Digital design and manufacturing of a railway bogie
demonstrator via multi-material wire arc directed
energy deposition

Antoine Queguineur, Amirmohammad Daareyni, Hossein Mokhtarian, Matti

Isakov, Remco Rook, Wei Ya, Constantinos Goulas, Jean-Yves Hascoët, Iñigo

Flores Ituarte

► To cite this version:

Antoine Queguineur, Amirmohammad Daareyni, Hossein Mokhtarian, Matti Isakov, Remco Rook, et al.. Digital design and manufacturing of a railway bogie demonstrator via multi-material wire arc directed energy deposition. International Journal of Advanced Manufacturing Technology, 2025, 136 (11-12), pp.5281-5303. 10.1007/s00170-025-15132-7 . hal-05505491

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# Digital design and manufacturing of a railway bogie demonstrator via multi-material wire arc directed energy deposition

Antoine Queguineur<sup>1,2,●</sup> · Amirmohammad Daareyni<sup>1</sup> · Hossein Mokhtarian<sup>1</sup> · Matti Isakov<sup>1</sup> · Remco Rook<sup>3</sup> · Wei Ya<sup>3</sup> · Constantinos Goulas<sup>4</sup> · Jean-Yves Hascoët<sup>2</sup> · Iñigo Flores Ituarte<sup>1</sup>

Received: 11 September 2024 / Accepted: 30 January 2025 / Published online: 10 February 2025
© The Author(s) 2025

## Abstract

The sequential digital design and manufacturing of components play a crucial role in realizing the industrial potential of directed energy deposition (DED), particularly when employing an electric arc as the energy source to melt a filler wire (DED-ARC). This study explores the application of DED-ARC for manufacturing large-scale, load-bearing structures, using a railway bogie as a case study. Originally a cast Bettendorf-type design, the bogie was redesigned using a multi-material approach. High-strength low-alloy (HSLA) steel was utilized in high-stress areas, while low-carbon steel was used elsewhere to reduce mass, enhance manufacturability, and improve repairability. The workflow included computer-aided design (CAD), topological optimization, finite element analysis (FEA), material selection, and iterative CAD modifications to address process constraints. The redesigned bogie underwent pre-manufacturing, fabrication, and a final scan of the as-built part. Representative multi-material wall samples were characterized, revealing typical microstructures and elastic limits of 468 MPa and 737 MPa for ER70S-6 and ER100S-G, respectively. These tensile properties were incorporated into FEA verification simulations, demonstrating a higher safety factor compared to the original design. A CAD-to-part analysis, including scan comparisons, highlighted manufacturing-induced deformation, material-dependent over-thickness, and localized geometric variations. This study offers a comprehensive overview of the DED-ARC process, from design through characterization, and demonstrates its capability to produce high-quality industrial components. The findings underscore the manufacturability and potential of DED-ARC for fabricating robust, multi-material structures for demanding applications.

**Keywords** Directed energy deposition · Design for additive manufacturing · Railway · Multi-material

## 1 Introduction

Directed energy deposition (DED), as defined by the ISO/ ASTM 52900 standard [1], is an additive manufacturing (AM) process that uses focused thermal energy, such as an electric arc, to melt filler wire, and produce parts. In this

context, this study refers to it specifically as DED-ARC. It is a competitive manufacturing alternative enabling the production of end-use parts with an increased stiffness-to-mass ratio, relatively complex, large-scale geometries, and low production volumes [2–4]. DED-ARC can be used to produce metallic structures and components. The technology provides a comprehensive material selection window as in practice any weldable material can be processed by DED-ARC. Additionally, adapting a welding torch on a robotic integrated system gives the possibility for large dimensions with almost no size limitations [5]. Large, complex multifunctional structural parts can be efficiently produced by DED-ARC, and, as the technology is maturing, it becomes a feasible and attractive alternative for a variety of industrial sectors, such as aerospace, automotive, marine, energy, defense, and other key industrial sectors [6].

DED-ARC is one of the most economical and widely used process among DED technologies [7]. The potential benefits

<figure><img src="image_2.png" /></figure>

<footnote>Antoine Queguineur
antoine.queguineur@tuni.fi</footnote>

<footnote n="1">1 Faculty of Engineering and Natural Sciences, Tampere University, 33720 Tampere, Finland</footnote>

<footnote n="2">2 GeM - UMR CNRS 6183, Ecole Centrale de Nantes, 44321 Nantes, France</footnote>

<footnote n="3">3 RAMLAB BV, Scheepsbouwweg 8, 3089 JW Rotterdam, The Netherlands</footnote>

<footnote n="4">4 Department of Design Production & Management, Faculty of Engineering Technology, University of Twente, 7500AE Enschede, The Netherlands</footnote>
of DED-ARC can be listed as (i) cost-effectiveness compared to other AM technologies, such as powder bed fusion, as it uses wire feedstock instead of powder; (ii) higher material deposition rates with 10–14 kg/h [8, 9] and increased efficiency of the power supply and the arc generation system (~90% of efficiency rate) [10]; (iii) high material utilization as nearly 100% of the filler material wire is deposited, reducing waste [11]; (iv) enhanced mechanical properties compared to traditional casting methods, producing finer-grained microstructures, resulting in improved strength and toughness [12]; (v) reduced health risk compared to powder-based techniques as wire feedstocks do not require special handling by the operators; and (vi) reducing the overall cost and lead time in end-use applications involving slow-moving parts and low-production volumes [13].

DED-ARC is a feasible technology in repairing and re-manufacturing of high-value components, as well as in producing uniquely designed, large-scale components and high-value metal parts for various industrial sectors, including aerospace, energy, military, and naval industries [14]. Artaza et al. [15] demonstrated how the retrofitting of existing machine components becomes technically feasible, while being an alternative for part production in special cases. In their study, they implemented a DED-ARC system into a gantry-type machine tool. A part design is proposed, followed by parameter testing and manufacturing of the part. Previous research has investigated large size components manufactured with DED-ARC as for the “3D-Printed Bridge” manufactured in the Netherlands by the MX3D company. Gardner et al. [16] performed extensive testing on coupons and large representative parts of the bridge to assess the structural performance under loading. Finite element simulation and verification complete this study allowing the part to be used in a public environment.

Similarly, Lipiäinen et al. [17] examined the performance of a large-scale pressure vessel fabricated using DED-ARC. Their findings demonstrated favorable mechanical properties and microstructural characteristics, including a pressure test exceeding the required values. Additionally, the dimensional accuracy showed some deviation but was considered acceptable. In the specific focus of construction industry, Camacho et al. [18] suggested the interest of using multi-material and the advantages of localized high-performance materials with interdisciplinary approach. Innovative architectural forms can be achieved using the DED-ARC process supported by computational and advanced parametric design, which integrates the material properties and manufacturing constraints [19]. Incorporating multiple material approach raises uncertainties in terms of material compatibility at the interfaces. The integration of Design for Additive Manufacturing (DfAM) principles in DED-ARC can rely on methods such as topological optimization and functional design to produce complex geometries alloying for potential multi-material

designs [20]. However, further research is required to facilitate mainstream adoption of this technology.

