Patrick Queutey Lheea
email: patrick.queutey@ec-nantes.fr

Gan Bo

Deng Lheea

Emmanuel Guilmineau
email: emanuel.guilmineau@ec-nantes.fr

Francesco Salvatore
email: francesco.salvatore@cnr.it

A COMPARISON BETWEEN FULL RANSE AND COUPLED RANSE-BEM APPROACHES IN SHIP PROPULSION PERFORMANCE PREDICTION

The paper compares the development of the coupling between a viscous Reynolds-averaged Navier-Stokes (RANSE) method and an inviscid Boundary Element method (BEM) with application to the prediction of the propulsive performance of a propelled ship. The BEM computational model is implemented into the PRO-INS code developed by CNR-INSEAN. It is based on a boundary integral formulation for marine propellers in arbitrary onset non-cavitating and cavitating flow conditions. The RANSE approach is based on the unstructured finite-volume flow solver ISIS-CFD. An essential feature for full RANSE simulations with the ISIS-CFD code developed by ECN-CNRS is in the use of a sliding grid technique to simulate the real propeller rotating behind a ship hull. The STREAMLINE tanker and propeller are proposed as validation test case. Full RANSE simulations are performed for design speed only, while hybrid RANSE/BEM selfpropulsion computations are performed for a speed range. Both computations are compared with experimental data and show good agreement for ship resistance and for propeller thrust and torque.

INTRODUCTION

For a RANSE CFD based method to compute the viscous flow around the ship only, the propeller can be replaced by an equivalent body force acting in the r.h.s of the momentum equations. There are numerous approaches to calculating the body force field including simple prescribed distributions, which recover the total thrust T and torque Q, to more sophisticated methods which use a propeller performance code in an interactive fashion with the RANSE solver to capture propeller-hull interaction and to distribute this force field according to the actual blade loading. The latter require the development of a special interface to extract the effective wake from RANSE solution and to produce a body force field calculated by a propeller performance code.

As a first approximation, ISIS-CFD does, however, include a prescribed body force with axial and tangential components. The radial distribution of forces, is based on non-iterative calculation [START_REF] Stern | A viscous flow approach to the computation of propeller-hull interaction[END_REF], the Hough and Ordway [START_REF] Hough | The generalized actuator disk[END_REF] circulation distribution with optimum type from Goldstein [START_REF] Goldstein | On the vortex theory of screws propellers[END_REF] and without any loading at the root and at the tip. In that case, the integrated torque and thrust of the propeller can be prescribed or self-adapted according to the ship resistance for the propeller thrust or derived from open-water test for the torque.

Higher sophisticated solution can be obtained by a propeller performance code based upon the BEM theory for the potential flow around the real propeller. The body forces in axial and rotative directions result from integration over the boundaries of the fluid domain. The distributions are computed iteratively with the RANSE solver that provides the propeller inflow velocities. In that case no rotating grid is still required from the RANSE side, and the computational cost is similar to a now classical resistance test from a RANSE simulation including ship motion and freesurface effects. Description of the theoretical and computational methodology with examples about the BEM method retained and implemented in PRO-INS are given in [START_REF] Greco | Validation of a quasi-potential flow model for the analysis of marine propellers wake[END_REF][5][6].

To move a step forward, the use of full RANSE simulations of the flow around the ship and around the rotating propeller in a single numerical framework including free-surface effects could be considered now, however in calm water conditions, as a matter of routine given the currently available computational resources. A common practice to take account for the relative motions of multiple bodies is to manage multiple domains such as the overlapping technique [START_REF] Carrica | Selfpropulsion computations using a speed controller and a discretized propeller with dynamic overset grids[END_REF][START_REF] Muscari | Numerical simulation of the flow past a rotating propeller behind a hull[END_REF] and the sliding grid technique [START_REF] Visonneau | Sliding grids and adaptive grid refinement for rans simulation of ship-propeller interaction[END_REF][START_REF] Queutey | Sliding grids and adaptive grid refinement for rans simulation of shippropeller interaction[END_REF][START_REF] Visonneau | Sliding grids and adaptive grid refinement applied to ship hydrodynamics[END_REF] to allow a part of the grid (containing the propeller) to rotate within the main part of the grid, while keeping a connection between the two parts. Nevertheless, the cost is still prohibitive to be a common practice if we consider, as an example, the challenging computation of a self-propelled ship in seakeeping situations involving severe motions and multiple time scales involved, even in monochromatic waves [START_REF] Carrica | Cfd analysis of broaching for a model surface combatant with explicit simulation of moving rudders and rotating propellers[END_REF].

The aim of the paper is to illustrate that a preliminary design can be achieved by the cost-effective RANSE/BEM hybrid model. Then full RANSE flow simulations allow a careful assessment of claimed design performance. This constitutes a significant step bearing in mind that one of the main goals is the simulation of ship propellers in extreme operating conditions, with accurate modelling of all the physics involved such as the effects of free-surface deformation, the ventilation and the cavitation phenomena. For first comparison purpose, the 7000 DWT tanker and propeller addressed in the EU-funded project STREAM-LINE proposed as validation test-case. Full RANSE simulation is performed for design speed only, while hybrid RANSE/BEM self-propulsion computation is performed for a speed range for which experimental data are available.

GEOMETRY AND MAIN PARTICULARS

The ship investigated is a 7000 DWT vessel which a singlescrew vessel with a bulbous bow. This "STREAMLINE tanker", as shown in Figure 1. During the project, this vessel performance has been extensively characterized through tests performed on a 16.5 scaled model.

Table 1 shows main geometrical characteristics of the hull: length between perpendiculars L PP , length of waterline L W L , draft at forward perpendicular T F , draft at aft perpendicular T A , volume displacement ∇, wetted surface S W and design speed V . The scale factor is λ = 16.5.

The ship is equipped with a propeller and a rudder. The propeller is a four-bladed fixed-pitch propeller with right rotation. Table 2 shows its main characteristics: propeller diameter D, propeller distance from aft perpendicular X P , pitch ratio at 0.7 propeller radius P 0.7 /D, effective skew angle of propeller blades θ e f f , non-dimensional hub diameter at upstream end D F /D and non-dimensional hub diameter at downstream end D A /D.

In Figure 1, the plane X P corresponds to the plane of nominal wake when the hull is without the propeller. The plane X U is located in 0.15D upstream the plane X P while the plane X D is located in 0.22D downstream the plane X P .

For the full RANSE simulations, a prescribed sinkage, -1.45 cm, is imposed as well as a trim angle, defined positive bow up,

NUMERICS

This section is devoted to the viscous and inviscid baseline methods. A description of the rationale of the coupling strategy is presented.

RANSE Flow Solver: ISIS-CFD

ISIS-CFD, available as a part of the FINE TM /Marine computing suite, is an incompressible unsteady Reynolds-averaged Navier-Stokes (URANS) method. The solver is fully implicit, based on the finite volume method to build the spatial discretisation of the transport equations. Surface and volume integrals are evaluated according to second-order accurate approximations and the unstructured discretisation is face-based. Time derivatives are evaluated using three-level Euler second-order accurate approximations. While all unknown state variables are cellcentred, the systems of equations used in the implicit time stepping procedure are constructed face by face. Fluxes are computed in a loop over the faces and the contribution of each face is then added to the two cells next to the face. This technique poses no specific requirements on the topology of the cells. Therefore, the grids can be completely unstructured; cells with an arbitrary number of arbitrarily-shaped faces are accepted.

