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Taking into account thermal residual stresses in topology optimization of structures built by additive
manufacturing
G. Allaire, L Jakabčin
To cite this version:
G. Allaire, L Jakabčin. Taking into account thermal residual stresses in topology optimization of structures built by additive manufacturing. 2017. �hal-01666081�
Taking into account thermal residual stresses in topology optimization of structures built by additive manufacturing
G. Allaire and L. Jakabčin
Centre de Mathématiques Appliquées, UMR CNRS 7641
École Polytechnique, Université Paris-Saclay, 91128 Palaiseau, France.
Email: gregoire.allaire@polytechnique.fr, lukas.jakabcin@polytechnique.edu December 12, 2017
Abstract
We introduce a model and several constraints for shape and topology optimization of structures, built by additive manufacturing techniques. The goal of these constraints is to take into account the thermal residual stresses or the thermal deformations, generated by processes like Selective Laser Melting, right from the beginning of the structural design optimization. In other words, the structure is optimized concurrently for its final use and for its behavior during the layer by layer production process. It is well known that metallic additive manufacturing generates very high temperatures and heat fluxes, which in turn yield thermal deformations that may prevent the coating of a new powder layer, or thermal residual stresses that may hinder the mechanical properties of the final design. Our proposed constraints are targeted to avoid these undesired effects. Shape derivatives are computed by an adjoint method and are incorporated into a level set numerical optimization algorithm.
Several 2-d and 3-d numerical examples demonstrate the interest and effectiveness of our approach.
1 Introduction
Topology optimization methods have long suffered from a drawback, inherent to its main ad- vantage: the computed optimal structures have so complex topologies that they are, very often, impossible to build by traditional techniques like casting. This has boosted many research works on the addition of manufacturing constraints during the optimization, see e.g. [4], [5], [17], [18], [19], [21], [37], [44]. The picture is completely different with the emergence and maturity of additive manufacturing technologies, which are able to build structures with a high degree of complexity, thereby allowing to process almost directly the designs predicted by shape and topology optimization algorithms [8], [16]. Although, in principle, additive manufacturing can build any kind of shapes, there remain some explicit or implicit constraints on their geometries and topologies. Let us quote a few of them: overhang limitations [2], [3], [11], [26], [27], alloy anisotropy [40], [45], orientation with respect to the build direction [28], no closed holes (in order to remove the powder inside), and finally thermal residual stresses or thermal deformations.
Here we focus on this last type of constraints which is typically associated with metallic powder bed additive manufacturing processes. Indeed, thermal residual stress is one of the main undesired effect that results from metallic additive manufacturing processes. They are generated by the large heat fluxes, and high temperatures, brought to the metallic powder by
the laser or electron beam. They can yield large deformations of the built structure, when it is detached from its support, or even cause irreversible damage, fracture or delamination of the successive layers. Therefore, the prediction of thermal residual stress and its possible avoidance are key technological issues. There are already many works, either experimental or numerical, devoted to the study of thermal residual stress [15], [20], [23], [25], [30], [35], [36], [42], [43]. The numerical studies are based either on detailed models, which are very complex and computationally expensive since they are typically multi-physics and multi-scales, or on simplified models, which are relevant at a more macroscopic scale, thanks to some homogenization process.
However, to the best of our knowledge, none of these models, even the simplest ones, have been yet incorporated into a shape and topology optimization loop. Our goal is thus to propose a model for the appearance of thermal residual stress and to suggest several objective functions or constraints to take them into account in shape and topology optimization of structures.
Since this model of thermal residual stress is going to be repeatedly called into an optimization loop, in order to avoid too expensive computational costs, we rely on a simplified modeling which, in particular, ignores details of phase change, annealing effects, any nonlinearities in heat exchange and mechanical irreversibility. Yet, it features some key issues: the presence of two phases, powder and solid, the layer by layer building process, time evolution of heat propagation following the beam path and thermo-mechanical response of the structure under construction.
To this model are associated several possible constraints for the optimization. We focus on two different choices. A first constraint is an integral, in time and space, of the stress created during the building process, which should remain below some predefined threshold. A second constraint is linked to the vertical displacement of the upper boundary of each intermediate structures which must stay below a maximal gap distance so that the rake or roller (which coats a new powder layer on top of the previous intermediate structure) can advance without being stopped by the thermally deformed structure, acting as an obstacle.
The proposed model and constraints are related to the behavior of the structure during its building process. Of course, the final use of the structure has to be incorporated in the optimization too. Therefore, our shape and topology optimization problem features two state equations: one for the final use of the structure (typically, linearized elasticity and compliance as an objective function), another one for the sequence of intermediate structures during the building process (our thermal residual stress model). Of course, this has the consequence of making the optimization problem much more costly than in the absence of thermal residual stress constraints, which in turn explains our choice of a simplified model.
The content of the present paper is the following. In Section 2 our model of thermo-elastic evolution is described in details. To simplify the exposition, we first ignore the layer by layer building process. In other words, our model is described in the final configuration of the build chamber which is filled by two different materials or phases: powder and solid. Our model is made of two partial differential equations: the time dependent heat equation, coupled to the quasi-static thermoelastic equilibrium system. It is inspired by a model proposed in [42] (see also [30]), except that, for simplicity, plastic irreversible deformations are neglected and not taken into account (we plan to come back to the original model of [42] in a further work). An objective function for our simplified model depends on the position of the interface between the two phases, powder and solid. The corresponding adjoint problems are introduced which allow us to compute the shape derivative of this objective function (based on previous works [1], [7]). The main difficulties are twofold. First, it is an interface between two subdomains, and not the exterior boundary, which is optimized. Second, the adjoint problem for the heat equation is backward in time, which makes the numerical computation of the shape derivative quite expensive. Our main result is Theorem 2.4 which delivers the shape derivative of a generic objective function (in the simplified setting of a spatially discretized problem). Theorem 2.5
gives a similar result in a discrete time setting.