This research investigates the sequential digital design and manufacturing steps for large-scale load-bearing applications using DED-ARC. The focus is on a railway structural bogie, specifically a Bettendorf-type, which is used as a demonstrator. The rationale to select this specific case study is that national rail companies invest heavily in the annual maintenance of train components, emphasizing the critical nature of maintenance and the need to identify potential failure risks in bogies [21, 22]. Vibration-induced fatigue damages bogie frames, impacting their longevity and maintenance costs [23, 24]. Involving topology-optimized light weight design could be beneficial as the reduction in train mass significantly decreases the railcar travel costs, enhancing economic efficiency. Additionally, lighter trains contribute to environmental sustainability by decreasing the carbon dioxide emissions [25]. A bogie manufactured with DED-ARC could become a solution to reduce mass and save material. Besides, DED-ARC-fabricated structures, as they are made by weld material, can be repaired more easily by welding than a cast low carbon steel equivalent product, extending its potential lifetime and supporting remanufacturing strategies. From an economic perspective, DED-ARC is generally more costly than traditional methods such as casting and machining, particularly for simple geometries and high production volumes [20, 26]. However, additional factors must be considered such as easier maintenance and repair during the lifecycle of the component, as well as reduced lead-times for replacement or spare-part application and the improved design flexibility.

The DED-ARC process, despite all its advantages, has an intrinsic interrelated process and material challenges. These challenges include (i) high residual stress and deformation [27, 28] and (ii) the occurrence of defects such as porosity and cracking [29]. Additionally, it is crucial to achieve the necessary quality, reliability, and repeatability to meet the geometric and material requirements, defined by the final application, for robust production [30]. This necessitates extensive automation, process monitoring, detailed process parameter optimization studies, and material testing [31]. Experimental research in DED-ARC is often limited to material and process-related characterization using small samples. Limited research is available on large-scale applications in DED-ARC technology. DED-ARC industrialization implies contributions from multiple engineering and scientific disciplines, including welding research and materials science [32], engineering design to manufacturing process planning [5], control, and quality [33, 34].

The research presented here takes advantage of digital design-to-manufacturing workflows by utilizing the multi-material capabilities of DED-ARC technology. The novelty of this research is based on several key aspects. First, it

<figure><img src="image_3.png" /></figure>
develops and implements a sequential digital design-to-manufacturing workflow to topologically redesign and manufacture a structural bogie using a multi-material approach. This strategy aims to provide an efficient and integrated solution for optimizing structural performance and material utilization. Second, it introduces the new concept of a bogie as a large-scale complex demonstrator fabricated entirely using the multi-material DED-ARC process. The application of multi-material DED-ARC has been already implemented in previous applications. However, this work demonstrates its scalability and applicability to the fabrication of large, complex structures, such as a bogie, which has not been explored in detail in prior research. Finally, the study examines the effects of the manufacturing process plan, computer-aided design (CAD) to part distortions, and the mechanical characterization of multi-material walls representative of the fabricated component. This work integrates these aspects into a unified investigation for the multi-material DED-ARC process, proposing a complementary dimension to the design workflow. It links the gap between design, manufacturing, and performance evaluation.

The remainder of this paper is divided into two sections. The “Materials and methods” section details the methods, including a presentation of the use case and workflow, optimization techniques, the manufacturing equipment utilized, and material characterization conducted on the additionally produced sample walls. The “Results and discussion” section presents the results of characterization, design verification, manufacturing process plan, and the CAD to part comparison. A conclusion summarizes the primary and secondary findings of the study.

# 2 Materials and methods

## 2.1 Case study: the bogie demonstrator—topology optimization and (re)design workflow

This study focuses on a Bettendorf-type bogie originally made from cast iron; a design first developed in the USA in 1910. This part can be considered an obsolete spare part, demonstrating significant value as a use case. The accessibility of the design and the transparency of its inputs and outputs, which are openly available, ensure that there are no concealed industrial details or confidentiality issues. The original design can be found on GrabCAD repository [35]. In trains, bogies support and guide the axles of wagons, designed to stabilize, and support railway vehicles. Hence, these systems are subjected to significant mechanical forces under high levels of traction and braking, thereby requiring robust mechanical strength. Typically, each rail coach has two bogies. Bogies are placed near each end, and they serve as connection between the

wheels and the train. The main objective of this study is to analyze and enhance the side frame, which the authors have identified as the most promising component to be additively manufactured as it is a structural part originally cast.

Furthermore, the demonstrator is designed through topology optimization to reduce its weight and subsequently the production costs, as well as it combines two steel grades with different mechanical properties, namely a higher and a lower strength steel. Having practically the same density, the two alloys result in a difference of specific strength. Therefore, the use of high specific strength material in loaded parts can lead to weight reduction. The part, which is entirely made of melted wire, enables easy welding repairs using wire of a similar chemical composition and manufacturing parameters. This strategy aims to enhance the repairability of the system and extend its lifespan. The material selection involves high-strength steel for mechanically loaded areas and low-carbon steel for other areas, enabling additional mass reduction. Figure 1 illustrates the optimization process and design adaptation for manufacturability developed in this study. Each step is detailed in the subsequent sub-section, highlighting the strategic modifications made to ensure compatibility with manufacturing constraints and capabilities.

The weight of bogie was reduced through topological optimization, a design procedure which also increased its stiffness by incorporating multi-material use. Similar boundary conditions as described in EN 13749 standard [36] were selected and shown in Fig. 1a together with an expanded design space. Realistic loading conditions were chosen to guide the (re)design, which included a vertical load of 90 kN applied at the center of the frame where the coil springs actuate, along with perpendicular shear forces at the connection with the central frame, equal to two times 16 kN. Ultimately, the design is constrained at both extremities, on the planes connecting to the wheels, with fixed displacement constraints in all three axes. Multiple iterations on the topology-optimized design were necessary, as shown in Fig. 1b, in order to reach the final design.

During the preliminary stage of the part preparation, the iterations considered the resistance to loading, as well as the manufacturing constraints and the capability to produce the part. Figure 1c shows the finite element analysis (FEA) verification considering safety factors and load-bearing areas. The design verification is based on the mechanical properties obtained from the material characterization of the additional walls manufactured alongside the demonstrator. The wall dimensions were representative of the dimensions expected to be encountered during the production of the bogie. The final design incorporates an alternative material, high-strength low-alloy (HSLA) steel, originally located in areas of locally high stress. Figure 1d illustrates a proposed
<figure><img src="image_4.png" /><figcaption>**Fig. 1** Overall description of the bogie optimization from the definition of the boundary conditions to the final part to be manufactured. a Design space analysis and boundary condition definition. b Topology optimization</figcaption></figure>

iteration. c FEA verification and validation simulations.

d DED-ARC path planning and printing

DED-ARC build strategy comprising three manufacturing steps.

## 2.2 Shape optimization, (re)design workflow, and CAD to part comparison

The objective of re-designing the bogie as a demonstrator was to achieve a DED-ARC-adapted multi-material, topology optimized bogie. The shape optimization of the bogie demonstrator followed a 3-step workflow, shown in Fig. 2. The workflow includes the following three steps: (i) shape optimization under static loading conditions for a single material, followed by surface smoothening and finite element analysis (FEA) static validation simulation; (ii) Feature modifications for multi-material consideration, DED-ARC adaptability, and increase of part’s stiffness; and (iii) FEA static validation simulation for multi-material, topologically optimized part with material mechanical properties derived from material characteristics of the current study.

The first step of the workflow aims to create a topologically optimized part using a single material. Table 1 summarizes the material properties of the selected low carbon steel (LCS), used in shape optimization. An initial design space covering a wider area around the Bettendorf-type bogie was utilized.