Pressure-velocity coupling is obtained through a Rhie & Chow SIMPLE type method: in each time step, the velocity updates come from the momentum equations and the pressure is given by the mass conservation law, transformed into a pressure equation. In the case of turbulent flows, transport equations for the variables in the turbulence model are added to the discretisation. Free-surface flow is simulated with a multi-phase flow approach: the water surface is captured with a conservation equation for the volume fraction of water, discretised with specific compressive discretisation schemes, see [START_REF] Queutey | An interface capturing method for free-surface hydrodynamic flows[END_REF].

The method features sophisticated turbulence models: apart from the classical two-equation kε and kω models, the anisotropic two-equation Explicit Algebraic Stress Model (EASM), as well as Reynolds Stress Transport Models, are available, see [START_REF] Deng | Comparison of explicit algebraic stress models and second-order turbulence closures for steady flow around ships[END_REF][START_REF] Duvigneau | On the role played by turbulence closures in hull ship optimization at model and full scale[END_REF]. The technique included for the 6 degree of freedom simulation of ship motion is described by Leroyer & Visonneau [START_REF] Leroyer | Numerical methods for RANSE simulations of a self-propelled fish-like body[END_REF]. Time-integration of Newton's laws for the ship motion is combined with analytical weighted or elastic analogy grid deformation to adapt the fluid mesh to the moving ship.

An essential building block for the simulation of ship propellers is a sliding grid technique, which allows a part of the grid (containing the propeller) to rotate within the main part of the grid, while keeping a connection between the two parts. Sliding grid methods for unstructured grids, such as we use, are far more complicated than for structured grid solvers. Among others, the connection between the two subdomains of the grid has to be reconstructed often, as it does not follow a regular pattern. Also, it is not easy to ensure flux conservation over the interface. We have chosen a connection between the domains that does not explicitly guarantee conservation; it is based on connectivities between the cells and faces on the interface that mimic as closely as possible the connectivities for all other cells in ISIS-CFD.

Parallelization is based on domain decomposition. The grid is divided into different partitions; these partitions contain the cells. The interface faces on the boundaries between the partitions are shared between the partitions; information on these faces is exchanged with the MPI (Message Passing Interface) protocol. This method works with the sliding grid approach and the different subdomains can be distributed arbitrarily over the processors.

BEM Solver: PRO-INS

In the proposed hybrid viscous/inviscid model, propeller effects are determined through a hydrodynamic model based on a Boundary Element Method (BEM). The methodology is valid under inviscid flow assumptions and allows to describe the flowfield around a marine propeller operating in a prescribed onset flow. Considering as input the non-homogeneous velocity distribution representing the hull-induced wake, propeller operation in behind hull conditions can be analysed.

The computational model involves the numerical solution of the Laplace equation for the velocity potential via a boundary integral formulation implemented into the PRO-INS code developed by CNR-INSEAN. In its more general formulation, the PRO-INS solver includes a wake alignment model to correctly describe trailing vorticity sheeding process, as described in [START_REF] Greco | Validation of a quasi-potential flow model for the analysis of marine propellers wake[END_REF].

A simple two-phase flow model is integrated with the BEM solver to describe the effects of transient sheet cavitation on propeller loads and on induced velocity and pressure fields radiated to the surrounding flow, see [5] and [6]. In the present work, hydrodynamic loading distribution on propeller blade surface predicted by BEM is recasted as a volume force distribution to define body force terms for the RANSE solver.

RANSE/BEM Coupling

The RANSE/BEM coupling contains the following steps: The ISIS-CFD code can handle any number of boat (limited to 99) with any prescribed and/or solved motion. An arbitrary number of propeller can be attached to each boat. Parallelized computation with adaptive mesh refinement and automatic load balancing is fully supported. Each block interpolates the velocity at the inflow plane located at a prescribed position in front of the propeller for all points at the inflow plane found inside the block. The interpolated results are collected and distributed to all blocks so that each block contains required inflow conditions for all propellers. Those inflow conditions as well as all other information such as the kinematics for all boats are transferred to the potential code using a user defined dynamic subroutine. The potential code can be either integrated directly into this dynamic subroutine, or launched from it using a system call as it is the case in the present study. Data exchange is accomplished with disk files in the latter case.

Body force transfer from the BEM code to the RANSE code, the distance of the inflow plane to the propeller, and the way the propeller induced velocity is computed are the key points in a RANSE/BEM coupling implementation. The most accurate way to achieve the body force transfer is to communicate all cells in the RANSE mesh within the swept volume by propeller blades to the BEM code, and then map the forces acting at the blades to those grid cells directly. However, such implementation is not only too complex, but also too expensive, especially when an unstructured grid, possibly updated dynamically by adaptive grid refinement, is employed by the RANSE code. In the present study, a very simple procedure is adopted. The forces acting on the propeller blades are mapped to a two dimensional cylindrical grid. Based on this two-dimensional distribution, body forces are distributed into a cylindrical volume swept by the propeller blades in the RANSE code. A uniform body force distribution in the axial direction is assumed. Such simple implementation has an impact on the choice of inflow plane distance to the propeller. The BEM code needs the effective velocity which is the difference between the total velocity provided by the RANSE code and the propeller induced velocity. In our implementation, the propeller induced velocity is computed by the BEM code using the real geometry of the propeller, while the total velocity is com-puted by the RANSE code using body forces. Due to the simplification made for the body force transfer from the BEM code to the RANSE code, it is expected that numerical error increases as the inflow plane moves toward the propeller. On the other hand, even without the propeller, the velocity field always changes in space in the wake. To obtain a better prediction, it is desirable to locate the inflow plane as close as possible to the propeller. Numerical experiences show that a distance of 0.15D in front of the propeller is a good compromise. This value is chosen for all the computations in the present study.

As the objective of the present computation is selfpropulsion simulation to predict the forces, a steady BEM computation is performed while unsteady RANSE is always considered to solve the air/water interface. The inflow velocity is averaged in the circumferential direction for such computation. The output body force from the BEM code is also averaged in the circumferential direction. To save computational time, only half domain computation is performed by the RANSE code. Tangential body force is not taken into account in the computation.

MESH AND NUMERICAL DATA

The size of the computational domain is [-16.5 m, 13.5 m] x [-10.0 m, 10.0 m] x [-10.0 m, 1.0 m] in X, Y and Z directions respectively. Z=0 corresponds to the free surface location at rest. The mesh is generated by the unstructured mesh generator Hexpress TM which is a part of the FINE TM /Marine. This software generates meshes only containing hexaedrals.

A grid dependency study has been performed for resistance computation. Three systematically refined grids are generated. The number of grid nodes are indicated in the first three lines of Table 3. In the case where the propeller is present, the part of the grid containing the propeller consists of approximately 1.72 million cells. To allow a better comparison with two other grids including the propeller, the number of nodes indicated in the table correspond to a mesh with a whole domain, while the computations are performed with half boat with mirror boundary condition. For all simulations, the non-linear EASM turbulence model is used.

The mesh size around the propeller is based on a grid convergence analysis achieved about an isolated propeller [START_REF] Visonneau | Sliding grids and adaptive grid refinement for rans simulation of ship-propeller interaction[END_REF] in order to evaluate the minimum settings for the generation of a hexahedral mesh.

RESULTS

First, hull resistance tests are performed. Then, propulsion tests with a hull with and without the rudder are carried out.

