**1. Introduction**

In recent years, the development of additive manufacturing (AM) technologies has led to numerous opportunities to fill the gap between optimal design and product application. The advantages of AM over conventional subtractive or formative methods clearly emerge when considering the grea<sup>t</sup> design freedom that can be achieved [1–3]. AM technologies allow fully dense and near-net-shaped parts to be produced with complex structures made of excellent materials. Industrial applications can be found for metal components [4], for which traditional manufacturing processes are expensive or difficult to apply [5]. The geometries that result from such design techniques as topology optimization (TO) [6–10] are examples of such geometries [1]. The so-called design for AM (DfAM) is being explored to show the design opportunities that are enabled by the adoption of both TO and AM [11,12]. At the current state-of-the art, components for structural applications are redesigned to achieve both weight reduction and performance improvement. In these components, TO has been conducted so far by introducing a decrease in stiffness [13] or using more performing materials specifically developed for AM technologies [14–16]. Such solutions may be effective in several fields, but they may not be practicable when the choice of new material involves a large increase in the cost of a component, or a much lower component stiffness. The stiffness of the structural components (such as the measuring probe, brackets, and the rails that support the measuring probes and motors) plays a key role in the accuracy of the machine, especially for high precision applications [17]. The use of conventional design methods and manufacturing systems lead to heavy and large components as final output [18] to guarantee

high rigidity and limited deformations and vibrations [19,20]. However, the increased component weight (inertial forces) involves higher bending forces which may cause larger measurement errors [17]. Additionally, the accumulation of mass forces implies the use of lower traverse speeds that decrease machine productivity [21]. The aim of this research has been to show how such critical issues can be overcome by exploiting the benefit of DfAM. The study focused on the redesigning of some component of a high precision machine that uses flying probes to test the boards of micro-electromechanical systems (MEMS). Considering that only small production lots are produced per year and each testing machine is highly customized according to the specific requirements of each customer, the redesign lends itself well to production through metal AM systems. Because of the requirements of high dimensional accuracy, the laser powder bed fusion (L-PBF) technique is considered [22]. A comprehensive approach was proposed in which material distribution optimization, design for L-PBF, and design for assembly (DfA) are considered. This approach aimed also to overcome the current limitation of the design techniques and building volume of the L-PBF systems. The feasibility of the components and the machining operations have been investigated, and a comprehensive and detailed cost model has been developed and applied.

#### **2. Design for L-PBF**

During an L-PBF process, a laser source fuses a region of a metallic powder bed according to the computer aided design (CAD). When the one layer has been completed, the building platform is lowered, and the next layer of powder is deposited on the previous one. The process is then repeated until the part is completely built. Production by L-PBF (black flow line in Figure 1) involves several steps: the cleaning of loose powder from the part and post-process operations, including stress-relieving, removal of the supports, shot peening, heat treatments, and finishing operations [23].

The design of a part and its orientation on the building platform together with the choice of process parameters play a key role in the success of the process. From this point of view and according to one of the main rules for correctly using AM technologies, the design for L-PBF should focus on using the material only where necessary. Designing for L-PBF means considering during the design phases not only the constraints of the process, such as the minimum dimension of the feature, but also all activities that aim to guarantee the process and the part compliances, including the next manufacturing steps (support removal and machining). Here, the dimensional and the surface qualities and the metallurgic properties to be achieved on the part need to be considered [24].

For the part design, the DfAM and the DfA are the only possible design methodologies related to AM [25]. However, a comprehensive approach considers five steps: (1) the acquisition of the CAD model; (2) the part optimization; (3) the resulting new design; (4) the optimization of the orientation (purple square), and (5) the design verification. The optimized part from a structural point of view is the input for the subsequent steps. Advanced design techniques, such as TO, can be applied in this phase [2]. The optimized geometry needs to be checked under the geometrical limitation of the process. Adam et al. [26] provided a classification of these limitations, but it should be noted that design rules for L-PBF must not be considered as constraints but as modifiers during the design optimization [27]. Points 3 and 4 are iterative steps which consider reducing as little as possible the modifications of the optimize geometry resulting from step 2. Point 4, together with the support optimization, considers all of the above-mentioned activities. The evaluation of the optimal orientation is a hot topic in literature and usually is mainly based on the avoiding of support structures [28]. Leary et al. [29] proposed a methodology to evaluate the optimal build orientation according to the manufacturing time and component mass. However, because the L-PBF components are only near-net-shape, finishing operations should be carefully considered. Additionally, since support structures cannot be completely avoided, proper support design should be considered as leverage for the process [30].