Section 3 introduces the additional difficulty of the layer by layer building process. We follow the approach of [2], [3] to model the layer by layer construction. If the final solid structure is called Ω, there are a sequence of intermediate structures Ω(ti) corresponding to an increasing sequence of discrete timesti, each corresponding to the addition of a new layer in the building process. Of course, in numerical practice several layers (which are very thin, typically of the order of 50micrometers) can be lumped together in order to have a smaller number of intermediate structures to analyse. Each Ω(ti) is simply the truncation ofΩ at the height (in the building direction) attained at timeti. Therefore, the only variable in the optimisation problem is the final structureΩ, although the objective function and the constraints depend on state equations posed both inΩand inΩ(ti). Our main result is Theorem 3.1 which furnishes the shape derivative of the objective function for this multiple loads optimization problem. In a discrete time setting a variant of this result is Theorem 3.2.
In Section 4 details are given about our numerical algorithm. The level set method is briefly recalled, as well as the regularization and extension process for the velocity in the Hamilton- Jacobi equation which is deduced from the shape derivative. The regularization of the interface between the solid and powder, proposed in [1], is also explained.
Finally, Section 5 is devoted to numerical results for a MBB beam in 2-d and 3-d. We consider two objective functions. The first one is the L2-norm in space and time of the Von Mises stress (i.e. its deviatoric part) which is our proposed measure of thermal residual stresses in our simplified modeling. The second one is a measure of the vertical displacement above a given threshold in the upper layer of all intermediate shapes during the building process. We perform test cases for the simplified model of Section 2, as well as for the layer by layer model of Section 3. Since the computational time is quite large (compared to a standard compliance minimization problem), we always choose as an initialization a structure which has already been optimized for its final use, without taking into account the additive manufacturing process.
We minimize one of the two above objective functions, keeping the volume fixed and with an inequality constraint on the compliance which must not increase too much. Our minimization algorithm works well in the sense that the objective functions are substantially reduced (say by almost 50% in many cases) while the constraints are satisfied at convergence. The optimized shapes are not very different from the initial ones (in particular, they share the same topology) and it is not intuitive to understand how the geometry changes influenced the decrease of the objective functions. Future work will be devoted to the exploration of other objective functions, trying other initializations (which may be very costly) and extending our layer by layer model to take into account plastic deformations.
2 A thermo-elastic optimization problem
The goal of this section is to introduce a simpler model than the complete one of the next Section 3 which, in particular, does not feature the layer by layer additive manufacturing process. However, the equations are the same. Therefore, this allows us to separate the difficulties and to simplify the exposition. This model is made of the coupling of the heat equation with a quasi-static thermo-elastic system. It is thus a simplified model since it ignores plastic deformations, as in [42], or the melt pool physics (including phase changes and annealing effects), as in [30]. This (possibly exaggerated) simplification is motivated by our requirement of limited computational cost in an optimization loop. Further, we present the various shape derivatives and required adjoint systems without taking into account the varying domain and material properties which will be introduced in Section 3.
2.1 A thermo-elastic evolution model
Throughout the paper,Dis a bounded connected open set inRn (typically, the build chamber) with Lipschitz boundary∂D=∂DD∪∂DN∪∂DF where∂DD, ∂DN, ∂DF are disjoint relatively open sets in∂Dwith the (surface) measure of∂DDassumed to be positive. We denoteHD1(D) :=
{u∈H1(D);u= 0on∂DD}.
We suppose that D = Ω1∪Ω2 where Ω1 and Ω2 are relatively open sets in D (typically, they correspond to the solid part and to the powder part). The interface between these two subsets isΓ =∂Ω1∩∂Ω2 (see Figure 1). The subdomainsΩ1 andΩ2 are filled by two different thermoelastic materials. All the mechanical parameters ξ of our model are constant in each subdomain, in other words are considered under the form
ξ(x) :=ξ1χΩ1(x) +ξ2(1−χΩ1(x)), (1) whereχΩi is the characteristic function of the setsΩi (equal to1 ifx∈Ωi and to0 otherwise) andξ1,ξ2 are the mechanical parameters defined onΩ1 andΩ2.
Ω1
Γ
D Ω2
Figure 1: A domainDwith two sub-domains separated by an interface Γ.
The mechanical unknowns of our model are the displacement field u(t, x) : [0, tf]× D →Rn, and the temperature fieldT(t, x) : [0, tf]× D →R. They follow a thermo-elastic time-evolution model, defined by the following equations.
• Heat equation:
ρ∂T
∂t −div(λ∇T) =Q in(0, tf)× D, (λ∇T)·n=−β(T−Tinit) on(0, tf)×∂DF, T =Tinit on(0, tf)×∂DD, (λ∇T)·n=g on(0, tf)×∂DN, T(0) =Tinit inD,
(2)
where λ > 0 is the thermal conductivity coefficient, ρ > 0 the product of the material density by the specific heat andβ >0the heat transfert coefficient. A Neumann boundary condition g = g1χΩ1 +g2(1−χΩ1) with g1, g2 ∈ L2((0, tf), H1(D)) is imposed on ∂DN
and a thermal body source Q=Q1χΩ1 +Q2(1−χΩ1)with Q1, Q2 ∈L2((0, tf), H1(D)) is considered in D. The initial and reference temperature is Tinit ∈ C([0, tf], L2(D))∩ L2([0, tf], H1(D)).
• Thermoelastic equilibrium system:
−div(σ) =f in(0, tf)× D, σ=σel+σth in(0, tf)× D,
σel =Ae(u) σth:=K(T−Tinit)In, σ·n= 0 on(0, tf)×∂DN ∪∂DF, u= 0 on(0, tf)×∂DD,
(3)
where σ is the Cauchy stress tensor, decomposed as the sum of the elastic stressσel and the thermal stressσth,Ais the fourth-order elasticity tensor of an isotropic material with Young modulus E > 0 and Poisson ratio −1 < ν <1/2, In the identity matrix, α the thermal expansion coefficient and
K=− Eα 1−2ν.