Resistance Simulations

Table 4 presents resistance model test results where R T M is the total model resistance, C T M the resistance coefficient, C F0 , C F the numerical friction coefficient and C P the numerical pressure coefficient. At the model scale, the ITTC-57 friction resistance coefficient C F0 is 3.036×10 -3 . Quantity w m corresponds to the normalized mean axial velocity component inside the propeller region in plane X P . That propeller region corresponds to the interior of the disk limited by the black circle in Figure 2. Richardson extrapolation gives a resistance of 63.53 N with an observed order of convergence of 3.7. With such a high observed order of convergence, extrapolation with assumed second order of accuracy is considered to be more reliable. Such extrapolation gives a resistance of 62.38 N, about 1.9% higher than the measured value. Based on this analysis, we can conclude that numerical discretization error for resistance is about 3%. The predicted numerical result agrees with the measurement data within the uncertainty level.

Figure 2 shows the computed and experimental nominal wake fields (both axial velocity component and streamwise velocity vectors normalized by the ship speed) in plane X = X P . The black circle figures the limit of the propeller. On both meshes, the computed streamwise vectors are interpolated on the same circular zone in order to simplify the comparisons. Except on the coarse mesh for the low speed region, the predictions on the medium and on the fine meshes are qualitatively similar. The relative difference of the mean velocity coefficient w m , see Table 4, is only 0.5% between the medium and the fine mesh, and is about 3.6% between the computed value on the medium mesh and the experimental value. For the extrapolated grid converged value the relative error is less than 2% with the measurement that can be explained with the remaining sources of uncertainty, both from the numerics with the error induced by the turbulence modelling and from the experiments considering the noticeable asymmetry in the measured wake. This justifies the use of the medium mesh for this kind of computation. The computed w m underestimates the measurement which means that the mean axial speed at the propeller plane is lower than expected. As a con-sequence it is expected that the thrust will be overestimated but it should be mentioned that a propulsion test is performed with the rudder. Therefore, considering the significantly lower value of the computed w m with the rudder, 0.684 instead of 0.720, it is hard to anticipate now on the behavior of the computation with propeller.

Table 5 presents full scale ship resistance where R T S is the total resistance for ship, C T S the resistance coefficient. The other coefficients are identical to those defined in table 4. The extrapolation to full scale ship is made on the assumption that residuary resistance in dimensionless form is identical for model and ship at the same Froude's number. A roughness allowance of 0.575 × 10 -3 is included in the extrapolation to full-scale of the measurements. This explains why the extrapolated ship resistance measured is higher than the numerical result predicted without taking into account wall roughness.

Figure 3 compares the isowake contours at both model and full scale without propeller at station X = X P . The scale factor of 16.5 gives a ratio of about 63 for the Reynolds numbers between the model (Re = 9.35 × 10 6 ) and the ship (Re = 5.87 × 10 8 ). The first clearly visible consequence is the dramatic reduction of the boundary layer thickness in full scale. This results in increased axial velocity magnitude in the wake of the upper hull region. An other interesting phenomena which can be observed in the same figure at full scale is the reduction of the low velocity region located on the external side of the propeller shaft. This could be correlated with the reduction of the strength of the upper longitudinal vortex.

Propulsion Simulations

While self-propulsion simulation is performed with RANSE/BEM approach, full RANSE computations are performed with prescribed propeller revolution rate using the value obtained in the measurement for model scale computation, and the extrapolated value for full scale computation. A time accurate full RANSE simulation is still time consuming and self-propulsion prediction could be considered from the thrust identity method as we used about the KCS container ship [START_REF] Visonneau | Sliding grids and adaptive grid refinement for rans simulation of ship-propeller interaction[END_REF]. In the self-propulsion RANSE/BEM computation, propeller revolution rate is adjusted every 10 time steps such that the estimated propeller thrust is in balance with the ship resistance and the added additional friction correction towing force F D = 13.86 N. Table 6 presents propulsion model test results where F X is the resistance of the hull, n the propeller rate of revolutions, T M the propeller thrust and Q M the propeller torque. Table 7 presents propulsion results at full scale with corresponding propeller thrust T S and propeller torque Q S .

The propeller thrust in the computation is obtained by integrating forces on the propeller blades without the propeller hub. The hub is not included in the BEM computation. Results obtained by RANSE/BEM coupling approach are in good agreement with the measurement data for all quantities with discretization error less than a few percents. However, unlike in the measurement, the rudder is not included in those computations. Full RANSE computation indicate that without the rudder, propeller thrust is under-estimated by about 6% in model scale. With rudder included as in the measurement, the full RANSE simulation with prescribed propeller revolution rate under estimates propeller thrust by about 4% in model scale and by about 4% in full scale. Similar result is observed in our previous computation for the KCS test case [START_REF] Visonneau | Sliding grids and adaptive grid refinement applied to ship hydrodynamics[END_REF]. Using the total velocity provided by the full RANSE computation including the rudder, the BEM code over estimates the thrust by about 4% in model scale but under estimes it by about 5% in full scale. Based on those observations, as regards the model scale simulation, we could conclude that a full RANSE simulation under estimates the propeller thrust while a RANSE/BEM coupling approach over estimates it. While turbulence modelling error might be the main reason for the under-estimation observed in a full RANSE computation, the over-estimation observed in the RANSE/BEM coupling computation in model scale could come from sources of uncertainty or inaccuracy related with the RANSE/BEM coupling strategy. In particular, the propeller induced velocity employed in the RANSE/BEM coupling procedure is computed by the BEM code in a open domain without taken into account the effect of the hull and the appendages. A careful analysis of those aspects of a RANSE/BEM coupling are beyond the scope of the present work.

The flow field is averaged during the last period of rotation to generated the mean flow quantities. The specific wake plane X D is selected to analyse the mean wake flow downstream of the propeller as shown in Figures 4 and5 for model and full scale simulations respectively. In both cases the simulations with and without rudder are included.

The axial velocity contours are characterized by two regions inside and outside the propeller disk. With the rudder at both model and full scale and inside the propeller disk, one notice an asymmetric behavior with high normalized velocity between 1.1 and 1.3 on the left and between 1.1 and 1.2 on the right. This differs from the case without rudder characterised by a moon crescent-like shape of the high velocity region. The presence of two separate regions indicates already the influence of the rud- der leading edge located downsteam this plane. This explains why a single region with high axial velocity immediately downsteam of the propeller has been split into two parts in that plane.

The main visible scale effect concerns the region of low velocity between the hull and the top of the propeller. To compensate for the increase in mass flow rate through the propeller disk, the surrounding mass flow rate is decreased and particularly on the top of the propeller. In model scale the upstream effect of the rudder is to significantly reduce the intensity of the axial velocity component. This is also verified in full scale but with higher distorsion of the contour lines.

Figure 6 shows a comparison of a iso-surface of dimensionless second invariant at model and full scales. Both simulations predict the same topology of flow behind the propeller. The tip vortices generated by the propeller are dissipated in the far wake since the mesh is not dense enough to maintain these structures accurately. In both cases, one significant effect of the rudder and propeller interaction is to deflect upwards the helical shape formed by the tip vortices.

An important point of the simulations concerns the CPU cost. The full unsteady RANSE simulation uses a small time step to resolve the propeller (0.0006s) but the RANSE/BEM computation is much faster with a time step adpated to free-surface capturing only (0.08s) since the objective is the prediction of the forces. The cost-effectiveness is that the CPU effort for the steady RANS/BEM coupling is divided by a factor of 50 compared to the full RANS simulation CPU effort.