A simplified set of boundary conditions based on EN 13749 standard [36], which characterizes static bending, was adopted for the shape optimization. Equations (1) and (2) represent the calculation of lateral ($F_y$) and vertical forces ($F_z$), respectively. In Eqs. (1) and (2), $m_v$, $m+$, $c_l$, and $g$ stand for the vehicle mass, the bogie mass, the passengers’ mass, and acceleration due to gravity, respectively. $F_{z1}$ and $F_{z2}$ represent the vertical forces in each frame. Each train carriage is located on two bogie frames (in total 4 bogies per wagon); hence, the total force is divided evenly between

<figure><img src="image_5.png" /></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 2** Adopted workflow to simplified topology optimization (multi-material optimized and DED-ARC adopted part)</figcaption></figure>

bogies. The mass of the wagon, passengers, and the bogie itself were assumed to be 20 tons, 6.5 tons, and 0.2 tons, respectively. Hence, the total lateral force was calculated to be 32 kN, and vertical force was 90 kN. Figure 3 schematically represents the boundary conditions (loads, constraints, symmetry planes, and preserved regions with their dimensions) on the design space. As shown in Fig. 3, the constraint is applied to the right and left end of the bogie, where the bogie is attached to the axles.

$$F_{y1} = F_{y2} = \frac{F_y}{2} = 10^4 + \frac{(m_v + c_1)g}{12} \quad (1)$$

$$F_{z1} = F_{z2} = \frac{F_z}{2} = \frac{1.4g(m_v + c_1 - 2m^+)}{4} \quad (2)$$

The shape optimization was performed on a single material (LCS) with the Fusion 360 software, focusing on mass reduction while preserving equivalent mechanical strength.

Fusion 360 utilizes the Solid Isotropic Material with Penalization (SIMP) method, to optimize the distribution of material within a given boundary conditions and design space. The SIMP algorithm uses local mechanical constraints to design a part with minimum material. The method produces an outcome of a continuous distribution of voxel density, which needs to be penalized to produce a manufacturable result without intermediate densities. The outcome often requires human intervention to decide on the design, which often is not the global optimal. Additional post processing may be necessary to smoothen the rough geometries produced from discrete methods, such as SIMP [38]. A total of 640,286 linear tetrahedron elements with an average element size of 10 mm were used to mesh the design space. The shape optimization is followed by surface smoothing and FEA static simulation. This study employs a linear elastic static model to simulate the structural behavior of the bogie under specified loads and boundary conditions.

The safety factor (Sf), a key concept in engineering design, quantifies the ability of a system to handle loads exceeding the expected ones. This study employs Eq. (3) to calculate Sf.

$$Sf = (\text{Yieldstrength}/\text{Max.vonMisesstress}) - 1 \quad (3)$$

The second step of the workflow in Fig. 2, focused on adapting the part features to enhance the manufacturability in DED-ARC and increase the part’s stiffness. The optimized component, when fabricated using a single-material approach, may result in excessive mass, thereby diminishing the potential benefits of the AM process for train bogies. Consequently, to reduce the final mass of the part, it is necessary to minimize the amount of material added and to decrease wall thickness. This reduction would lead to higher stress in specific areas, justifying the substitution of material to high-strength steel locally. The incorporation of multi-material regions into the optimized geometry, along with reductions in material quantity and thickness, effectively lowers the final mass of the component. Additionally, stiffeners were added to the geometry to further increase part stiffness, increase safety factor, and minimize the deformation during and after manufacturing. To increase manufacturing adaptability with DED-ARC and improve printability

<table><caption>**Table 1** Material properties (LCS and HSLA)—extracted from [37]</caption><thead><tr><th>Parameter (symbol)</th><th>Unit</th><th>Low carbon steel</th><th>High-strength<br>low-alloy steel</th></tr></thead><tbody><tr><td>Density (ρ)</td><td>kg/mm³</td><td>7.85E-6</td><td>7.85E-6</td></tr><tr><td>Young's modulus (E)</td><td>GPa</td><td>220</td><td>200</td></tr><tr><td>Poisson's ratio (ν)</td><td>—</td><td>0.27</td><td>0.30</td></tr><tr><td>Yield strength (σ<sub>y</sub>)</td><td>MPa</td><td>207</td><td>720</td></tr><tr><td>Ultimate tensile strength (UTS)</td><td>MPa</td><td>345</td><td>780</td></tr></tbody></table>
<figure><img src="image_7.png" /><figcaption>**Fig. 3** Shape optimization boundary conditions</figcaption></figure>

of the part, a teardrop shape was considered for overhanging geometries, as shown later in Fig. 4c.

The third step of the workflow in Fig. 2, used a static FEA analysis to analyze the multi-material DED-ARC-adapted design. The material properties of LCS and HSLA were assigned to the corresponding sections according to

the data obtained from the mechanical characterization of the additional walls. The rest of the boundary conditions (loading, constraints, preserved regions, mesh type, and mesh size) remained the same. Conducting FEA analysis with similar boundary conditions allows us to compare the results in three cases of original design, topology optimized, and multi-material DED-ARC-adapted design.

Geometrical characterization is achieved by comparing the CAD model with the manufactured part. Geometric dimensioning and tolerancing (GD&T) is a comprehensive standard in engineering and manufacturing that defines part dimensions and shapes to ensure the design requirements are achieved. First accepted in 1994, the ASME Y14.5 standard is the primary document governing GD&T practices [39]. In this study, GOM INSPECT (Zeiss) metrology tool was used to perform GD&T analysis on the 3D scan of the printed bogie demonstrator.

Hence, to produce a 3D reconstruction of the printed part, photogrammetry was used. A set of high-resolution pictures was made from all sides of the part. Using some filtering steps, the high-resolution pictures could give a point cloud of the part that was converted into a mesh (.stl). Using some markers with a known size and distance between each other, the reconstructed model could also be scaled to the proper dimensions. The maximum resolution of surface deviations is based on the camera and can be taken as 0.2 mm for this reconstruction. The actual dimension trueness is based on the measurement accuracy of the distance between the markers used in the reconstruction. A typical value of dimension trueness is 0.5 mm per 300 mm, but a more in-depth investigation should be conducted to get the actual dimension trueness of this reconstruction.

Utilizing the mesh file provided by RAMLAB along with the original CAD file, a GD&T analysis was conducted in the software virtual environment. Initially, both the CAD

<figure><img src="image_8.png" /><figcaption>**Fig. 4** Manufacturing preparation: a Scaling down. b Toolpath strategies used in extra sampling. c Representative pre-production part</figcaption></figure>

<figure><img src="image_9.png" /></figure>
file and the mesh file were imported into the software. Next, the measured data (mesh) was aligned with the CAD model using best-fit alignment. Planes were constructed on functional surfaces using Gaussian best-fit on in GOM inspect. Finally, the specified GD&T features, such as the flatness of reference surfaces (boundary faces) and deviations of the measured features from the nominal CAD model, were evaluated on the scanned data. Parallelism and perpendicularity of the functional surfaces were analyzed. The details of the definition of planes and GD&T results are described in the “Shape optimization results and design verification” section.

## 2.3 DED-ARC: machine, materials, and manufacturing process plan

The topologically optimized bogie, representative parts, and associated test specimens were executed with an integrated Valk Welding manufacturing cell using a Panasonic TM 2000 6-axis welding robot and a 2-axis external positioner. The DED-ARC setup consists of Panasonic Super Active TAWERS and a YT-CET351 Active torch with synergic welding mode capability. A controlled-short circuit mode has been selected that uses a push-pull motion of the wire to ensure a stable metal transfer during welding. LCS plates, 25 mm thick, were used as substrates and fixed to the external manipulator for the fabrication of wall samples and the demonstrator/use case. The steel plates were cleaned and brushed before manufacturing for each configuration to remove any impurities and grease that may interfere with the deposition.