Recall that, under the usual assumptions of small displacements and small deformations, the strain tensor e(u) is defined by e(u) = 12(∇u+ (∇u)T). The body forces (typ- ically gravity) are f = f1χΩ1 +f2(1 −χΩ1) with f1, f2 ∈ L2((0, tf), H1(D)n). The associated variational formulations to the problems (2) and (3) are as follows. Find T ∈C([0, tf];L2(D))∩L2((0, tf);H1(D))such thatT(0) =Tinit and for a.e. 0< t < tf, T(t) =Tinit on∂DD and for anyϕ∈HD1(D)
d dt
Z
D
ρT(t)ϕ dx+
Z
D
λ∇T(t)·∇ϕ dx+
Z
∂DF
β(T(t)−Tinit)ϕ ds= Z
D
Q(t)ϕ dx+
Z
∂DN
g(t)ϕ ds.
Findu∈L2((0, tf);HD1(D)n)such that for a.e. t∈(0, tf), for anyϕ∈HD1(D)n Z
D
Ae(u(t)) +K(T(t)−Tinit)In
:e(ϕ)dx= Z
D
f(t)·ϕ dx , where the symbol:denotes the full contraction (or scalar product) of square matrices.
Note that problem (2) can be solved independently of (3), and that problem (3) depends on the temperature field T, solution of (2). Under our assumptions, it is well known [29] that (2) admits a unique solution T ∈C([0, tf];L2(D))∩L2((0, tf);H1(D)). This implies that divσth is a forcing term that belong to the dual space ofL2((0, tf);HD1(D)n). Then, in turn (3) admits a unique solutionu∈L2((0, tf);HD1(D)n).
Remark 2.1 It is only for the simplicity of exposition that Dirichlet boundary conditions are applied for both the temperatureT and the displacementuon the same part∂DDof the boundary.
2.2 The optimization problem
In this section we optimize the position of the interface Γ between two linear thermoelastic materials. EitherΓis a smooth surface without boundary, included in the interior ofD, orΓis a smooth surface with a boundary which lies on the boundary of the domain, ∂Γ ⊂∂D. The interfaceΓis the only optimisation variable ; the boundary ∂Dand its subsets are not allowed to move.
Clearly, this is a simplification of the true optimization problem, described in the next section.
We consider the following objective function J(Γ) =
Z tf 0
Z
D
j(u(Γ))dx dt+ Z tf
0
Z
∂D
l(u(Γ))ds dt, (4)
wherej andl are two smooth functions with quadratic growth, andu(Γ)is the elastic displace- ment solution of the model (2) and (3). Of course, (4) is just an example and many more choices are possible, for example, depending on the gradient∇uor on the temperatureT too ,
J(Γ) = Z tf
0
Z
D
j(u(Γ),∇u(Γ), T(Γ))dx dt+ Z tf
0
Z
∂D
l(u(Γ), T(Γ))ds dt, (5) or involving also an integral on the domain evaluated at the final timetf. Our approach easily extends to such cases. Associated to the objective function (4) we introduce the following adjoint systems
−div(Ae(η)) =−j0(u) in(0, tf)× D,
(Ae(η))·n=−l0(u) on(0, tf)×∂DF∪∂DN, η= 0 on(0, tf)×∂DD,
(6) and
ρ∂p
∂t +div(λ∇p) =Kdiv(η) in(0, tf)× D, (λ∇p)·n=−βp on(0, tf)×∂DF, (λ∇p)·n= 0 on(0, tf)×∂DN,
p= 0 on(0, tf)×∂DD,
p(tf) = 0 inD.
(7)
The variational formulations associated to the adjoint problems (6) and (7) are as follows. Find p∈C([0, tf];L2(D))∩L2((0, tf);HD1(D))such that p(tf) = 0and for a.e. 0< t < tf, for any ϕ∈HD1(D)
d dt
Z
D
ρp(t)ϕ dx− Z
D
λ∇p(t)· ∇ϕ dx− Z
∂DF
βp(t)ϕ ds= Z
D
Kdiv(η(t))ϕ dx . (8) Findη ∈L2((0, tf);HD1(D)n)such that for a.e. t∈(0, tf), for anyϕ∈HD1(D)n
Z
D
Ae(η(t)) :e(ϕ)dx=− Z
D
j0(u(t))·ϕ dx− Z
∂DN∪∂DF
l0(u(t))·ϕ ds. (9) Note that the coupling of (6) and (7) is the inverse of that of (2) and (3) in the sense that, here, one solves first the adjoint elasticity system (6) before solving in a second step the backward heat equation (7) with a final condition. It is well known that (6) admits a unique solution η ∈ L2((0, tf);HD1(D)n) (time is just a parameter, playing no role), which implies that the source term Kdiv(η)in (7) belongs to the dual space ofL2((0, tf);HD1(D)). Then, in turn (7) admits a unique solutionT∈C([0, tf];L2(D))∩L2((0, tf);H1(D)).
Although the notion of shape derivative is only introduced in the following section, let us say a few words about a classical difficulty in computing the shape derivative of the objective function (4) in the present setting. As explained in [1], [7], [10], [24], [33], the differentiation of the objective functionJ(Γ)with respect to the position of the interfaceΓis a delicate topic for two reasons. First, the solution (u, T) of the system (2) and (3) is not shape differentiable, in the usual sense, because the solution gradient has jumps through the interface. Second, even if this difficulty can be circumvented (by differentiating separately in the subdomainsΩ1 andΩ2), the shape derivative features jumps of the solution gradient at the interface, which are always difficult to compute when using standard numerical methods on a fixed mesh where the interface is captured and not tracked. Therefore, it is much more convenient and numerically efficient to compute the shape derivative of a discrete version of this problem, as we now explain.
More precisely, in the rest of this section we replace the exact solution(u, T, p, η)of systems (2), (3), (6) and (7) by its discret analogue(uh, Th, ph, ηh), solution of the following variational
formulation (or rather semi-discrete because the time variable is not yet discretized). We intro- duce a finite-dimensional subspace Vh of H1(D), which is independent of the interface Γ. For example,Vh could be the finite-dimensional space ofP1 Lagrange finite element associated to a given simplicial mesh Dh (with elements of maximal sizeh). We also defined its subsetVh0 of all functions vanishing on the Dirichlet part of the boundary
Vh0=n
v∈Vhsuch thatv= 0on∂DD
o.