CONCLUSIONS

The objective of this paper is the validation of the development of a RANSE/BEM coupling with comparison to a full RANSE approach for the the prediction of the propulsive performance of a propelled ship. A 7000 DWT tanker and propeller addressed in the EU-funded project STREAMLINE is used as validation test-case. The steady RANSE/BEM coupling is performed in three steps to exchange iteratively the total inflow velocity predicted by unsteady RANSE to steady BEM and the body force field representing the propeller effects in RANSE from BEM. Grid convergence analysis has been conducted about a resistance test simulation and the self-propulsion test involving the RANSE/BEM coupling in model scale. Promising results are obtained with the hybrid RANSE/BEM self-propulsion computations as regards the forces at both model and full scale with comparison to the time resolved full RANSE approach and also with the experiments. Furthermore the computational cost is incomparably lower with the hybrid approach so that preliminary design can be effectively achieved that way.

ACKNOWLEDGMENT

This work was granted access to the HPC resources of IDRIS under the allocation 2011-21308 made by GENCI (Grand Equipement National de Calcul Intensif).

Part of the work was supported by the STREAMLINE project, a collaborative R&D project, partly funded by the 7th Framework Programme of the European Commission (FP7).

FIGURE 1 .

 1 FIGURE 1. GEOMETRY OF THE TANKER.

  (a) The RANSE code computes the flow around the hull and provides total velocity as inflow condition to the potential code. (b) Using the total velocity provided by the RANSE code, the potential code performs a simulation for a rotating propeller. Effect of the propeller is represented by body forces. (c) Those body forces are added into the right hand side of the Navier-Stokes equation in the RANSE code. They are also taken into account when solving the motion equation for the boat.

  (a) COARSE MESH. (b) MEDIUM MESH. (c) FINE MESH. (d) EXPERIMENTS.

FIGURE 2 .

 2 FIGURE 2. DISTRIBUTION OF NOMINAL WAKE AXIAL VELOCITY AND TRANSVERSAL COMPONENTS AT MODEL SCALE.

  (a) MODEL SCALE. (b) FULL SCALE.

FIGURE 3 .

 3 FIGURE 3. COMPARISON OF DISTRIBUTION OF NOMINAL WAKE AXIAL VELOCITY AND TRANSVERSAL COMPONENTS AT MODEL SCALE AND FULL SCALE.

  (a) WITHOUT RUDDER. (b) WITH RUDDER.

FIGURE 4 .

 4 FIGURE 4. FULL RANSE: INFLUENCE OF THE RUDDER FOR THE AXIAL VELOCITY AT X = X D AT MODEL SCALE.

TABLE 1 .

 1 MAIN DIMENSIONS OF HULL.

		Model	Full scale
	L PP [m]	5.696970	94.000
	T F , T A [m] 0.363923	6.005
	∇ [m 3 ]	1.518355	6820.6
	S W [[m 2 ] 8.335159 2269.247
	V [m/s]	1.773	7.2022

TABLE 2 .

 2 PROPELLER PARAMETERS

		Model	Full scale
	D [m]	0.233333	3.85
	X P [m]	0.128121	2.114
	P 0.7 /D [-]	1.0	1.0
	θ e f f [deg]	13.0	13.0
	of -0.2 degrees. For the resistance and the RANSE/BEM simula-
	tions, the hull is free in trim and sinkage, and the values obtained
	are similar.

TABLE 4 .

 4 RESISTANCE AT MODEL SCALE. Rudder R T M [N] C T M [10 -3 ] C F [10 -3 ] C P [10 -3 ]w m

	Experiments	No	61.19	4.676			0.747
	Coarse mesh	No	66.43	5.076	3.047	2.029	0.712
	Medium mesh	No	64.75	4.948	3.057	1.892	0.720
	Fine mesh	No	64.09	4.898	3.072	1.826	0.724
	Extrapolation	No	62.38	4.766		1.682	0.733
	Medium mesh	Yes	65.05	4.971	3.071	1.901	0.684

TABLE 5 .

 5 RESISTANCE AT FULL SCALE. Rudder R T S [kN] C T S [10 -3 ] C F0 (ITTC) [10 -3 ] C F [10 -3 ] C P [10 -3 ]

	Extrapolation	No	218.00	3.641	1.637
	Medium mesh	No	211.88	3.509	1.637	1.757	1.752
	Medium mesh	Yes	214.29	3.540	1.637	1.770	1.769

TABLE 6 .

 6 PROPULSION RESULTS AT MODEL SCALE.

	Experiments		Yes	72.54	8.92	58.68	2.254
	Coarse mesh	RANSE/BEM	No	74.61	9.01	60.94	2.307
	Medium mesh RANSE/BEM	No	72.50	8.92	58.64	2.233
	Fine mesh	RANSE/BEM	No	71.60	8.90	57.88	2.218
	Medium mesh RANSE/BEM	Yes	76.80	9.02	62.91	2.386
	Fine mesh	FULL RANSE	No	69.87	8.92 *	52.52	2.164
	Fine mesh	FULL RANSE	Yes	71.73	8.92 *	56.16	2.247

CFD Model

Rudder

F X [N] n [rpm] T M [N] Q M [Nm] * Computation with imposed revolution rate

TABLE 7 .

 7 PROPULSION RESULTS AT FULL SCALE. CFD Model Rudder F X [kN] n [rpm] T S [kN] Q S [kNm]

	Extrapolation		Yes	271.00	132.2	271.00	171.00
	Medium mesh RANSE/BEM	No	244.28	131.7	244.50	155.40
	Medium mesh RANSE/BEM	Yes	256.92	131.9	256.87	160.42
	Fine mesh	FULL RANSE	No	244.53	132.2 *	237.31	154.27
	Fine mesh	FULL RANSE	Yes	280.00	132.2 *	260.44	161.78

*

Computation with imposed revolution rate

Copyright c 2013 by ASME


E Asadollahi-Yazdi
email: elnaz.asadollahiyazdi@utt.fr

J Gardan

P Lafon



Abstract-Nowadays, manufactures are encountered with production of different version of products due to quality, cost and time constraints. On the other hand, Additive Manufacturing (AM) as a production method based on CAD model disrupts the design and manufacturing cycle with new parameters. To consider these issues, the researchers utilized Design For Manufacturing (DFM) approach for AM but until now there is no integrated approach for design and manufacturing of product through the AM. So, this paper aims to provide a general methodology for managing the different production issues, as well as, support the interoperability with AM process and different Product Life Cycle Management tools. The problem is that the models of System Engineering which is used for managing complex systems cannot support the product evolution and its impact on the product life cycle. Therefore, it seems necessary to provide a general methodology for managing the product's diversities which is created by using AM. This methodology must consider manufacture and assembly during product design as early as possible in the design stage. The latest approach of DFM, as a methodology to analyze the system comprehensively, integrates manufacturing constraints in the numerical model in upstream. So, DFM for AM is used to import the characteristics of AM into the design and manufacturing process of a hybrid product to manage the criteria coming from AM. Also, the research presents an integrated design method in order to take into account the knowledge of layers manufacturing technologies. For this purpose, the interface model based on the skin and skeleton concepts is provided, the usage and manufacturing skins are used to show the functional surface of the product. Also, the material flow and link between the skins are demonstrated by usage and manufacturing skeletons. Therefore, this integrated approach is a helpful methodology for designer and manufacturer in different decisions like material and process selection as well as, evaluation of product manufacturability.