A monitoring system, the MaxQ system, is employed to ensure quality control during manufacturing, aiming to reduce the product's final cost by minimizing unacceptable manufacturing errors. The electrical parameters can vary during deposition due to local variation in the resistance, and contact-tip to work distance (CTWD). The MaxQ system is equipped with high-resolution sensors capable of recording the current and voltage with a sampling rate up to 50 kHz. The CTWD is adjusted between layers by projecting the programmed weld points onto a reconstructed surface derived from interlayer surface scanning. This surface

reconstruction is performed using a 3D scanner mounted on the robot arm adjacent to the welding torch. Maintaining a constant CTWD during welding ensures a stable deposition process, resulting in a high-quality build-up.

The manufacturing constraint and material availability in this study led to the selection of ER70S-6 wire, according to AWS A5.18, to replace the LCS used for the calculation/optimization/design part. The selected HSLA steel is an ER100S-G wire, according to AWS A5.28. The wire diameter was 1.2 mm, and the typical chemical composition and mechanical properties can be found in Table 2 and are comparable to S460 and S690 steels. In addition, Ferromaxx® Plus (68%Ar 20%He 12%CO2) gas mixture from AirProducts was used as shielding gas with a flow rate of 20 L/min, a nozzle diameter of 16 mm, and the CTWD was set to 15 mm. Similar interpass temperature of 150 °C was used and measured using a thermal camera, selecting the highest recorded temperature from an 80×80 pixel measurement area.

Focusing on the manufacturing strategy, the demonstrator was reduced to half its size prior to manufacturing, as depicted in Fig. 4a. The motivation for scaling down was a reduction in manufacturing time while preserving the complexity of the part. This is in line with the main focus of the study on digital design-to-manufacturing workflows for large-scale complex demonstrators. The contour of the part's CAD surfaces was used as the centerline for the toolpaths, resulting in half of the weld beads extending outside of the surfaces. This approach is justified by the high roughness level typically associated with DED-ARC. However, this deposition strategy may affect the final part's mass and potential deformation behavior. This issue will be explored further in the CAD to part comparison section.

Prior to part manufacturing, two major steps were considered: (i) parameter development and (ii) pre-printing of local area representative of the challenging sectors which might raise concerns during manufacturing. Regarding the parameter selection, the wire feed speed (WFS) was 5 m/min and travel speed 700 and 800 mm/min for the ER70S-6 and ER100S-G, respectively, as shown in Table 3. The WFS was set and is dependent on the current

<table><caption>**Table 2** Typical wire chemical composition and mechanical properties</caption><thead><tr><th colspan="7">Filler metal typical chemical analysis</th></tr><tr><th></th><th>C</th><th>Si</th><th>Mn</th><th>Ni</th><th>Cr</th><th>Mo</th></tr></thead><tbody><tr><td>ER70S-6-S460 -wt %</td><td>0.10</td><td>1.00</td><td>1.70</td><td>-</td><td>-</td><td>-</td></tr><tr><td>ER100S-G-S690-wt %</td><td>0.08</td><td>0.60</td><td>1.70</td><td>1.50</td><td>0.20</td><td>0.50</td></tr><tr><td colspan="7">Typical mechanical properties acc. to EN ISO 15792-1</td></tr><tr><td></td><td>Yield strength-Rp0.2</td><td colspan="3">Tensile strength-Rm</td><td colspan="2">Elongation (L0=5d0)</td></tr><tr><td>ER70S-6-S460</td><td>480</td><td colspan="3">580</td><td colspan="2">24</td></tr><tr><td>ER100S-G-S690</td><td>720</td><td colspan="3">780</td><td colspan="2">16</td></tr></tbody></table>
<table><caption>**Table 3** Selected printing parameters</caption><thead><tr><th></th><th>Heat input (kJ/mm)</th><th>WFS (m/min)</th><th>Current (I)/voltage (V)</th><th>TS (mm/min)</th><th>Interpass temperature (°C)</th></tr></thead><tbody><tr><td>ER70S-6</td><td>0.27</td><td>5</td><td>175/18.0</td><td>700</td><td>150</td></tr><tr><td>ER100S-G</td><td>0.16</td><td>5</td><td>170/16.1</td><td>800</td><td>150</td></tr></tbody></table>

and voltage in the welding machine technology employed in this study. The parameters developed affect the layer width and height essential for the manufacturing process.

The toolpath proposed to manufacture the demonstrator has a nominal bead width of 6.5 mm and a layer height of 2 mm. These values were obtained from initial trials and were used for the generation of the toolpath. Figure 4b illustrates the two deposition strategies used in this study, which are compared in the characterization section (“Mechanical characterization: hardness, tensile, and impact properties” section). These toolpaths consist of longitudinal weld beads with closed loop circular beads on one wall and square form spiral strategy on the other one. All layers start from the external surface toward the center of the wall. The main variation between the two considered techniques is the avoidance of intermediate arc start/stop with the spiral toolpath compared with the concentric paths within each layer, locations commonly associated with structural defects.

The manufacturing process plan and the optimization of the toolpath design were crucial to identifying the characteristic and challenging areas of the part. Given that the bogie includes specific features such as overhangs, trials were necessary, as depicted in Fig. 4c. The trial parts produced, representative of the real configuration, have been produced and served as validation of the previously mentioned parameters.

The path plan was generated using the Additive plugin from Autodesk Powermill software. The toolpath followed the volume and contour of the final part. Figure 5a shows the three sub-sections proposed as the build strategy required to divide the part into three sections named (i) base square, (ii) stiffener, and (iii) rib structure. As described by Xiong et al. [40], and seen in Fig. 5b, the path planning strategy considers the overlapping aspect to promote smooth and flat layers. The weld bead width directly influences the distance between beads and, consequently, the overall tool pathing approach. In Fig. 5c, the base square and stiffener, along with the purple sections of the rib structure, are fabricated using low carbon steel. The green sections of the rib structure are constructed with HSLA steel, requiring a wire change during the deposition of these subsections.

## 2.4 Mechanical characterization: multi-material characterization

Figure 6 displays the manufacturing configuration and dimensions of the considered sample walls. The two

<figure><img src="image_10.png" /><figcaption>**Fig. 5** Path planning description per section and manufacturing process plan: a three-step deposition strategy, b schematic representation of overlapping bead depositions [40] Fig. 1, c multi-material deposition sequence</figcaption></figure>

<figure><img src="image_11.png" /></figure>
<figure><img src="image_12.png" /><figcaption>**Fig. 6** DED-ARC-produced walls and extracted samples for mechanical and microstructural characterization: a wall configuration as manufactured, b sample extraction plan on the considered walls and toolpath strategies</figcaption></figure>

materials are deposited alternately, with ER70S-6 in orange and ER100S-G in yellow as shown in Fig. 6a. This multi-material deposition sequence produces wall samples representative of the optimized bogie. The interfaces are characterized as ER100S-G/ER70S-6 and ER70S-6/ER100S-G. As indicated in Fig. 6b, two toolpath strategies are examined. One configuration is produced using the spiral deposition strategy, named as Block A, and its results are compared with those of the alternative strategy, concentric toolpath, named as Block B.