The optimization phases (orientation and supports) are developed here with the aim to limit the support structure and the allowance for the machining operations. The iterative flow (red flow line in Figure 1) is maintained between the product design and the optimizations steps that may be involved in partial design modifications.

The part orientation phase (purple square in Figure 1) has the purpose of optimizing the part orientation, position, and arrangemen<sup>t</sup> on the build platform because they can have an impact on the stability and speed of the process as well as on the properties of the components e.g., on residual stress-induced warping, known as curl e ffect. When the recoating blade deposits a new layer of powder on the previous one, it approaches the already fused area. If the molten section is large, the force applied by the blade on the part could detach it from the building platform or lead to the stalling of the blade motor. To reduce the contact length, the part should be rotated by a small angle (from 5 to 8 degrees on the building platform with respect to the blade).

Cylindrical surfaces are the first features that need to be considered. The best solution, which reduces the dimensional error, is the positioning of the cylindrical surfaces with their axis perpendicular to the building platform. Among the surfaces, the accuracy of the internal ones has the priority due to the di fficulty to machine these kinds of surfaces. Anyway, the accessibility of the area to remove the support should always be verified. In fact, the purpose of adjusting the orientation of a part is also to alter the inclined angles of the overhanging surface to minimize the number of support structures. Support structures locally reduce the dimensional and the surface quality, and therefore they limit the design freedom due to the additional post-processing operations required to remove the supports. Similarly, support structure should be avoided on thin features that could be damaged during the support removal operations. The orientation should also minimize the number of surfaces to be finished. If the rough surface or support structure cannot be avoided, those surfaces should be the same that, according to the design requirements, need to be machined. Therefore, e.g., the best solution is to move and rotate the part until the surfaces to be machined are the same that serve to attach the support to the part. Overall, it should be considered:


The next step "support design" (blue square in Figure 1) aims to design proper support structures that allow fixing the part to the building platform, to support critical surface angles and to prevent deformation of the part due to heat accumulation and thermal stresses [30]. Additionally, the support design workflow helps in designing suitable support structures that are easy to remove and minimize the machining.

**Figure 1.** Design and manufacturing for an laser powder bed fusion (L-PBF ) process.

#### **3. Economic Analysis**

The overall objective of the economic analysis is to estimate the manufacturing costs. Di fferently to the literature on the conventional manufacturing processes, the costs for metal AM processes have only been dealt with in a limited number of case studies, and a well-structured approach has not ye<sup>t</sup> been presented. Rickenbacher et al. [31] introduced a cost model for L-PBF that considered only some components to calculate the manufacturing costs. However, they neglected some relevant items, such as the fixed cost of the machine due to the maintenance and the heat treatment required to release the thermal stresses. Moreover, they introduced arbitrary factors to model the frequency of material changes, which was evaluated on the basis of a single build rather than a single part. Baumers et al. [32] proposed a general production cost model for electron beam melting (EBM) and direct metal laser sintering (DMLS), which is the EOS GmbH tradename for their L-PBF process machine. Their study estimated the costs according to machine usage. However, they did not consider any design optimizations and neglected the partition of the machine cost of when di fferent components are produced in the same job. A general estimation model of the manufacturing costs of AM processes should consider the so-called well-structured costs, Cwell-structured, which cover the direct and the indirect costs and can be computed for a single produced part. The direct costs refer to the costs that are directly associated with the production. They are absent if the production is halted. The indirect costs refer to those costs which cannot be avoided when the production is interrupted, such as the salaries of the administrative sta ff. The direct costs and the indirect costs are functions of the build time of each part.