Keywords-Additive manufacturing, 3D printing, design for manufacturing, integrated design, interoperability.

I. INTRODUCTION

DDITIVE Manufacturing and its different technologies were derived from rapid prototyping. These techniques have been evolving over three decades in the industry [START_REF] Bourell | Roadmap for additive manufacturing: identifying the future of freeform processing[END_REF]. AM is like a revolution in the ways products are designed, manufactured, and distributed to end users in the academic and industrial environment [START_REF] Gao | The status, challenges, and future of additive manufacturing in engineering[END_REF]. The main difference which distinguishes AM with other conventional process is the 'layer by layer' manufacturing by adding material(s), which makes AM capable to realize extremely complex geometries. This unique characteristic of AM changes the design and it makes the designer free of constraints to design complex products. Also, it is necessary to consider all the manufacturing constraints as soon as possible to create an integrated approach. By this way, it helps the designer and manufacturer in their decisions to provide the better solution to comply with the desired product shapes [START_REF] Boschetto | Design for manufacturing of surfaces to improve accuracy in Fused Deposition Modeling[END_REF]. It is better to consider these issues in design stage and provide an integrated design based on the concurrent engineering aspects. In other researches, DFM is used for other manufacturing processes to ease manufacturing by optimizing manufacturing, quality, productivity, reliability, cost, time of production and time to market [START_REF] Skander | Méthode et modèle DFM pour le choix des procédés et l'intégration des contraintes de fabrication vers l'émergence de la solution produit[END_REF]- [START_REF] Skander | Design and manufacturing interface modelling for manufacturing processes selection and knowledge synthesis in design[END_REF]. Recently, researchers provided some approaches to apply DFM approach for AM [START_REF] Boschetto | Design for manufacturing of surfaces to improve accuracy in Fused Deposition Modeling[END_REF], [START_REF] Rosen | Computer-Aided Design for Additive Manufacturing of Cellular Structures[END_REF]- [START_REF] Thompson | Process chain modeling and selection in an additive manufacturing context[END_REF]. In these researches, they provided a new approach named Design For Additive Manufacturing (DFAM) but in fact DFAM is not a new concept; it is just applying the DFM approach for AM like another manufacturing process. Also, due to these researches, an integrated approach of DFM for AM is not presented till now. So, our objective is to provide an integrated and complete DFM approach for AM to manage the new manufacturing criteria coming from AM to create an interoperable process into product development in order to help designer and manufacturer in their decisions. For this purpose, DFM-Skin and Skeleton approach which was presented for traditional manufacturing process [START_REF] Skander | Méthode et modèle DFM pour le choix des procédés et l'intégration des contraintes de fabrication vers l'émergence de la solution produit[END_REF], [START_REF] Skander | Design and manufacturing interface modelling for manufacturing processes selection and knowledge synthesis in design[END_REF] is used to define the different attributes of design and manufacturing process. Skin and skeleton model is used to model AM. In Section II, AM and its criteria will be explained. In Section III, the integrated design and DFM approach will be described in summary and in Section IV, a brief literature review is presented about the DFM approaches for AM which were presented by other researchers. Then, proposed approach of the paper will be explained comprehensively in Section V. Finally, the conclusions and future works are provided.

II. ADDITIVE MANUFACTURING

Rapid prototyping is created to help the realization of what engineers have in mind in 1980's [START_REF] Wong | A review of additive manufacturing[END_REF]. Nowadays, this technology has other names like 3D printing and AM. The main advantage of AM is to create almost any possible and complex geometry without using the fixtures, tooling, mold or any other additional auxiliary [START_REF] Zhang | Evaluating the Design for Additive Manufacturing: A Process Planning Perspective[END_REF] and by increasing the Integrated Design in Additive Manufacturing Based on Design for Manufacturing E. Asadollahi-Yazdi, J. Gardan, P. Lafon A complexity, the cost is not increasing contrary to traditional production methods. The fundamental attribute of AM is fabricating parts by creating layers successively which are cross-sectional. For this purpose, the process is started from a tree-dimensional solid model which is modeled by CAD model or it is achieved by reverse engineering, then the model based on the appropriate resolution is sliced into the thousands of layers. Finally, each layer is created via different technologies of AM which are explained comprehensively later [START_REF] Gao | The status, challenges, and future of additive manufacturing in engineering[END_REF]. AM can be utilized in short-term prototype manufacturing and small-scale series production, multimaterial parts, as well as, tooling application [START_REF] Stampfl | Additive Manufacturing Technologies[END_REF]. It is like a cure for the ills of manufacturing systems such as intermediation, stock flows, divergence of functions, workforce, productivity and labor cost savings [START_REF] Cozmei | Additive Manufacturing Flickering at the Beginning of Existence[END_REF].

Generally, this type of manufacturing is combination of flexibility, speed and low cost which make this type of manufacturing so disruptive and interesting [START_REF] Cozmei | Additive Manufacturing Flickering at the Beginning of Existence[END_REF]. STL (STereoLithography or Standard Tessellation Language) file format as a standard is used to adapt all the CAD models to different AM technologies. There are another file types like SLC, SLI, CLI, HPGL and IGES [START_REF] Halloran | Photopolymerization of powder suspensions for shaping ceramics[END_REF] but the STL is the standard for every AM process. STL file creation process is to convert the continuous geometry in the CAD file into small tringles, this process is not accurate but the smaller triangles are closer to the reality [START_REF] Noorani | Rapid prototyping: principles and applications[END_REF], [START_REF] Gardan | Additive manufacturing technologies: state of the art and trends[END_REF].

AM has found appropriate status between different methods of production due to its different benefits including producing complexes geometries without any additional cost, accuracy, flexibility, positive impact on sustainability, geographically delocalized production, less waste in material, no need for additional tooling, re-fixturing, etc. [START_REF] Cozmei | Additive Manufacturing Flickering at the Beginning of Existence[END_REF]. Despite these advantages, AM is encountered with different disadvantage including interdependency between the material and physical process, limitation in material selection, no efficiency for serial production, longer design process than manufacturing process, capacity limitation, mechanical property limitations, surface finishing problem and quality assurance as well as, the problems for post process machining [START_REF] Cozmei | Additive Manufacturing Flickering at the Beginning of Existence[END_REF].

Different available technologies of AM can be categorized in different groups based on the ASTM International standard which is show in Table I. Each category uses special kind of technology for production, Also, the printing ink and power source for fabrication are different based on the different materials and their characteristics, according to these differences, different properties and levels of quality are created [START_REF] Gao | The status, challenges, and future of additive manufacturing in engineering[END_REF]. For one technology to another, different criteria such as part orientation, manufacturing direction and material behavior, slicing strategy and layer thickness , speed and postprocessing procedure are important to get an efficient production and accurate model [START_REF] Gardan | Additive manufacturing technologies: state of the art and trends[END_REF]. For example, in Fused deposition modeling, the most important criteria are the layer thickness, road width, air gap, deposition speed, infill density, infill and support structure, etc. which must be determined to provide an efficient process.

Today, AM is used in different domains such as light weight machines, aeronautic, biomedical, architectural modelling, art and specially in customization production [START_REF] Wong | A review of additive manufacturing[END_REF] and other scopes that everyday different progress is adding. Therefore, it is necessary to improve the performance of the AM in different aspects. In the next section, the Integrated Design as a method for improving the AM by integrating the design step and manufacturing constraints is described.