The multi-material approach includes interfaces between the two materials. As the melt pool partially remelts the underlying material, a partial mixture of the two materials occurs (dilution). This mixing can potentially affect the properties at these interfaces and alter the chemical composition of each deposited layer. Consequently, particular attention is paid to the interfaces through micro-hardness mapping transitioning from one material to the other. The specimens were extracted via machining following the plan proposed in Fig. 6b. The samples were ground, polished, and etched to precisely characterize the interface locations. A Vickers micro-hardness test with 200 gF load (1.96 N), a 10-s dwell time, and an indentation spacing of 300 µm, was employed

accordance with the EN ISO 6507–1 standard [41]. Tests were made using a NEMESIS 5100G2 hardness testing unit. For microstructural characterization, transversal cross-sections were mounted and then ground and polished using a Struers Tegramin automatic grinding machine, and 2% Nital etchant (Nitric and Ethanol acid) was applied for optical characterization. Observations were made using a Leica Dmi8 optical microscope.

Longitudinal tensile test specimens were separately extracted from the ER70S-6 and ER100S-G areas, with each area providing two specimens within the thickness—one from the center and one from the side of the wall. Transversal tensile test specimens, which included both considered materials with the interface positioned at the center of the specimen, were also extracted. Four samples per investigated wall were taken, following a similar approach to that used for the longitudinal tensile test specimens. The shape of the test specimens was designed according to ISO 6892–1 standard [42].

The tensile tests were conducted using an Instron 8800 servo hydraulic universal testing machine, equipped with a 100 kN load cell and a 25-mm gauge length extensometer (used in the first half of the tests but omitted in the second half to increase the field of view for the optical deformation
tracking). The displacement rate during the experiments was set to 0.02 mm/s resulting in nominal strain rate of 0.0005/s.

Specimen deformation was tracked optically based on the digital image correlation (DIC) method with the LaVision StrainMaster CompactDIC system operating at 2 Hz. Prior to the experiments a stochastic speckle pattern was laid on the specimen surface using black and white spray paint. The acquired images were analyzed with LaVision Davis 10 software, which was used to calculate the displacement and strain fields of the specimen surface and further processed to obtain so-called virtual extensometer data, i.e., the relative change of distance between two points set to the gauge area.

For selected experiments, the specimens were equipped with longitudinal strain gauges (one on each side) to accurately measure the material Young's modulus. The measurements were carried out so that first the material was loaded and unloaded in the elastic region, i.e., up to 200 MPa and 400 MPa for the ER70S-6 and ER100S-G, respectively. In these tests, linear response with minimal

hysteresis was observed indicating linear elastic material behavior. The tensile tests were subsequently carried out with the strain gauges still attached to the specimen. As expected, prior to yielding, the data agreed with the previous cyclic tests carried out in the elastic region. The results of the measurements are presented in the next chapter (Table 4).

Impact tests were carried out on a Zwick-Roell Pendulum Impact Tester HIT450P system, using a V-notch impact test specimens according to ISO 148–1 standard [43], and the specimens were machined to dimensions of 55 × 10 × 10 mm. Two samples V-notch orientations were investigated on the two considered materials with longitudinal orientation symbolized as Z-orientation and transversal symbolized as Y-orientation. Three testing temperatures of 20 °C, –20 °C, and –40 °C were selected to identify the ductile-to-brittle transition temperature (DBTT) of the steel.

<figure><img src="image_13.png" /><figcaption>**Table 4** Mechanical properties derived from experimental characterization of material, used in verification FEA simulation</figcaption></figure>

<table><thead><tr><th></th><th>Name</th><th>Material</th><th>Direction</th><th>Location</th><th>Yield strength (MPa)*</th><th>UTS (MPa)</th><th>Young's mod. (GPa)**</th></tr></thead><tbody><tr><td rowspan="6">Block B<br/>-<br/>concentric toolpath</td><td>B1.1</td><td>ER70S-6/ER100S-G</td><td>Transversal</td><td>Edge</td><td>425</td><td>527</td><td>-</td></tr><tr><td>B1.2</td><td>ER70S-6/ER100S-G</td><td>Transversal</td><td>Center</td><td>498</td><td>589</td><td>-</td></tr><tr><td>B2.1</td><td>ER70S-6/ER100S-G</td><td>Transversal</td><td>Edge</td><td>408</td><td>524</td><td>-</td></tr><tr><td>B2.2</td><td>ER70S-6/ER100S-G</td><td>Transversal</td><td>Center</td><td>482</td><td>573</td><td>-</td></tr><tr><td>B9.1</td><td>ER70S-6</td><td>Longitudinal</td><td>Edge</td><td>468</td><td>544</td><td>-</td></tr><tr><td>B9.2</td><td>ER70S-6</td><td>Longitudinal</td><td>Center</td><td>508</td><td>573</td><td>208.90</td></tr><tr><td rowspan="6">Block A<br/>-<br/>spiral toolpath</td><td>B10.1</td><td>ER100S-G</td><td>Longitudinal</td><td>Edge</td><td>737</td><td>807</td><td>-</td></tr><tr><td>B10.2</td><td>ER100S-G</td><td>Longitudinal</td><td>Center</td><td>825</td><td>870</td><td>204.50</td></tr><tr><td>A1.1</td><td>ER70S-6/ER100S-G</td><td>Transversal</td><td>Edge</td><td>425</td><td>526</td><td>-</td></tr><tr><td>A1.2</td><td>ER70S-6/ER100S-G</td><td>Transversal</td><td>Center</td><td>416</td><td>527</td><td>-</td></tr><tr><td>A2.1</td><td>ER70S-6/ER100S-G</td><td>Transversal</td><td>Edge</td><td>397</td><td>521</td><td>-</td></tr><tr><td>A2.2</td><td>ER70S-6/ER100S-G</td><td>Transversal</td><td>Center</td><td>406</td><td>530</td><td>-</td></tr><tr><td>A9.1</td><td>ER70S-6</td><td>Longitudinal</td><td>Edge</td><td>430</td><td>515</td><td>-</td></tr><tr><td>A9.2</td><td>ER70S-6</td><td>Longitudinal</td><td>Center</td><td>427</td><td>528</td><td>206.70</td></tr><tr><td>A10.1</td><td>ER100S-G</td><td>Longitudinal</td><td>Edge</td><td>708</td><td>808</td><td>-</td></tr><tr><td>A10.2</td><td>ER100S-G</td><td>Longitudinal</td><td>Center</td><td>717</td><td>832</td><td>202.00</td></tr></tbody></table>

<figure><img src="image_14.png" /></figure>

<footnote>* Upper yield point was used as a measure of material yield strength</footnote>

<footnote>** Young's modulus was measured only in selected tests</footnote>

<footnote>*Upper yield point was used as a measure of material yield strength</footnote>

<footnote>**The Young's modulus was measured only in selected tests</footnote>
# 3 Results and discussion

## 3.1 Mechanical characterization: hardness, tensile, and impact properties

Micro-hardness measurements were performed on the cross-sections of the sample walls fabricated using the concentric toolpaths (Block B), used for the manufacturing of the bogie part. A focus is placed on the interfaces between ER70S-6/ER100S-G and ER100S-G/ER70S-6 with the superimposed two-dimensional mapping presented in Fig. 7. The examination of the layers within the analyzed surface allows for a clear comparison between the materials and the walls, facilitating the identification of the layers across the different areas. Hence, the ER70S-6/ER100S-G configuration (meaning that ER70S-6 is deposited on the previously deposited ER100S-G) exhibits micro-hardness values of 289 ± 13, 220 ± 9, and 193 ± 7, for the ER100S-G, diluted layer, and ER70S-6, respectively. Alternatively, the ER100S-G/ER70S-6 configuration shows micro-hardness values of 199 ± 9, 272 ± 13, and 290 ± 24, for the ER70S-6, diluted layer, and ER100S-G,

respectively. The behavior of the diluted layers in each configuration consistently exhibits either a decrease or increase in hardness for the ER70S-6/ER100S-G and ER100S-G/ER70S-6 combinations, respectively, due to the partial remelting of the previous layer. This pattern is advantageous at the part level, and the mixing of elements within the melt pool does not result in the formation of harder (and therefore more brittle) microstructures.