The discrete variational formulations are: findTh∈C([0, tf];Vh0)such that Th(0) =Tinit and for0< t < tf, for anyϕh∈Vh0
d dt
Z
D
ρTh(t)ϕhdx+ Z
D
λ∇Th(t)· ∇ϕhdx + Z
∂DF
β(Th(t)−Tinit)ϕhds
= Z
D
Q(t)ϕhdx+ Z
∂DN
g(t)ϕhds.
(10)
Finduh∈L2((0, tf);Vh0n)such that for a.e. t∈(0, tf), for anyϕh∈Vh0n Z
D
Ae(uh(t)) +K(Th(t)−Tinit)In
:e(ϕh)dx= Z
D
f(t)·ϕhdx. (11) Findηh∈L2((0, tf);Vh0n)such that fort∈(0, tf), for anyϕh∈Vh0n
Z
D
Ae(ηh(t)) :e(ϕh)dx=− Z
D
j0(uh(t))·ϕhdx− Z
∂DN∪∂DF
l0(uh(t))·ϕhds, (12) Findph∈C([0, tf];Vh0)such thatph(tf) = 0and for a.e. 0< t < tf, for anyϕh∈Vh0
d dt
Z
D
ρph(t)ϕhdx− Z
D
λ∇ph(t)· ∇ϕhdx= Z
D
Kdiv(ηh(t))ϕhdx+ Z
∂DF
βph(t)ϕhds . (13) The key point is that, under a mild assumption on the interfaceΓ, the discrete solution(uh, Th) is now shape differentiable in the usual sense (see [1] for details).
Lemma 2.1 ([1]) Assume that the interfaceΓ generically cuts the mesh Dh, namely that it is never aligned with part of a face of any cell of the mesh. Let θ∈W1,∞(D,D)be a displacement field for the interface Γsuch that θ·n= 0on ∂D. Then, the discrete solution(uh, Th) of (10) and (11) is differentiable with respect to the displacementθ of the interfaceΓ.
Because of the differentiability property of Lemma 2.1 and the deduced simplified formulas for shape derivatives, in the sequel, we shall always rely on the following assumption.
Assumption 2.2 In the present paper, we always replace the exact (continuous) solutions of the state and adjoint equations by their discrete approximations in finite dimensional subspaces.
Furthermore, we assume that the interface Γ generically cuts the mesh Dh, namely that it is never aligned with part of a face of any cell of the mesh. Therefore, these discrete solutions are shape differentiable. Eventually, for simplicity in the presentation, we shall abuse notations and drop the superscript hwhen considering these discrete solutions.
2.3 Shape derivative
The setting of the previous section is slightly different from the classical framework of shape differentiation [12], [31], [34], [39], since it is the interface between two subdomains, rather than the exterior boundary of a single domain, which is the optimization variable. The main idea of the Hadamard method of shape differentiation is to parametrize the deformations of the interface Γ of the subdomains Ω1,Ω2 by a vector field θ from D into D, and then to differentiate with respect to θ. In other words, for any θ∈W1,∞(D,D)such that θ·n= 0 on∂D(the working domainDis fixed and not allowed to change withθ), define
Γθ= ( Id +θ)Γ and Ω1,θ= ( Id +θ)Ω1,
then shape derivatives are defined as derivatives with respect toθat the origin. To establish the shape derivative for the objective function (4), the following standard lemma is needed.
Lemma 2.3 Let Ω1be a smooth bounded open set included inDandθ∈W1,∞(D,D)such that θ·n= 0 on∂D. Letf ∈H1(D)andg ∈H2(D)be two given functions. Let Γ be the interface between Ω1 andΩ2=D \Ω1 with a boundary∂Γ = Γ∩∂D. The shape derivatives of
J1(Γ) = Z
Ω1
f dx and J2(Γ) = Z
Γ
g ds are
J10(Γ)(θ) = Z
Γ
f θ·n ds and J20(Γ)(θ) = Z
Γ
∂g
∂n+gH
θ·n ds+ Z
∂Γ
g θ·τ dL, (14) respectively, where nis the exterior unit vector normal toΩ1,H is the mean curvature,τ is the unit vector tangent toΓand normal to∂Γ, anddLis the(n−2)-dimensional measure along∂Γ.
Remark 2.2 Be careful that, by a slight abuse of notations, the same letter n is used for the unit exterior normal to D, when we assume that θ·n = 0 on ∂D, and for the unit exterior normal toΩ1 in the formula (14) for the shape derivatives. Note that, in (14), the termθ·τ on
∂Γ = Γ∩∂D is equal toθt·τ whereθt =θ−(θ·n)n is the tangential part of θ on ∂D, since θ·n= 0 on∂D.
Then, our main result is the computation of the shape derivative of the functional J(Γ), defined by (4). Recall that, by Assumption 2.2, we always consider discrete solutions of the direct and adjoint problems, instead of the true continuous ones.
Theorem 2.4 Letθ∈W1,∞(D,D)be a displacement field for the interfaceΓsuch thatθ·n= 0 on∂D. Under Assumption 2.2 the shape derivative of J(Γ)is
J0(Γ)(θ) =
Z tf 0
Z
Γ
(ρ1−ρ2)∂T
∂tp+ (λ1−λ2)∇T· ∇p−(Q1−Q2)p
θ·n ds dt
+ Z tf
0
Z
Γ
n(A1−A2)e(u) + (K1−K2)(T−Tinit)In
:e(η)−(f1−f2)·ηo
θ·n ds dt
+ Z tf
0
Z
∂DF∩∂Γ
(β1−β2)(T−Tinit)p θ·τ dL dt− Z tf
0
Z
∂DN∩∂Γ
(g1−g2)p θ·τ dL dt, whereη andpare the (discrete) solutions of the adjoint problems (12) and (13),nis the normal unit vector toΓ (oriented from Ω1 toΩ2), andτ is the unit vector tangent to Γand normal to
∂Γ, anddLis the(n−2)-dimensional measure along ∂Γ.