III. INTEGRATED DESIGN

According to the standard (NF X50-415,94), Concurrent Engineering or Simultaneous Engineering is the process of taking into account the needs of each different stages of the product life cycle simultaneously [START_REF]Management des systèmes -Ingénierie intégrée -Concepts généraux et introduction aux méthodes d'application[END_REF]. Therefore, concurrent engineering aims to integrate product life cycle knowledge earlier during the design process and different engineering activities must be integrated together and performed in parallel rather than in sequence [START_REF] Skander | Design and manufacturing interface modelling for manufacturing processes selection and knowledge synthesis in design[END_REF], [START_REF] Sohlenius | Concurrent Engineering[END_REF]. So, the iteration between the design activities which create the advantages in time, quality and cost is reduced. The strategy of concurrent engineering is to integrate the material and manufacturing constraints into the design procedure as well as, tool utilization must be computerbased to ensure the accuracy [START_REF] Skander | Méthode et modèle DFM pour le choix des procédés et l'intégration des contraintes de fabrication vers l'émergence de la solution produit[END_REF]. Since, the design is the most important stage in the product life cycle and simultaneous engineering is related to the design step, it is necessary to explain the integrated design.

There is a need to determine the design step in detail to define a product. Also, by optimizing the design stage the total cost can be reduced up to 70% thereby the production cost is optimized significantly [START_REF] Skander | Méthode et modèle DFM pour le choix des procédés et l'intégration des contraintes de fabrication vers l'émergence de la solution produit[END_REF]. Integrated design is an approach considering all aspects of the product life cycle in its design like functions, analysis, manufacturing, assembly, recycling, etc. [START_REF] Tichkiewitch | De la CFAO à la conception intégrée[END_REF]. In this approach, the design solution is based on the material constraints, attributes and designer experiences. The integrated design is illustrated in Fig. 1. Fig. 2 Design for x in concurrent engineering [START_REF] Skander | Design and manufacturing interface modelling for manufacturing processes selection and knowledge synthesis in design[END_REF] According to Fig. 1, indeed, the simultaneity is presented by overlapping stages of the design process and integration through the involvement of professional actors and stakeholders on each step. This approach is known as the design for X (DFX) where X represents the different professional activities. In the part c, this design approach is presented according to the work of our research team-LASMIS. DFX emphasizes the intervention of different expertise profession in the product definition [START_REF] Skander | Méthode et modèle DFM pour le choix des procédés et l'intégration des contraintes de fabrication vers l'émergence de la solution produit[END_REF]. Therefore, DFX which is linked to the concurrent engineering or it can be treated no longer independently, is assessing and integrating "x-field" information. In Fig. 2, the status of DFX in concurrent engineering framework is shown [START_REF] Skander | Design and manufacturing interface modelling for manufacturing processes selection and knowledge synthesis in design[END_REF]. In this research, DFM approach is used to provide an integrated framework for AM which will be explained in the rest.

Generally manufacturing is a sequence of processes for transforming raw or partially processed material into a final product that has value for the customer [START_REF] Pahl | Conceptual Design[END_REF]. The manufacturing processes are chosen during the conceptual design of the product. The all pre-defined design constraints including the manufacturability of the part using the company's or suppliers existing machines and processes must be met in design phase [START_REF] Effa | Using the Case Method to Facilitate Learning of Design for Manufacturing and Cost[END_REF]. Also, various studies show that detecting and rectifying the errors in the design phases of the product cost less than when rectifying at manufacturing or further downstream stages [START_REF] Rajadhyaksha | Turning designs into reality: The Manufacturability paradigm[END_REF]. So, manufacturing and assembly must be considered during product design as early as possible in the design cycle [START_REF] Skander | Méthode et modèle DFM pour le choix des procédés et l'intégration des contraintes de fabrication vers l'émergence de la solution produit[END_REF]. Since, about 70% of manufacturing costs of a product (cost of materials, processing, and assembly) are determined by design decision. Therefore, handling manufacturing problems in the design stages has influence on the cost, time and quality [START_REF] Rajadhyaksha | Turning designs into reality: The Manufacturability paradigm[END_REF] , these issues underscore the need for approach of DFM to integrate the product design and process planning and manufacturing into one common activity in order to design a product that is easily and economically manufactured. So, The DFM approach which is chosen for our study will be explained in the next section.

A. DFM DFM as an integral methodology aims to simplify the manufacturing process, increase the productivity and minimize cost while maintaining the product quality in desirable level [START_REF] Effa | Using the Case Method to Facilitate Learning of Design for Manufacturing and Cost[END_REF]. This technique is used to optimize the product and process concepts during the design phase of a product to ensure ease of manufacture [START_REF] Venkatachalam | A knowledge-based approach to design for manufacturability[END_REF] by optimizing the manufacturing, quality, productivity, reliability, cost, time of production and time to market [START_REF] Skander | Méthode et modèle DFM pour le choix des procédés et l'intégration des contraintes de fabrication vers l'émergence de la solution produit[END_REF]. Generally, the products which are designed based on the DFM approach contains less number of parts that can be assembled more easily in a shorter time and with higher quality [START_REF] Skander | Méthode et modèle DFM pour le choix des procédés et l'intégration des contraintes de fabrication vers l'émergence de la solution produit[END_REF]. The benefits of DFM can be summarized as:



Improving the quality of new products during developing period including design, technology, manufacturing, service, etc. [START_REF] Xie | Design for manufacture and assembly[END_REF].



Cost reduction, including cost of design, technology, manufacturing, delivery, technical support, discarding, etc. [START_REF] Xie | Design for manufacture and assembly[END_REF].



Reduction in time of development for new products including time of design, manufacturing preparing, and repeatedly calculation [START_REF] Xie | Design for manufacture and assembly[END_REF].



Improving manufacturing by ensuring the quality and reliability [START_REF] Boothroyd | Product design for manufacture and assembly[END_REF], [START_REF] Venkatachalam | A knowledge-based approach to design for manufacturability[END_REF].



The manufacture participation in the upstream process [START_REF] Boothroyd | Product design for manufacture and assembly[END_REF], [START_REF] Venkatachalam | A knowledge-based approach to design for manufacturability[END_REF].



Improving the communication between the departments [START_REF] Boothroyd | Product design for manufacture and assembly[END_REF], [START_REF] Venkatachalam | A knowledge-based approach to design for manufacturability[END_REF]. DFM is considering the limitations related to the manufacturing at the early stage of the design, the design engineer must select among the deferent materials and technologies and estimate the manufacturing time and cost quantitatively and rapidly among the different schemes. They compare all kinds of the design plans and technology plans, then the design team will make revises as soon as possible at the early stage of the design period according this feedback information and determine the most satisfied design and technology plan [START_REF] Xie | Design for manufacture and assembly[END_REF].