As far as the indirect costs are concerned, the following costs were calculated:

• Machinery depreciation, which is distributed over the total working hours of the year and computed in proportion to the build time, according to Equation (1):

$$\mathbf{S} = \frac{\mathbf{C}\_{\text{machire}} (\mathbf{1} + \mathbf{i}) \ ^ {\text{n}}}{\mathbf{n}} \cdot \frac{\mathbf{t}\_{\text{build}}}{\mathbf{h}\_{\text{year}}} \tag{1}$$

where Cmachine is the cost of the machine, n is the number of years, which is usually assumed equal to 5, i is the interest, hyear is the annual working hours, and tbuild is the building time.


The direct costs are:

• The design costs per part Cd, which can be referred to as the time required to design and optimize the geometry. Since an optimized geometry must be obtained from a manufacturing design, Cd also refers to the time spent assembling the job (all activities included in checking Figure 1 such as the creation of the STL file, orientation, creation of the support structures, slicing and setting the process parameters). Thus, Cd can be computed as:

$$\mathbf{C\_{d}} = \frac{1}{\mathbf{N\_{ps}}} \left[ \mathbf{C\_{d\text{oper}}} + \frac{\mathbf{C\_{CAD\text{sw}}}}{\mathbf{h\_{CAD\text{sw}}}} \right] \mathbf{t\_{d}} + \left( \mathbf{C\_{d\text{oper}}} + \frac{\mathbf{C\_{CAM\text{sw}}}}{\mathbf{h\_{CAM\text{sw}}}} \right) \mathbf{k\_{1}t\_{\text{job}}} \tag{2}$$

where Cdoper is the designer's hourly rate, expressed in €/h, CCADsw and CCAMsw are the cost per user of the annual software license for the CAD model and the job preparation, respectively, hCADsw and hCAMsw are the number of hours of use of the software per year (€/year) for the CAD model and the job preparation, respectively, td is the time that is required for the design and tjob is the time that is required to prepare the job, and it is weighed by k1, which is the ratio between the volume of the part (including the support structures and the allowances) V O+A+S and the total job volume Vjob. k1 is used to account the building of parts with di fferent geometries in the same job. k1 considers that the larger the volume of the part, the more time is required for the building time. Nps is the total number of parts that have to be produced.

•Setup Cost per part, which refers to the preparation of the machine before the job starts. It includes the cost of filling the dispenser, Cfill, the cost of preparing and checking the chamber, Cenv (e.g., argon flow), the cost of resurfacing the build platform, and Cbuild plat, the cost of removing the supports after the part has been removed. Build platforms are usually re-surfaced by milling operation [33]:

$$\mathbf{C\_{setup}} = \mathbf{k\_2C\_{fill}} + \mathbf{k\_1C\_{errV}} + \mathbf{k\_3C\_{build}} \text{ plant} \tag{3}$$

where Cfill = Coper·tfill, and tfill is the time required to fill the dispensers. Cbuild plat = (Cmachining · W(H + ex)) ⁄ (0.50Dvo) is the cost of resurfacing the build platform by machining in a single operation. Cmachining is the hourly cost of the milling machine, W and H are the dimensions of the build platform, ex is the sum of the approach length and the overtravel, D is the diameter of the mill, and vo is the material removal rate (mm/min). As k1, k2, and k3 take into account the building of parts with different geometries in the same job, k2 considers the percentage of material utilized for the building of the part against the quantity used to fill the dispensers (Matfilldisp). Matfilldisp considers a quantity of powder expressed in kg that corresponds to the quantity of material to fill a building volume corresponding to the maximum height of job multiplied for the dose factor. The dose factor depends on the saturation of the build platform. An additional 20% of material could be also considered. k3 considers that the larger the projected area of the part on the build platform is, the higher the cost of the part for the milling operations:

$$\mathbf{k}\_2 = \frac{\mathbf{W\_m}}{\mathbf{M\_{fulldisp}}} \tag{4}$$

$$k\_3 = \frac{\text{surface of the build platform occupied by the part}}{\text{total surface of the occupied bulb platform}} \tag{5}$$

where Wm is the quantity of material used to build the part and Matfilldispis the quantity of material used to fill the dispenser.

