the bar after cogging (i.e. maximizing the average ASTM

grain size). The parameters of the optimization are the initial

temperature of the bar (ranging from 1000-1250°C/1800-2282°F)

and the velocity of the tools (ranging from 40-60 mm/s). The

worst results are shown in Figure 5; the best results in Figure 6.

In both cases, the value shown is the ASTM average size, and the

legend ranges from 3.5 to 6.5.

The optimization shows that better microstructure is achieved

with a lower initial temperature of the bar and higher forming

velocities. It also shows that a high forming temperature generally

gives poor microstructural results independent of the press velocity.

Both preceding examples were run using the commercial fea-

tures of the Finite Element code of the FORGE simulation pack-

age – variation of the billet shape in the crankshaft (Example 1)

and variation of the process parameters in the open-die forging

(Example 2). In some cases, however, a coupling of the MAES

optimizer and CAD packages could be required.

Automotive Part PreformThe final example shows a successful coupling of FORGE software

to a commercial CAD software package for the design of an

automotive part preform. The objective is to optimize the forged

preform after bending in order to fill the finisher die impression

with the least possible amount of material and with no defects

(e.g., no laps or folds).

Figure 7 shows the different parameters allowed to vary

inside the parametric CAD system and optimized by the MAES

algorithm. After each simulation, the CAD system was launched

and fed with the new diameters until an optimum was found.

Figure 8 shows the results of the optimization.

ConclusionThe application of optimization methods to forging and, more

generally, to metal-forming simulation is a relatively new

approach. Though R&D centers and universities have been

working on these techniques in recent years, they have remained

February 2013 37

Figure 3. Optimization result – comparison of the original (in red) and the optimized design (in blue)

Figure 4. Description of the cogging process

Figure 5. Worst results with high forming temperature – smallest average ASTM grain size

Figure 6. Optimized results with low forming temperature – smallest average ASTM grain size

Figure 2. Forging sequence of a heavy-vehicle crankshaft

1. Rolled billet

4. Trimming

2. Blocker

3. Finisher

5. Oil quenching

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38 February 2013

complicated because they usually involve using a variety of very

different codes. The newly integrated MAES algorithm in the

FORGE software system makes it relatively easy to optimize many

parameters in a range of processes such as closed-die forging,

open-die forging, heat treatment and others. The industrial

examples presented in this article were simulated within hours on

recent multi-core systems.

AcknowledgementThis work has been carried out under the auspices of the French

National Research Agency (ANR) through its LOGIC program,

whose support is gratefully acknowledged. The support of Bharat

Forge Kilsta AB (Sweden) is also gratefully acknowledged.

The French National Research Agency’s web address is www.agence-nationale-recherche.fr/en/. For additional product information, please contact Bruno Castejon, president and CEO, Transvalor Americas. He may be reached at 312-558-1781, Bruno.Castejon@transvalor.com or visit www.transvalor.com.

Figure 7. Parameters that may vary in the parametric CAD system; in this case, diameters.

Figure 8. Results of the optimization in the blocker die. Temperature-distribution optimum diameters were found to be 45 mm for D1 and D3 and 35 mm for D2.

D3 D2

D2

D1

D1

❱❱❱ Computerized Optimization

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