The Systematic DFM approach involves the range of activities such as the selection of processes, materials selection and evaluation of the manufacturability of a product which is shown in Fig. 3 [4]. Fig. 3 Systematic Approach for DFM [START_REF] Skander | Méthode et modèle DFM pour le choix des procédés et l'intégration des contraintes de fabrication vers l'émergence de la solution produit[END_REF] The DFM activities are described as:  The selection of processes and materials which are the most important factors in design solution for manufacturing. It is necessary to consider the design optimization during the selection of process activities [START_REF] Skander | Méthode et modèle DFM pour le choix des procédés et l'intégration des contraintes de fabrication vers l'émergence de la solution produit[END_REF], [START_REF] Corbett | Design for manufacture: strategies, principles and techniques[END_REF].  The evaluation of manufacturability consists of analysis and evaluation of the ability to produce and design with the necessary requirements and with minimum cost and time. For this purpose, it is necessary to decompose the product to sub-elements (for example surface, dimension, tolerance, etc.) due to the manufacturing data, this evaluation consists of three step as follow:

 Verification: Manufacturability determination of products including: [START_REF] Koenig | Manufacturing Engineering: Principles for Optimization: Principles for Optimization[END_REF]  Identification the design intention and manufacturing capacities.  Accepting the compatible concepts with existed solutions, if the manufacturing is complex.  Rejecting the design plan which requires expensive changes in the production system.

 Quantification: The parameters must be quantified like cost, Time, quality [START_REF] Boothroyd | Product design for manufacture and assembly[END_REF].



Optimization: Optimization must be done based on three levels of Human (competence), means (machines, tools and software), Product (design) [START_REF] Arimoto | Development of Machining-Producibility Evaluation Method (MEM)[END_REF].

IV. STATE OF ART

A. AM in DFM

Since AM is a technology that produced the parts directly from the design stage, it seems necessary to provide an integrated framework for DFM approach to consider all manufacturing and design aspects as soon as possible in order to improve the productivity. Different researches proposed an approach named DFAM approach as a new approach, but actually DFAM is not a new approach, it must be considered that AM is a manufacturing process like others and DFAM is just applying DFM approach for AM. In one of these research, A new design for AM method is provided which supports part and specification modelling, process planning and manufacturing solution based on the process-structure-property-behavior model [START_REF] Rosen | Computer-Aided Design for Additive Manufacturing of Cellular Structures[END_REF]. In another research [START_REF] Yim | A retrieval method (DFM framework) for automated retrieval of design for additive manufacturing problems[END_REF], a framework for DFM consists of four steps of representing design requirements, determining manufacturing rules coming from the requirements, structure the problem repository, retrieving and ranking the DFM problems is provided. Another approach based on the process-structure-propertybehavior model is proposed which Unit Cell-Based Design approach is used to achieve the minimum weight, desired compliance distribution and for finding the optimal size Particle Swarm Optimization (PSO) algorithm is used [START_REF] Chu | Design for Additive Manufacturing of Cellular Structures[END_REF]. Also, It is interesting to combine AM with another traditional manufacturing process to utilize the advantages of these technologies simultaneously to provide a better solution for design and manufacturing. Therefore, for the first time an approach for DFM which combines AM with machining process in a hybrid modular vision is presented to choose the best way to obtain each module [START_REF] Kerbrat | A new DFM approach to combine machining and additive manufacturing[END_REF]. Also, it is a need to provide a knowledge-based support tool for AM and it is developed for providing a DFM approach for AM. So, they provided an environment database named Wiki for documentation and using DFAM knowledge [START_REF] Doubrovski | Optimal Design for Additive Manufacturing: Opportunities and Challenges[END_REF]. In another research, an approach is proposed which consists of global analysis for finding the geometrical dimensions due to the dimension constraints, permitting the fulfillment of the dimensional and geometrical specification due to the AM process characterization and capabilities and finishing process characteristics to determine the functional volumes and finally determining the physical and assembly requirement based on the AM process capabilities to determine the linking volumes including Functional Volume (FV) and Manufacturing Direction (MD) [START_REF] Ponche | A new global approach to design for additive manufacturing[END_REF]. In another research [START_REF] Ponche | A novel methodology of design for Additive Manufacturing applied to Additive Laser Manufacturing process[END_REF], the approach contains determining functional specification, part orientation, design area, functional optimization and manufacturing path optimization to consider manufacturing part and CAD model at the same time. Another DFM approach for AM is also provided and it is including specification analysis, preparing the initial shape, definition the parameters' set, parametric optimization and validation of the shape [START_REF] Vayre | Designing for Additive Manufacturing[END_REF]. Also, a Geometric DFX for AM is provided to considers 3D parts as input and automatically analyze all the manufacturability issues based on the predefined rules based on the machine and AM type like maximum part size, thickness, minimum feature size, rib reinforcement check, … [START_REF] Kannan | Design for additive manufacturing[END_REF]. A systematic search is presented for identifying the components of design for AM based on the criteria including integrated design, individualization, lightweight design and efficiency. These components are analyzed due to the different requirements to develop DFM in both technological and economical direction [START_REF] Klahn | Design for Additive Manufacturing -Supporting the Substitution of Components in Series Products[END_REF]. Moreover, in another one [START_REF] Salonitis | Redesign Optimization for Manufacturing Using Additive Layer Techniques[END_REF], Multi-criteria decision making approach is used to select the best solution for design which is obtained by the method including specification analysis, initial concept, interpretation of result and design evaluation. Also, a multi-level design method for AM is developed and CAD model design requirement and manufacturability are considered as inputs and topological optimization are adapted consequently to find the optimal structure in macro-level and lattice structure in meso-level [START_REF] Tang | Integration of Topological and Functional Optimization in Design for Additive Manufacturing[END_REF]. another approach [START_REF] Yang | Additive manufacturing-enabled design theory and methodology: a critical review[END_REF] is also presented which consists of design specification including analysis of initial CAD model based on the functional and performance requirements and design process including function integration, structure optimization, verifying the design solutions due to the process constraints. A Design for Rapid prototyping approach [START_REF] Boyard | A design methodology for parts using additive manufacturing[END_REF] is provided to allow the design of parts satisfying both DFA and DFM in the earliest phase of the design but this possibility will limit a priori costly late changes. A new two-dimensional approach is proposed for process chains modelling for AM to support the process selection, concurrent engineering and DFM in the early phase of design [START_REF] Thompson | Process chain modeling and selection in an additive manufacturing context[END_REF]. In order to overcome the poor dimensional accuracy, another DFM approach is also provided and mathematical formulation is used to formalize the dimensional deviations to redesign of component knowing the prediction of the obtainable dimensional deviation. The modifications are carried out in the design step to compensate the deviations to improve the accuracy and the methodology is applied to mathematical definition of the surface. For this method, there is not a need to fabricate the part and perform measurement to gain the model, it just requires to apply this method directly before CAM environment [START_REF] Boschetto | Design for manufacturing of surfaces to improve accuracy in Fused Deposition Modeling[END_REF].

According to this literature review analysis, there is a need to present a complete and integrated approach of DFM for AM. So, our objective is to provide an integrated approach to show AM process from the first step in detail in order to show and manage the new manufacturing criteria coming from AM in order to get an interoperable process with product development. This methodology must take into account AM characteristics and criteria into the design and manufacturing hybrid process of a product. Generally, our research scenario which shows AM integration in product development is illustrated in Fig. 4. As shown in Fig. 4, the first step is determining the required specification for each product due to the knowledge of the specialist as well as, the user which is performed in other researches [START_REF] Nicolás | On the generation of requirements specifications from software engineering models: A systematic literature review[END_REF], [START_REF] Wilson | Chapter 6 -Specification Preparation[END_REF]. Due to the capability of AM in customization production, the personalized products are used to produce with AM technologies due to our presented approach. Since, there is no integrated methodology to consider the criteria and characteristics coming from AM in the design and manufacturing steps simultaneously, the DFM approach must be applied in order to facilitate the design and manufacturing procedure concurrently. There are the gaps in the performed researches in proposing DFM approaches and also using hybrid solution for production which are illustrated by sad smiles. Therefore, our aim is to present a detailed and integrated approach for DFM to help designer and manufactures to find the best solutions for their process and producing the hybrid products. In the next section, the proposed DFM approach will be presented.