When Argon is used, Cenv can be computed as follows:

$$\mathbf{C\_{err}} = \mathbf{C\_{Ar}}V\_{\mathbf{Ar}} + \mathbf{C\_{oper}}\mathbf{t\_{operAr}}\tag{6}$$

where CAr is the price of the Argon per m3, and VAr is the total volume of Argon used to fill the build chamber and achieve the right pressure before the process starts. toperAr is the time required for the operator to start and control the procedure.

• Production cost, Cproduction, refers to the direct cost of building the part. This cost includes the energy consumption of the machine and of the other systems, including gas consumption. In addition, this cost includes the maintenance of the machine and the other systems during which a downtime period is required. These costs are computed as indirect costs and are a function of the time that the machine is used to build the part. However, these costs are not taken into account when the machine is not utilized, unlike the indirect costs, which still have to be considered, even when the production is halted:

$$\mathbf{C\_{production}} = \mathbf{C\_{gas}}(\mathbf{t\_{exp}} + \mathbf{k\_4}\mathbf{t\_{cooling}}) + \mathbf{C\_{AM}}\mathbf{t\_{build}} \tag{7}$$

where Cgas is the hourly rate cost of the gas that is used during the building and cooling of the part, CAM is the direct hourly rate cost of the AM system and is the sum of the costs of the energy consumption per hour, of the maintenance of the machine and the other Cmm systems. Cmm, which is distributed over the total hours between two subsequent maintenance operations of the tb2m systems, is calculated as follows:

$$\mathbf{C\_{mm}} = \sum \frac{\left(\mathbf{C\_{open}}\mathbf{t\_{mm}} + \mathbf{C\_{nc}}\right)}{\mathbf{t\_{b2m}}} \tag{8}$$

where Coper is the operator's hourly rate cost, expressed in €/h, tmm is the time that is required for the maintenance operations and Crc is the cost of the replaced components.

•Material cost, Cmat, is obtained from the total material quantity Wm and is calculated according to Equation (9). The cost of powder, Cpowder, refers to the cost per kilogram of powder:

$$\mathbf{C}\_{\text{mat}} = \mathbf{W}\_{\text{m}} \mathbf{C}\_{\text{powder}} \tag{9}$$

• The manufacturing cost is related to the build time, and it includes the idle time and the exposure time, texp, per part, as presented in Equation (10):

$$\mathbf{t}\_{\text{build}} = \mathbf{k}\_1 \mathbf{(t}\_{\text{heating\ plant}} + \mathbf{t}\_{\text{aux}} + \mathbf{t}\_{\text{fill}\text{Ar}}) + \mathbf{t}\_{\text{exp}} + \mathbf{k}\_4 \mathbf{t}\_{\text{cooling}} \tag{10}$$

where theating plat is the time required to heat the build platform, taux is the extra time required before starting the process (the cleaning and levelling processes of the build platform, compacting and leveling the powder, cleaning the lens and lens cover), tfillAr is the time required to fill the build chamber with argon, and tcooling is the time required to cool the part. k4 is a coefficient that is introduced to consider the building of parts with different geometries in the same job and is defined as follows: 

$$\mathbf{k}\_4 = \left( 1 - \frac{\text{the total surface of the part}}{\sum \text{the surface of the parts}} \right) \tag{11}$$

k4 considers that the larger the surface of the part is, the more rapid the cooling.

• The post-processing cost, Cpost proc, only includes the operations that are mandatory to consider the AM process complete. For these reasons, Cpost proc contains the cost of removing the support structures, Crem supp, the cost of the post treatment, Cpost treat, the cost of the heat treatment to release the residual stresses for the L-PBF process, and the cost of polishing the part, Cpolishing, by shot peening:

$$\mathbf{C\_{post\,proc}} = \mathbf{k\_3}\mathbf{C\_{rem\,suppp}} + \mathbf{k\_1}\mathbf{C\_{post\,treat}} + \mathbf{C\_{polishing}}\tag{12}$$

$$\mathbf{C}\_{\text{rem\\_supp}} = (\mathbf{C}\_{\text{EDM}} \mathbf{v}\_{\text{EDM}} + \mathbf{C}\_{\text{saw}} \mathbf{v}\_{\text{oCr}}) \mathbf{l}\_{\text{build plantform}} + \mathbf{C}\_{\text{oper\\_rem}} \tag{13}$$