Proof Following the Lagrangian method of Céa [12], we introduce a Lagrangian depending on the interfaceΓ and functionsT ,ˆ pˆ∈C([0, tf], Vh), and ˆu,ηˆ∈L2([0, tf], Vh0n)
L(Γ,u,ˆ η,ˆ T ,ˆ p)ˆ = Z tf
0
Z
D
j(ˆu)dx dt+ Z tf
0
Z
∂D
l(ˆu)ds dt+ Z tf
0
Z
D
( ρ∂Tˆ
∂tpˆ+λ∇Tˆ· ∇ˆp−Qˆp )
dx dt
+ Z tf
0
Z
∂DF
β( ˆT−Tinit)ˆp ds dt− Z tf
0
Z
∂DD
n(λ∇Tˆ)·npˆ+ (λ∇ˆp)·n( ˆT−Tinit)o ds dt
− Z tf
0
Z
∂DN
gp ds dtˆ + Z
D
ρ( ˆT(0)−Tinit)ˆp(0)dx +
Z tf 0
Z
D
n(Ae(ˆu) +K( ˆT−Tinit)In) :e(ˆη)−f ·ηˆo dx dt.
Differentiating L with respect to ηˆ in the direction of a test function ϕ ∈L2([0, tf], Vh0n), and making this derivative equal to zero at the optimal point(T, u, η, p)we obtain
Z tf
0
Z
D
n(Ae(u) +K(T−Tinit)In) :e(ϕ)−f·ϕo
dx dt= 0,
which is nothing else than the (discrete) variational formulation of the thermo-elastic system (3). Similarly, the partial derivative of L with respect topˆin the direction of a test function ϕ∈C([0, tf], Vh), equal to zero at the optimal point(T, u, η, p), gives
Z tf
0
Z
D
ρ∂T
∂tϕ+λ∇T· ∇ϕ−Qϕ
dx dt+ Z tf
0
Z
∂DF
β(T−Tinit)ϕ ds dt
− Z tf
0
Z
∂DD
n(λ∇T)·nϕ+ (λ∇ϕ)·n(T −Tinit)o ds dt
− Z tf
0
Z
∂DN
gϕ ds dt+ Z
D
ρ(T(0)−Tinit)ϕ(0)dx= 0. (15)
First, restricting ourselves to compactly supported test functionsφ∈Cc∞(D ×(0, tf)), (15) yields the heat equation in (2). Second, taking into account this heat equation, after integration by parts for a general test function, (15) reduces to
Z tf 0
Z
∂DF
(λ∇T)·nϕ+β(T−Tinit)ϕ ds dt− Z tf
0
Z
∂DD
(λ∇ϕ)·n(T−Tinit)ds dt +
Z tf 0
Z
∂DN
((λ∇T)·n−g)ϕ ds dt+ Z
D
ρ(T(0)−Tinit)ϕ(0)dx= 0.
Varying ϕ leads to the initial condition T(0) = Tinit in D, as well as the various boundary conditions.
To find the adjoint systems, we now differentiate Lwith respect to the primal displacement variableuˆ in the direction of a test functionϕ∈L2([0, tf], Vh0n)to obtain at optimality
Z tf 0
Z
D
j0(u)·ϕ dx dt+ Z tf
0
Z
∂D
l0(u)·ϕ ds dt+ Z tf
0
Z
D
Ae(ϕ) :e(η)dx dt= 0,
which is nothing else than the (discrete) variational formulation (12) of the thermo-elastic adjoint system. Then, we differentiate L with respect to the primal temperature variable Tˆ in the
direction of a test functionϕ∈C([0, tf], Vh)which, at optimality, leads to Z tf
0
Z
D
ρ∂ϕ
∂tp+λ∇ϕ· ∇p+Kdiv(η)ϕ
dx dt+ Z tf
0
Z
∂DF
βϕp ds dt
− Z tf
0
Z
∂DD
n(λ∇ϕ)·np+ (λ∇p)·nϕo ds dt+
Z
D
ρϕ(0)p(0)dx= 0.
An integration by parts, both in time and space variables, leads to Z
D
ρϕ(tf)p(tf)dx− Z tf
0
Z
D
ρ∂p
∂t +div(λ∇p)−Kdiv(η)
ϕ dx dt
− Z tf
0
Z
∂DD
(λ∇ϕ)·np ds dt+ Z tf
0
Z
∂DF
{(λ∇p)·n+βp}ϕ ds dt (16) +
Z tf 0
Z
∂DN
(λ∇p)·nϕ ds dt= 0.
Varying the test functionϕin (16) leads to the thermal adjoint equation (7), its final condition attf and boundary conditions.
It remains to compute the shape derivative. By Lemma 2.1, under Assumption 2.2 that the interfaceΓis not aligned with part of a face of any cell of the mesh, the discrete solution(u, T) (omittingh) is shape differentiable in the usual sense. Then, it is a classical result [6], [12] that the shape derivative of the objective functionJ is just the partial derivative of the LagrangianL
J0(Γ)(θ) =∂L
∂Γ(Γ, u, T, η, p)(θ).
Thus, we apply Lemma 2.3 to the Lagrangian. The normal vector toΩ1isn1, the normal vector toΩ2isn2andn1=−n2. Recall that onlyΓis free to move. We start by differentiating volume integrals. Sinceuis continuous through the interfaceΓ, the following derivative cancels
∂
∂Γ Z tf
0
Z
D
j(u)dx dt
(θ) = ∂
∂Γ Z tf
0
Z
Ω1
j(u)dx dt
(θ) + ∂
∂Γ Z tf
0
Z
Ω2
j(u)dx dt
(θ)
= Z tf
0
Z
Γ
j(u)θ·n1ds dt+ Z tf
0
Z
Γ
j(u)θ·n2ds dt = 0.