Figure 7 shows the microstructural overview of ER100S-G deposited on the previous layer ER70S-6 and does not highlight critical defects (in ER100S-G/ER70S-6 configuration). Disperse dark spots can be noticed and are interpreted as oxides. Prior austenite grain boundaries are decorated with allotriomorphic ferrite on the ER70S-6 side of the specimen, owing to the lower carbon equivalent of the latter, which reduces hardenability, enhances ferrite formation kinetics, and thus promotes allotriomorphic ferrite formation.

Figure 8 shows a more detailed examination of the two materials revealing their microstructural differences. In Fig. 8a, the ER70S-6 is dominated by a fine grain polygonal ferritic microstructure, while the minor presence of pearlite

<figure><img src="image_15.png" /><figcaption>**Fig. 7** Superimposed micro-hardness HV0.2 color maps on the examined interfaces with a corresponding microstructural view</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>**Fig. 8** Higher magnification light optical microscopy of a ER70S-6, b ER100S-G, and c the diluted layer of ER100S-G partially remelting deposited ER70S-6</figcaption></figure>

is also noticed (dark spotted areas). The formation of pearlite is a manifestation of exposure to high temperature for a prolonged time interval or accumulated exposure over several reheating passes. On the other hand, in Fig. 8b, the ER100S-G is a very fine acicular microstructure, characteristic of high strength low alloy steel. The microstructure consists primarily of acicular ferrite, with the minor presence of bainite (dark brown) and martensite (light brown). The bainitic/martensitic areas have a scattered morphology but seem to follow certain flow lines, which is an indication of elemental micro-segregation. Limited recrystallization is observed, indicated by larger ferrite grains (white), which is typical for weld metal exposed to high temperatures after solidification. This is likely due to the exposure at elevated temperatures during subsequent heat cycles caused by the deposition of additional layers. These microstructural features for HSLA steels have been well documented in literature for regular welding [44] but also more recently for DED-arc, and the observations in this work are consistent with literature findings [45].

The interface between the two materials, Fig. 8, is also important to analyze because, as previously discussed, mixing different consumables can result in highly undesirable phases (either hard and brittle or very soft). These phases must be strategically avoided during multi-material

DED-ARC. The optical microscopy of the cross-section between layers reveals minor effects of the mixing that need to be taken into account when designing the deposition process. On the ER70S-6 side of the build, close to the fusion line, prior austenite grain boundaries are decorated with allotriomorphic ferrite. This effect may be due to the heat generated during subsequent deposition cycles or local modification in alloy composition, as ferrite-forming alloying elements may be promoted at these locations compared to austenite stabilizers. On the ER100S-G side, some darker zones can be observed, likely caused by local increased carbide formation due to the segregation of carbide-forming alloying elements. This segregation can be more pronounced due to the mixing of the two materials.

Figure 9 illustrates the engineering tensile stress–strain curves for all the specimens extracted from the walls fabricated with spiral (Fig. 9a) and concentric (Fig. 9b) toolpaths. Engineering stress–strain curves were selected over true stress–strain curves as they enable easier comparison with literature values (e.g., UTS) and provide relevant information also on the ductility of the material, including uniform elongation and fracture elongation. From selected locations in the wall, two specimens are taken at different depths through the thickness: one from the wall’s edge (1) region and another from the central (2) region. The

<figure><img src="image_17.png" /></figure>
<figure><img src="image_18.png" /><figcaption>**Fig. 9** Stress–strain curves of the tensile test specimens for the walls’ edge (region 1) and walls’ central part (region 2) with a spiral toolpath and b concentric toolpath</figcaption></figure>

stress–strain curves were constructed by using the DIC optical extensometer data for strain and load cell data for stress. As can be seen, in all cases, ductile behavior is observed, and the strength levels are close to the assumptions made in the initial design phase based on the wire’s typical mechanical properties (Table 2).

For the concentric toolpath strategy (Block B), there is a systematic difference in strength (50–100 MPa) between the specimens extracted on the sides of the wall and those extracted from the central region, as observed in Table 4. In the case of the spiral toolpath wall (Block A), the difference is notably smaller (10–25 MPa) (Table 4). This phenomenon is likely caused by differences in local material temperature history between the two toolpath strategies. In the spiral toolpath strategy, there is steady heat input to the work piece which leads to spatially uniform local temperature histories. This is expected to lead to uniform microstructure and hence explain the similar tensile properties between the edge and central regions. In contrast, in the concentric toolpath strategy, there is intermediate cooling between annular beads, which can result in significant variations in local temperature histories. This may lead to differences in the local material microstructure [45]. The difference in strength between the edge and central tensile specimens indeed suggests a heterogeneity in the microstructure created with the concentric toolpath strategy. However, since only material strength is affected and sufficient material ductility is observed in all cases, this effect can be considered of secondary importance and is not studied further in the context of this paper.

The Young’s moduli were determined from the specimens equipped with strain gauges. The values were found to agree with the commonly available data on steels, as shown in Table 4.

In the transversal test specimens composed of the two studied materials with the interface at the center of the specimen, as shown in Fig. 10a, plastic deformation and final fracture consistently occurred in the weaker material, ER70S-6, and clearly away from the interface. The mechanical properties are similar to the ones observed in the longitudinal specimens of ER70S-6 in the case of yield and ultimate strength. In terms of material ductility, there is an apparent decrease in the case of multi-material specimens in Fig. 9. This is, however, an artifact caused by the chosen specimen geometry and strain measurement method. Since the (optical) extensometer measures the strain as an average across the entire gauge section, the non-deforming high strength ER100S-G section decreases the total strain value. In contrast, when the strain is measured only from the deforming ER70S-6 section with a shorter optical extensometer, Fig. 10b, similar ductility as in the mono-material specimen is observed, as expected. The constant stress observed, particularly visible in ER70S-6, is attributed to the sharp yield point and Lüders band propagation. This behavior results from the interaction between free interstitial atoms, such as carbon and nitrogen, and mobile dislocations within the lattice. Such phenomena are characteristic of mild steels, as noted by Bhadeshia et al. [46]. In general, the results for the multi-material specimens confirm that acceptable material behavior is obtained also for the ER100S-G/ER70S-6 interface with the deposition parameters used.