V. PROPOSED METHODOLOGY: APPLYING DFM APPROACH FOR AM

According to other researches [START_REF] Skander | Méthode et modèle DFM pour le choix des procédés et l'intégration des contraintes de fabrication vers l'émergence de la solution produit[END_REF], [START_REF] Skander | Design and manufacturing interface modelling for manufacturing processes selection and knowledge synthesis in design[END_REF] which were performed to present an integrated DFM approach, DFM-Skin and Skeleton approach seems helpful in providing an integrated DFM approach. Since fabrication must be considered in product definition, this approach is used to model manufacturing process based on the manufacturing database, simulation, design characteristics and product definition as well as, the software for production definition. Therefore, for presenting an integrated DFM approach for AM, DFM-skin and skeleton approach is provided. Till now, this approach was used for another Manufacturing process in previous researches [START_REF] Skander | Méthode et modèle DFM pour le choix des procédés et l'intégration des contraintes de fabrication vers l'émergence de la solution produit[END_REF], [START_REF] Skander | Design and manufacturing interface modelling for manufacturing processes selection and knowledge synthesis in design[END_REF] but in this research, it is utilized to model the AM manufacturing process, as well as, the product concurrently. As shown in Fig. 5, AM is modeled by the skin and skeleton model and these skin and skeleton are provided due to the attributes which are defined based on AM process and product definition simultaneously. This model must be integrated into the interface model to provide a model that considers all the manufacturing and product attributes at the same time and this integration is useful to present the product solutions and process alternatives due to the manufacturing constraints. In the rest, this approach is described in the detail. The interface model which can be considered as an output for this approach represents the minimum information required and it supports the synthesis of both design and manufacturing attributes. This approach provides a data exchange between the usage and technological database like functional data and technological selection as well as, process and manufacturing database. Based on [START_REF] Skander | Méthode et modèle DFM pour le choix des procédés et l'intégration des contraintes de fabrication vers l'émergence de la solution produit[END_REF], [START_REF] Skander | Design and manufacturing interface modelling for manufacturing processes selection and knowledge synthesis in design[END_REF], [START_REF] Roucoules | Manufacturing process selection and integration in product design. Analysis and synthesis approaches[END_REF], two types of interface model including usage and manufacturing interface model are presented. The usage interface model which shows the requirement and manufacturing one is a kind of manufacturing information model.

In our approach, skin and skeleton model are utilized to model AM. According to the [START_REF] Skander | Méthode et modèle DFM pour le choix des procédés et l'intégration des contraintes de fabrication vers l'émergence de la solution produit[END_REF], [START_REF] Skander | Design and manufacturing interface modelling for manufacturing processes selection and knowledge synthesis in design[END_REF], [START_REF] Roucoules | Manufacturing process selection and integration in product design. Analysis and synthesis approaches[END_REF] skin used to describe the functional surface of product and skeleton are for presenting the flow trajectory. Moreover, two kinds of the skin and skeleton features are existed: Usage features and Manufacturing features. These features are described as:  Usage skin: usage skin is the surface which energetic flows circulate through it. For defining the usage skin, functional surfaces resulted from technological components selections are used. This skin supports the geometric attributes and tolerance.  Usage skeleton: this skeleton is a kind of energetic flow (mechanical, electrical, magnetic, etc.) which circulates in the product.  Manufacturing skeleton: This skeleton is flow trajectory processed during manufacturing. It is assumed that every manufacturing process is based on a material flow.  Manufacturing Skin: This skin is the surfaces which are generated by manufacturing process. These features are partly issued from manufacturing skeleton by a sweeping operation. Specific set of attributes are associated with skin and skeleton features. Skin attributes are including the shape, tolerance, and roughness as well as, material direction. For the skeleton attributes, initial section form, final section form, section variation and neutral fiber can be mentioned. In addition to these attributes, an extra attribute for material flow direction such as removal, addition or deformation are presented for manufacturing skeletons.

In summary, the proposed approach consists of Providing Design Requirement, Functional structural model, Skin and Skeleton Model, Selecting alternative solutions. In order to use this approach for AM technologies, this approach must be adapted to the AM attributes. For creating the usage Skin, the topological optimization is used to create and design usage skin that, in addition to complying specific mechanical performance, should be less expensive [START_REF] Gardan | Knowledge Management for Topological Optimization Integration in Additive Manufacturing, Knowledge Management for Topological Optimization Integration in Additive Manufacturing[END_REF], [START_REF] Gardan | Topological optimization of internal patterns and support in additive manufacturing[END_REF] by optimizing the structure and amounts of the material rather than primary model. The usage skeleton can be defined by the surface that material flows through it. Since, AM is a method that produces the parts by adding the material in layers, the material sense is considered as adding.

The manufacturing skin is defined by the CAD model which is converted to the suitable format like STL, STEP due to the utilizable machine. Since, the AM process generates the complex forms in once time, the manufacturing skin is integrated in one part as a complex form.

The manufacturing skeleton is created by determining the manufacturing parameters like part orientation, slicing strategy, nuzzle speed and diameter, road width, raster angle, air gap, raft and support material characteristics, infill density, temperature and post-processing, etc. Different alternative solutions for manufacturing are provided by choosing the different values for these parameters and different technologies can be presented for manufacturing technologies. The attribute values must be determined for the appropriate technologies to present a best way to produce the part.

Overall, the main benefit which encouraged us to use these modelling concepts is to take into account the manufacturing knowledge very early in product definition.

VI. CONCLUSION AND FUTURE WORKS

In this article, an integrated approach is provided for AM based on the DFM approach to apply Concurrent Engineering aspects through several AM technologies. DFM approach is used to consider the manufacturing constraints in the design stage as soon as possible. DFM skin and skeleton is rather used for modeling (or representing) AM process in the earlier step of product development. It helps to create a geometry model by integration the design and manufacturing attributes simultaneously. This approach is including skin and skeleton of usage for providing the desired part based on the design attributes and also manufacturing skin and skeleton based on the manufacturing attributes and constraints. It provides a data exchange between the usage and technological database like functional data and technological selection as well as, process and manufacturing database. According to these databases, the interface model is created to support both of the design and manufacturing attributes concurrently in order to define a product. Based on this method, different alternative solutions for design and manufacturing are provided and DFM specialist must select the best solution according to the desired specification.

For the future work, to show the capability of this approach, it will be applied on the Hook bag. For this purpose, the manufacturing and usage skin and skeleton are provided based on the DFM and Concurrent Engineering aspects. The experimental methods and Mathematical model and metaheuristic algorithms as decision making tools can be used to provide the best solution for design and manufacturing processes.

Fig. 1

 1 Fig.1Integrated Design[START_REF] Skander | Méthode et modèle DFM pour le choix des procédés et l'intégration des contraintes de fabrication vers l'émergence de la solution produit[END_REF]

Fig. 4

 4 Fig. 4 Research Scenario

Fig. 5

 5 Fig. 5 Proposed DFM-Skin and Skeleton approach for AM