Other volumic terms in the Lagrangian have non-zero derivatives since the material coefficients are discontinuous at the interface. For example,
∂
∂Γ Z tf
0
Z
D
Ae(u) :e(η)dx dt
(θ) = ∂
∂Γ Z tf
0
Z
Ω1
A1e(u) :e(η)dx dt
(θ) + ∂
∂Γ Z tf
0
Z
Ω2
A2e(u) :e(η)dx dt
(θ)
= Z tf
0
Z
Γ
θ·n1(A1−A2)e(u) :e(η)ds dt.
In the above result we strongly rely on Assumption 2.2 that(u, T, η, p)are the discrete solutions which thus have no discontinuity, as well as their derivatives, through the interfaceΓ.
We now turn to surface integrals. According to Lemma 2.3,τ1=−τ2, whereτ1 is the normal vector to ∂Γ and n1. Since u and its derivative are continuous through the interface Γ, the
following derivative cancels
∂
∂Γ Z tf
0
Z
∂D
l(u)ds dt
(θ) = ∂
∂Γ Z tf
0
Z
∂Ω1∩∂D
l(u)ds dt
(θ) + ∂
∂Γ Z tf
0
Z
∂Ω2∩∂D
l(u)ds dt
(θ)
= Z tf
0
Z
∂Γ
∂l(u)
∂n +l(u)H
θ·n1ds+ Z tf
0
Z
∂Γ
l(u)θ·τ1dL
+ Z tf
0
Z
∂Γ
∂l(u)
∂n +l(u)H
θ·n2ds+ Z tf
0
Z
∂Γ
l(u)θ·τ2dL = 0.
In the same way, we treat all surface integrals and the only non-zero terms come from the presence of discontinuous coefficients. For example,
∂
∂Γ Z tf
0
Z
∂DF
β(T−Tinit)p ds dt
(θ) = ∂
∂Γ Z tf
0
Z
∂Ω1∩∂DF
β1(T−Tinit)p ds dt
(θ)
+ ∂
∂Γ Z tf
0
Z
∂Ω2∩∂DF
β2(T−Tinit)p ds dt
(θ)
= Z tf
0
Z
∂Γ∩∂DF
(β1−β2)(T−Tinit)pθ·τ1dL . Note that the term in the Lagrangian, corresponding to the initial condition, gives no contribution to the shape derivative because, indeed, the temperatureT satisfies this initial condition. This
yields the desired result.
2.4 A time discretization procedure
In the previous section, in order to simplify the calculus of the shape derivative, discrete solu- tions (in space) were considered. Now, for computational reasons, the solutions should also be discretized in time. The goal of the present section is thus to give a consistent time discretization of the direct and adjoint solutions, of the objective function and its shape derivative. The time interval(0, tf) is discretized inN subintervals (tj−1, tj)of equal length ∆t=tj−tj−1=tf/N such that
0 =t0< t1< ... < tj< ... < tN =tf.
The direct problem (2) and (3) is discretized by a backward (implicit) Euler scheme with respect to the time variable. For1 ≤j ≤N, on each subinterval (tj−1, tj) the discrete time evolution (uj, Tj)is defined as the solution of the following system of equations. The initial temperature isTj=0=T0=Tinit in D.
• Heat equation:
ρTj−Tj−1
∆t −div(λ∇Tj) =Qj inD, (λ∇Tj)·n=−β(Tj−Tinit) on∂DF,
Tj =Tinit on∂DD,
(λ∇Tj)·n=gj on∂DN.
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• Thermoelastic equilibrium system:
−div(σj) =fj in D, σj =σelj +σjth in D,
σjel=Ae(uj) σthj :=K(Tj−Tinit)In, σj·n= 0 on∂DN ∪∂DF, uj = 0 on∂DD.
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The discrete time objective function is given by a simple piecewise constant interpolation J∆t(Γ) =
N
X
j=1
∆t Z
D
j(uj(Γ))dx+
N
X
j=1
∆t Z
∂D
l(uj(Γ))ds. (19)
To the discrete time objective function (19) are associated the following adjoint systems. The thermo-elastic adjoint problem is, for any1≤j≤N,
−div(Ae(ηj)) =−j0(uj) in D,
(Ae(ηj))·n=−l0(uj) on∂DF∪∂DN,
ηj = 0 on∂DD,
(20) The heat adjoint equation is, for1≤j≤N−1,
ρpj+1−pj
∆t +div(λ∇pj) =Kdiv(ηj) inD, (λ∇pj)·n=−βpj on∂DF,
(λ∇pj)·n= 0 on∂DN,
pj= 0 on∂DD,
(21)
and the "final condition" forj=N
ρpN −∆tdiv(λ∇pN) =−∆tKdiv(ηN) in D, (λ∇pN)·n=−βpN on∂DF,
(λ∇pN)·n= 0 on∂DN,
pN = 0 on∂DD.
(22)
Remark 2.3 Note that one can easily show thatkpNkL2(D)≤C∆t which is consistant with the fact that the continuous adjoint p(t) vanishes at the final timetf.
The discrete time version of the shape derivative of Theorem 2.4 is the following result.
Theorem 2.5 Let θ ∈W1,∞(D,D) be such that θ·n = 0 on ∂D. Under Assumption 2.2 the shape derivative of (19) is
J∆t0 (Γ)(θ) = ∆t
N
X
j=1
Z
Γ
θ.n
(ρ1−ρ2)Tj−Tj−1
∆t pj+ (λ1−λ2)∇Tj· ∇pj−(Q1j−Q2j)pj
ds
+ ∆t
N
X
j=1
Z
Γ
θ·nn
(A1−A2)e(uj) + (K1−K2)(Tj−Tinit)In
:e(ηj)−(fj1−fj2)·ηj
ods
+ ∆t
N
X
j=1
Z
∂DF∩∂Γ
(β1−β2)(Tj−Tinit)pjθ·τ dL−∆t
N
X
j=1
Z
∂DN∩∂Γ
(gj1−gj2)pjθ·τ dL,
whereηj andpj are the (discrete) solutions of the adjoint problems (20),(22) and (21),n is the normal unit vector to Γ (oriented from Ω1 to Ω2), and τ is the unit vector tangent to Γ and normal to∂Γ, anddL is the(n−2)-dimensional measure along∂Γ.