Tables 5 and 6 show the impact test results for ER70S-6 and ER100S-G steels across three temperatures (+20 °C, –20 °C, and –40 °C) and two V-notch orientations (longitudinal (Z) and transversal (Y)) to the manufacturing plane. Table 5 lists the average values of each configuration, calculated from the two tested specimens per configuration.
<figure><img src="image_19.png" /><figcaption>**Fig. 10** a Example of optical footage taken during the test of the specimen "A1_2" with superimposed results of the DIC analysis (axial strain), b stress-strain curves constructed for the test of "A1_2" so that the optical extensometer data is taken across the whole gauge</figcaption></figure>

<figure><img src="image_20.png" /></figure>

section and across the ER70S-6 section of the specimen only. In addition, the data from single material ER70S-6 specimen (A9_2) is shown for comparison

<table><caption>**Table 5** Impact energy at three temperatures +20, −20, and −40 °C on ER70S-6 and ER100S-G materials: comparison between the impacts energies at +20 °C, −20 °C, and −40 °C on the concentric toolpath strategy</caption><thead><tr><th>Temperature (°C)</th><th>Material</th><th>Orientation</th><th>Impact energy (J)—average</th><th>Standard deviation (J)</th></tr></thead><tbody><tr><td>+20</td><td>ER100S-G</td><td>Z</td><td>128.95</td><td>9.04</td></tr><tr><td>+20</td><td>ER100S-G</td><td>Y</td><td>113.44</td><td>2.86</td></tr><tr><td>+20</td><td>ER70S-6</td><td>Z</td><td>203.16</td><td>24.88</td></tr><tr><td>+20</td><td>ER70S-6</td><td>Y</td><td>194.83</td><td>6.16</td></tr><tr><td>−20</td><td>ER100S-G</td><td>Z</td><td>108.97</td><td>3.60</td></tr><tr><td>−20</td><td>ER100S-G</td><td>Y</td><td>98.56</td><td>7.58</td></tr><tr><td>−20</td><td>ER70S-6</td><td>Z</td><td>165.39</td><td>9.29</td></tr><tr><td>−20</td><td>ER70S-6</td><td>Y</td><td>166.96</td><td>1.82</td></tr><tr><td>−40</td><td>ER100S-G</td><td>Z</td><td>97.77</td><td>3.82</td></tr><tr><td>−40</td><td>ER100S-G</td><td>Y</td><td>81.16</td><td>1.67</td></tr><tr><td>−40</td><td>ER70S-6</td><td>Z</td><td>148.03</td><td>20.25</td></tr><tr><td>−40</td><td>ER70S-6</td><td>Y</td><td>126.56</td><td>39.81</td></tr></tbody></table>

At +20 °C, ER70S-6 exhibits a high impact energy on the longitudinal and transversal orientation, demonstrating good impact toughness. In contrast, ER100S-G shows a lower energy absorption on the longitudinal and transversal orientation.

At lower temperatures, both materials show a reduction in impact energy. The V-notch orientation results suggest a microstructural anisotropy of the deposited materials as the direction of the notch affects the impact energy. Additionally, ER70S-6 shows superior results at higher temperatures, but the gap between ER70S-6 and ER100S-G reduces as temperatures decreases as visualized in Fig. 11.

The chemical composition of ER100S-G and therefore microstructural differences with ER70S-6 justify these results. Despite decrease of values at lower temperatures, ER70S-6 still maintains relatively high impact toughness at lower temperatures compared to ER100S-G which shows lower impact energy, especially at lower temperatures. However, no clear brittle behavior is observed, and the materials exhibit a mixed ductile/brittle behavior at −40 °C. The combined use of ER70S-6 and ER100S-G in a multi-material approach should consider the impact toughness variations at the part level. The placement of each material should be based on their properties and the specific requirements of different regions within the part. Besides, Table 6 details the average values for the different toolpath strategies. No significant differences in impact toughness are observed, regardless of the configuration, indicating the limited impact of the considered toolpath in this experiment.
## 3.2 Shape optimization results and design verification

The bogie is used as a demonstrator for re-design and optimization process. The shape optimization followed a three-step workflow as described in Fig. 2. Figure 12 shows the

design evolution from the original design (obtained from the GrabCAD repository [35]) to topology optimized design and ultimately to DED-ARC-adapted topology optimized design. Similar boundary conditions (loads, constraints) were applied for topology optimization and static analysis of the three designs shown in Fig. 12. Applying similar boundary

<table><caption>**Table 6** Impact energy at three temperatures +20, –20, and –40 °C on Er70S-6 and ER100S-G materials: comparison between the impacts energies at +20 °C on the two considered toolpath strategies</caption><thead><tr><th rowspan="2">Temperature (°C)</th><th rowspan="2">Material</th><th rowspan="2">Orientation</th><th colspan="2">Block B</th><th colspan="2">Block A</th></tr><tr><th>Impact energy (J), average</th><th>Standard deviation (J)</th><th>Impact energy (J), average</th><th>Standard deviation (J)</th></tr></thead><tbody><tr><td>+20</td><td>ER100S-G</td><td>Z</td><td>128.95</td><td>9.04</td><td>124.34</td><td>14.14</td></tr><tr><td>+20</td><td>ER100S-G</td><td>Y</td><td>113.44</td><td>2.86</td><td>111.17</td><td>2.60</td></tr><tr><td>+20</td><td>ER70S-6</td><td>Z</td><td>203.16</td><td>24.88</td><td>211.20</td><td>1.71</td></tr><tr><td>+20</td><td>ER70S-6</td><td>Y</td><td>194.83</td><td>6.16</td><td>193.60</td><td>10.04</td></tr></tbody></table>

<figure><img src="image_21.png" /><figcaption>**Fig. 11** Visualization of the impact test results on the two considered materials ER70S-6 and ER100S-G</figcaption></figure>

<table><caption>**Fig. 12** Comparison of results from original design, topology optimized, and DED-ARC-adapted design for bogie case study</caption><thead><tr><th></th><th>Original design<br>Single material</th><th>Topology optimized<br>Single material</th><th>DED-ARC-adapted<br>Multi-material design<br>(LCS and HSLA)</th><th>As-manufactured<br>Multi-material part<br>(ER70S-6 and ER100S-G)</th></tr></thead><tbody><tr><td>Mass</td><td>178.91 kg</td><td>285.74 kg</td><td>170.67 kg</td><td>169.59 kg</td></tr><tr><td>Maximum stress</td><td>138.61 MPa</td><td>68.11 MPa</td><td>457.02 MPa</td><td>457.54 MPa</td></tr><tr><td>Elastic limit</td><td>207 MPa</td><td>207 MPa</td><td>207 MPa and 720 MPa</td><td>468 MPa and 737 MPa</td></tr><tr><td>Safety factor</td><td>1.49</td><td>3.04</td><td>1.03</td><td>1.61</td></tr><tr><td>Max. displacement</td><td>0.54 mm</td><td>0.61 mm</td><td>2.75 mm</td><td>2.72 mm</td></tr></tbody></table>
conditions allows to compare the performance of these three designs based on maximum Von-mises stress, safety factor, and maximum displacement. Figure 12 also shows result of validation simulation of final part with the exact material used in the manufacturing. The original geometry of the bogie is manufactured by casting and machining and the total mass is estimated to be 178.91 kg. The result of static analysis exhibits a safety factor of 1.49 for the original design. The maximum stress and displacement are calculated to be 138.60 MPa and 0.54 mm, respectively.

A bulk geometry of original design is considered for a single material (LCS) topological optimization. The results of the simulation suggest a considerable increase in mass of the part from 178.91 kg to 285.74 kg, due to the assumptions of preserved areas and target mass percentage on a bulk geometry. This design undergoes a maximum stress of 68.11 MPa and a maximum displacement 0.61 mm. The topology optimized part at this stage is still interpreted as an over-design, since the safety factor is 3.04. Furthermore, the geometry is not optimal for manufacturing with DED-ARC. Hence, as described in the re-design workflow shown in Fig. 2, a series of modifications have been performed to enhance the DED-ARC manufacturability, increase the stiffness, and further reduce the mass of the bogie. For instance, the size of some features (wall thickness) is locally reduced, and stiffeners are added to increase part stiffness, minimize the deformation during and after manufacturing, and increase the safety factor. The wall thickness of certain geometric features was significantly reduced, and hence to

accommodate the higher stress in those areas, LCS was substituted with HSLA steel. The precise placement of HSLA, as depicted in Fig. 5, is influenced by the layer-by-layer DED-ARC manufacturing process, and consequently, a flat transition between materials has been chosen.