The support can be removed by means of a wire electro discharge machining (EDM) process, manually or by sawing. CEDM and Csaw are the hourly costs of the EDM machine and of the saw, respectively. voEDM and voGr are the material removal rates for EDM and sawing processes, respectively. lbuild platform is a length of the build platform and trem is the time required to remove the support structures manually. Cpost treat and Cpolishing are evaluated as the hourly costs for the machine and the time needed to complete the operation. k3 considers that more supports are necessary for larger surfaces. k1 considers that the larger the parts are, the longer the time needed in the oven.

• Finishing costs, which refer to the additional operations necessary to finish the part and achieve the required dimensional, geometrical, and surface accuracy. This information should be defined at the design stage.

#### **4. Case Study**

The study has dealt with the system (Figure 2) that supports and moves a flying measure probe in a working volume. Each test machine has eight flying measurement probes: four to acquire signals

from the top of the board and four to acquire signals from the bottom of the board. The high precision flying probe is supported by a bracket which also contains the vision system and the lighting system that are used to acquire images during the measurements, the mechanisms that are used to move the flying measure probe and the data collection systems. The flying probe and the system used to collect data and to move the probe are joined to the bracket by means of six screws, while the vision system and its data collection system are joined to the bracket with four bolts. The whole system (bracket, flying probe and vision system) is joined to the X-rail by four bolts and aligned precisely with two dowel pins. The linear motor guide is assembled in the upper part of the X-rail. The X-rail also contains the motor stator magnets which are enclosed in the rail by two side covers. The X-rail is joined to the bottom part of the Y-rail and is equipped with another linear motor. The electric motors move the whole system linearly along the X and Y axes. The probe can also be moved along the Z\*axis, which is rotated by a certain angle with respect to the normal of the XY-plane. The overall dimensions of the two rails (orange in Figure 1) are 220 × 690 × 101 mm<sup>3</sup> and the total weight is around 5000 g. The maximum envelope for the bracket is 150 × 60 × 60 mm3. The bracket weighs 203 g, while the whole system (probe, vision and lighting systems) weighs about 1400 g.

**Figure 2.** Case study. The systems that are subjected to redesigning are the X and Y rails and the bracket.

From the structural point of view, the most critical components for the accuracy of the machine and its dynamics are the two rails and the bracket. Because of the low number of parts produced per year (around 400 parts), a three-axis milling machine is used today to shape the components from raw aluminum 7075 alloy ingots.

The part of the system that considers the two rails includes 16 elements and requires 12 operations to join the two rails. The weight of these elements lies on the linear motor and affects the acceleration and deceleration ramps when the flying probe is moving in the working volume. Owing to the design criterion which lies to the high stiffness, the system is subjected to low stresses. The redesigning of the rails is aimed at streamlining the assembly in order to reduce the geometric errors that are accumulated and propagated, step by step, during the assembly process and which may affect the machine accuracy [34]. Redesigning is also aimed at reducing the total weight while maintaining stiffness. The design limitations, due to the larger rail dimensions (690 mm) than for most industrial metal component AM systems, need to also be considered at the design stage together with subsequent machining operations.

The current design of the bracket exhibits a maximum displacement of over 30 μm, which exceeds the design requirements of 20 μm. The generated bending excess affects the position of the probe and the precision of the measurements. This error is currently compensated for by adopting suitable algorithms to adjust the probe positioning. From an industrial point of view, this choice is the best compromise between the time and costs necessary to design and manufacture (machine and component set, tools, etc.) the bracket. The bracket redesign is aimed at increasing the stiffness to that of the requirement (20 μm) while maintaining its original weight to avoid an increased load on the electric motors.

Both components are designed to be produced using gas atomized AlSi10Mg powder, as it has similar properties to the original material. The material properties are reported in Table 1. Exact details about the machine, the geometries, the load conditions and the working cycle considered in this case study cannot be disclosed for confidentiality reasons. However, this should not rule out the understanding of the main findings of the case study.


 71,700

> 503

 350

 75,000

 of Aluminum 7075-T6 for the three-axis milling machine and