Proof For1≤j≤N, letTˆj,pˆj ∈Vh×Vh0,uˆj,ηˆj ∈Vh0n. By conventionTˆ0=Tinit. We consider
the Lagrangian
L(Γ,uˆj,ηˆj,Tˆj,pˆj) = ∆t
N
X
j=1
Z
D
j(ˆuj)dx+ ∆t
N
X
j=1
Z
∂D
l( ˆuj)ds
+ ∆t
N
X
j=1
Z
D
(
ρTˆj−Tˆj−1
∆t pˆj+λ∇Tˆj· ∇ˆpj−Qjpˆj
) dx
+ ∆t
N
X
j=1
Z
∂DF
β( ˆTj−Tinit)ˆpjds−∆t
N
X
j=1
Z
∂DN
gpˆjds
− ∆t
N
X
j=1
Z
∂DD
n(λ∇Tˆj)·nˆpj+ (λ∇pˆj)·n( ˆTj−Tinit)o ds
+ ∆t
N
X
j=1
Z
D
n(Ae(ˆuj) +K( ˆTj−Tinit)In) :e(ˆηj)−f·ηˆj
odx.
The proof follows the ideas of Theorem 2.4. For the sake of brevity, we treat only the differen- tiation with respect to the primal temperature variableTˆj which yields a retrograde scheme in time for the associated dual variablespˆj.
First differentiate L with respect toTˆN in the direction of a test function ϕ∈Vh to get at optimality
Z
D
nρϕpN
∆t +λ∇ϕ· ∇pN +Kdiv(ηN)ϕo dx+
Z
∂DF
βϕpNds
− Z
∂DD
{(λ∇ϕ)·npN+ (λ∇pN)·nϕ}ds= 0.
from which we deduce system (22) for pN. Second, differentiate L with respect to Tˆj, for 1≤j≤N−1, in the direction of a test functionϕ∈Vh to obtain
Z
D
ρpj+1−pj
∆t +λ∇ϕ· ∇pj+Kdiv(ηj)ϕ
dx+ Z
∂DF
βϕpjds
− Z
∂DD
{(λ∇ϕ)·npj+ (λ∇pj)·nϕ}ds= 0.
from which we deduce system (21) forpj. The other adjoint equations and the shape derivative
are obtained by similar arguments.
3 A shape optimization problem in additive manufacturing
We now generalize the model of Section 2 to take into account the layer by layer construction process which is at stake in additive manufacturing. More precisely, we follow an approach initiated in [2], [3] for structural optimization. The equations are still the same as in Section 2 but we add the fact that the solid part is evolving in time during the building process.
3.1 A layer by layer model for additive manufacturing
We specify our notations to the case of additive manufacturing. The build chamberDis now a cylindrical domainD=S×(0, H)whereH >0is its height and its cross-sectionS⊂Rd−1 is a
D(ti)⊂ D(ti+1)
D(ti) Ω(ti) =D(ti)∩Ω
h D(tM)
Figure 2: Construction layer by layer. Intermediate domainD(ti)which is a mixture of powder and solid.
smooth simply connected open set: typically, a rectangle S=Qd−1
k=1(0, Lk). By convention, the vertical direction is the building direction. The build chamber D is divided intoM horizontal layers of equal thicknessh=H/M. Let(ti)0≤i≤M be a sequence of increasing time witht0 = 0 andtM =tf such that at each timeti−1 (i≥1) a new layer is added (in a negligible amount of time). Note that the indexM has nothing to do with the indexN in the previous Section 2. We define the intermediate domainD(ti) =S×(0, ih)(see Figure 2), satisfying
D(t1)⊂...⊂ D(ti)⊂...⊂ D(tf)≡ D.
Each D(ti+1) is obtained from the previous D(ti) by adding a layer Li+1 =S× ih,(i+ 1)h (see Figure 4). At timeti the composition of the previous subdomainD(ti)is known and fixed:
it is a mixture of metallic powder and solid. IfΩis the final shape after construction in D(see Figure 3), then the subdomain P := D \Ω is occupied by powder, Ω(ti) := Ω∩ D(ti) is the intermediate solid shape andP(ti) :=P ∩ D(ti)is the intermediate powder subdomain inD(ti).
On the contrary, the composition of the layerLi+1 is going to change between timesti andti+1. Initially,Li+1is made only of powder but, after the heat source term (a laser or electron beam) has passed, it is made partly of powder and partly of solid. The melting and solidification process is completely neglected: powder is immediately changed into solid upon heating by the source term.
Remark 3.1 By comparison with the previous section where the domainDwas decomposed into two sub-domainsΩ1 andΩ2, now each intermediate domainD(ti), after completion at time ti, is decomposed into two sub-domains Ω(ti) and P(ti), occupied by two different materials, solid and powder, respectively. In other words,Ω(ti)plays the role of Ω1 andP(ti)that ofΩ2.
To define the material properties in the layer Li+1, we first need to define the heat source term, which is
Q(x, t) :=q χbeam(t, x)χΩ(x)
whereq∈Ris a fixed value, χΩ(x)is the characteristic function of the shapeΩandχbeam(t, x) is the characteristic function of the beam spot, travelling with time along the beam path (see Figure 3). More precisely, forti< t < ti+1,χbeam is defined by
χbeam(t, x) =χspot(x0−x0i+1(t), xd−ih)
H
Beam path Ω⊂ D
D Build direction
Figure 3: A final shapeΩin the build chamberD.
wherex= (x0, xd)∈S×(0, H),x0i+1(t)is the trajectory of the beam spot in the cross-sectionS, forti< t < ti+1, andχspot(x)is the characteristic function of the beam spot, assumed to have compact support in one layerS×(0, h). We now define the characteristic function of that part of the layerLi+1 already built at timet, forti< t < ti+1,
χL(t, x) =
χΩ(x) ifx0 has already been reached by the trajectoryx0i+1(t)at timet, 0 otherwise.
In other words, the characteristic function χL(t, x) is the cumulated characteristic function of the heat source term.
Remark 3.2 The trajectoryx0i(t)of the beam spot in the top layerLiis an important data of the building process and it can follow a quite complicated path [8], [16]. In particular, the speed (i.e.
its time derivative) is a crucial parameter of additive manufacturing. However, in numerical practice, we content ourselves with simple trajectories, which are straight lines, and with a speed which corresponds to moving the source term from one macro-cell to the next one at each time step (see Figure 4 and Section 5.3 for details).
It is now possible to define the time dependent material propertyξ(t, x), which is a piecewise constant function. Recall that, between times ti and ti+1, only the last layer Li+1 in domain D(ti+1)has changing material properties. Thus, forti< t < ti+1,
ξ(t, x) =
ξΩχΩ(x) +ξPχP(x) ifx∈ D(ti),
ξΩχL(t, x) +ξP(1−χL(t, x)) ifx∈ Li+1. (23) All material properties being defined by (23), the layer by layer model for additive manufacturing is defined as an ordered sequence of problems posed in the intermediate domainsD(ti) for the time interval(ti−1, ti). For1≤i≤M, (ui, Ti)are the solutions of the heat equation
ρ∂Ti
∂t −div(λ∇Ti) =Q in (ti−1, ti)× D(ti), (λ∇Ti)·n=−β(Ti−Tinit) on(ti−1, ti)×∂D(ti)N, Ti =Tinit on(ti−1, ti)×∂D(ti)D, Ti(ti−1) = ˜Ti−1 in D(ti),
(24)
Cij Li
D(ti) D
D(ti−1)
Figure 4: Intermediate structureD(ti)with its last layerLi, made of macro-cellsCij, wherej is a time step index.
whereT˜i−1 is an extension of the previous temperatureTi−1(ti−1), defined as follows T˜i−1=
Tinit in D(ti)\D(ti−1),
Ti−1(ti−1) in D(ti−1), (25)
coupled with the thermoelastic system
−div(σi) =fi in (ti−1, ti)× D(ti), σi=σeli +σith in (ti−1, ti)× D(ti), σiel=Ae(ui) σthi =K(Ti−Tinit)In, σi·n= 0 on(ti−1, ti)×∂D(ti)N, ui= 0 on(ti−1, ti)×∂D(ti)D.
(26)
The equations of the model (24)-(25)-(26) are the same as those in the previous section, except that now the material properties and the domains vary with time. Note that, contrary to the previous section, there is no more a non-homogeneous Neumann boundary condition for the heat equation (24), since it is not necessary for the modelling of additive manufacturing. Therefore, for simplicity, the Fourier part of the boundary∂D(ti)F for the heat equation is assumed to coincide with the homogeneous Neumann part of the boundary∂D(ti)N for the thermoelastic system. As before, fi could be the gravity forces in each layer (and therefore would be independent of the indexi).
Note that problem (24) can be solved independently of (26), and that problem (26) depends on the temperature fieldTi, solution of (24). Even with time dependent coefficients, it is well known [29] that (24) admits a unique solution Ti ∈ C([ti−1, ti];L2(D(ti)))∩L2((ti−1, ti);H1(D(ti))).
ExtendingTi outsideD(ti)by a formula similar to (25) allows us to define a global temperature fields T, equal to Ti for ti−1 < t < ti, which belongs to C([0, tf];L2(D)). Then, in turn (26) admits a unique solutionui∈L2((ti−1, ti);HD1(D(ti))n).
3.2 Optimization problem and shape derivative
Before defining an optimization problem we recall that the shape Ω has to be optimized also with respect to its final utilization. We introduce the corresponding state equation which has completely different loads and boundary conditions with respect to the manufacturing model of the previous section. For some given surface loadsF, we consider the following final utilization
problem, which takes place after the layer by layer construction
−div(AΩe(u)) = 0 in Ω, (AΩe(u))·n=F on∂ΩN, (AΩe(u))·n= 0 onΓ,
u= 0 on∂ΩD,
(27)
with AΩ, the constant elasticity tensor of the solid shape Ω, ∂Ω = ∂ΩN ∪∂ΩD∪Γ where Γ =∂Ω\(∂ΩD∪∂ΩN)only is free to move. Note that (27) is solved just inΩ, without any more powder region, contrary to system (24)-(26) which is solved in the full subdomainD(ti).
We consider an objective function which depends both on the final utilization of Ω, as well as on its manufacturing process
J(Ω) = Z
∂ΩN
j1(u)dx+`
M
X
i=1
Z ti
ti−1
Z
D(ti)
j2(ui)dx dt, (28) where j1 and j2 are two smooth functions with quadratic growth and ` is a fixed weighting coefficient. As mentioned before, (28) is just an example and many more choices are possible, for example depending on the gradient ofui oruand on the temperatureTi, or involving a term at the final time. Our approach easily extends to such cases. Typical examples of functionsj1, j2are the compliance of the final structure and a least square penalization of the vertical displacement above a maximal thresholdu+>0in the top layerLi of each intermediate structure, during the building process,
j1(u) =F·u and j2(u) =
max(u·ed−u+,0)
2χLi.
The motivation for studying the objective function j2 for the building process is that, if the intermediate structures are deformed too much in the vertical direction, it will prevent the rake or the roller to coat a new powder layer, therefore stopping the additive fabrication process.
Remark 3.3 Note that the variable in the objective function (28) isΩwhile it was the interface Γ in the previous section. This is just a matter of notations. The present notation encompasses the previous one since the interface between the solid and powder subdomains is a subset of the boundary∂Ω.
Associated to the objective function (28) we introduce the following adjoint systems.
• For the final displacementu
−div(AΩe(η)) = 0 inΩ, (AΩe(η))·n= 0 onΓ, (AΩe(η))·n=−j10(u) on∂ΩN,
η= 0 on∂ΩD.
(29)
• For the intermediate displacementsui,1≤i≤M,
−div(Ae(ηi)) =−j20(ui) in (ti−1, ti)× D(ti) (Ae(ηi))·n= 0 on(ti−1, ti)×∂D(ti)N, ηi = 0 on(ti−1, ti)×∂D(ti)D.
(30)