As shown in Fig. 13, three sets of stiffeners were considered, namely tubular shape, oblong shape, and triangular shape. Figure 13 briefly illustrates the evolution of adding stiffeners and their effect on the performance of the bogie according to maximum stress, maximum displacement, and safety factor using static FEM simulation. A set of curved tubular stiffener has been symmetrically added to the structure, allowing for wall thickness reduction while increasing rigidity. This shape was chosen for its manufacturing advantage, as it facilitates deposition compared to traditional steel beam shapes. Although tubular stiffeners improved the performance, adding an oblong-shaped stiffener on the bottom of the bogie was the most influential element for increasing safety factor and significantly decreasing maximum stress and maximum displacement. As Fig. 13 illustrates, the safety factor was increased from 0.31 for the design without stiffener to 1.03 for the design with tubular- and oblong-shaped stiffeners. Adding these two stiffeners leads to decrease of maximum displacement from 7.28 to 2.75 mm. A set of triangular stiffeners was added to provide more structural integrity supports to the functional assembly surfaces of the bogie where the constraints are applied. Consequently, adding these three sets of stiffeners increases the total mass of the

<figure><img src="image_22.png" /><figcaption>**Fig. 13** Adding stiffeners and their effect on performance of the bogie</figcaption></figure>

<figure><img src="image_23.png" /></figure>
bogie by 16.91 kg from 153.76 kg for the design without stiffeners to 170.67 kg for the design with these three sets of stiffeners.

Moreover, as seen in Fig. 12, a tear drop-shaped cutout is strategically placed on the bogie in the HSLA areas enhancing both the mass reduction and the manufacturability using the DED-ARC process. These design modifications lead to a DED-ARC-adapted multi-material topology optimized bogie shown in Fig. 12. As a result of design modifications, the mass of the bogie is reduced to 170.67 kg. The result of static analysis demonstrates a safety factor of 1.03 and a maximum displacement of 2.75 mm for the DED-ARC-adapted design.

The last step of the shape optimization workflow shown in Fig. 2 is dedicated to the FEA static validation simulation by considering the material used in the manufacturing phase and mechanical properties derived from the material characterizations performed in this study. The earlier design phases relied on materials (LCS, HSLA) referenced from the literature, which did not accurately represent the actual material characteristics as deposited with the DED-ARC process. By introducing this additional step, the goal is to ensure the part reflects the appropriate performance with the correct material properties.

Figure 14 presents the result from a FEA validation simulation conducted using Fusion360. The loads and boundary conditions applied in this simulation are consistent with those used in the topology optimization process. As shown in Fig. 14a, b, the maximum Von-Mises stresses and, consequently, the minimum safety factor occur in sections made of high-strength steel, confirming the necessity of using multiple materials. The results indicate the minimum safety factor of 1.61 which is higher than the safety factor of 1.49 in the original design (see Fig. 12 and Fig. 14). Figure 14c presents the displacement under the specified loads and boundary conditions. The maximum displacement observed is 2.72 mm. Figure 14d shows the contact pressure at the material interfaces. The use of two materials necessitates checking the interface stresses to ensure they are below the yield strength of both materials to prevent interface failure. The results show that the maximum contact pressure (144.56 MPa) is lower than the yield strength of ER70S-6 and ER100S-G (see Table 4). Note that the simulations shown in Figs. 12, 13, and 14 were conducted using a uniform mesh size of 10 mm. This mesh size was determined as optimal based on a mesh sensitivity analysis, which demonstrated that a 10-mm mesh provides converged results while avoiding undesired increases in computation time.

<figure><img src="image_24.png" /><figcaption>Fig. 14 The results of finite element analysis, considering multi-material bogie with mechanical properties derived from experimental characterization (materials: ER70S-6 and ER100S-G), a Von-Mises stress, b displacement, c safety factor, and d contact pressure</figcaption></figure>
**3.3 Manufacturing process plan and CAD/CAM**

Figure 15 presents the manufactured part, shown in Fig. 15a, and its intermediate construction stages, depicted in Fig. 15b. The pre-production of the representative sub-part, as described in Fig. 4c (the “DED-ARC: machine, materials, and manufacturing process plan” section), created the need to modify the shape of the part to enhance manufacturability and reduce the risk of failure. The teardrop shape intended to reduce the mass of the bogie did not facilitate optimal deposition as designed, and a local melt pool collapsed at the overhang areas was observed. The variation in overhang, caused by the curved shape of the teardrop, is believed to be the primary cause of this issue. To control the manufacturing quality, it is crucial to consider fixed manufacturing parameters, and consistent overhang gap between layers.

Therefore, the teardrop shape has been replaced by a tri-
angular shape, as shown in Fig. 15a. This modification of
the part ensures consistent manufacturing but involves the
addition of extra material to an optimized part, thereby alter-
ing its characteristics and mass. For industrial application,
this modification would require additional steps, including
updating the design and post-treatments like machining to
restore the part to its original shape. However, these steps
would impact the final cost and feasibility of manufacturing
the part using AM.

Future research could concentrate on optimizing the
overhang section while maintaining a consistent deposition

rate. A similar approach, as detailed by Danielsen Evjemo
et al., could involve focusing on local parameter optimiza-
tion and the orientation of both the torch and part during
manufacturing [47]. Additionally, non-planar path planning
methods, as detailed by Diourté et al. with a “Continuous
Three-dimensional Path Planning,” could serve as an alter-
native for ensuring good deposition between layers, among
other methods [48].

Overall, no significant irregularities were observed dur-
ing manufacturing, and the part was successfully processed.
The total manufacturing time was 70 h, including 17 h of arc
time, representing 24% of the overall time. This demonstra-
tor concept presents an interesting use case that could be
expanded to additional concepts and multi-material indus-
trial applications. The necessary design optimization for
manufacturing and characterization are essential steps given
the advancement of AM and to promote the adoption of such
innovative techniques by large corporations as well as small
and medium enterprises (SMEs).

**3.4 CAD to part comparison**

As detailed in the “Shape optimization, (re)design workflow,
and CAD to part comparison” section, the scan of the part
was imported and aligned with the CAD model in the GOM
inspect software. The original scan of the bogie was saved in
STL format, containing 355,842 triangular elements (facets)
which represented the 3D geometry of the printed bogie.

<figure><img src="image_25.png" /><figcaption>**Fig. 15** a Manufactured part and b its intermediate construction stages</figcaption></figure>

<figure><img src="image_26.png" /></figure>
The mass of the final scaled designed part (scaled down by a factor of 0.5 in all directions) is expected to be 27.8 kg. A total mass of 46.4 kg is deposited during manufacturing. The mass of near net shape part after removing the excess material from the substrate is calculated to be 41.7 kg. Comparing the total mass of the near net shape part (41.7 kg) and the mass of the scaled designed part after manufacturing (27.8 kg) indicates that 50% of extra material is deposited to provide machining allowance.

As illustrated in Fig. 16, five planes were constructed on the functional surfaces. Table 7 provides details of the number of points on which the planes are defined, flatness, and minimum and maximum deviation of the constructed planes. Further analysis of the GD&T analysis is performed using these planes in virtual software environment.

Figure 16 illustrates a comparative analysis of the 3D scan of the manufactured bogie against the initial CAD model. The analysis indicates that there is material allowance for postprocess machining all over the bogie printed by DED-ARC. The values in the Fig. 16a show the distance between the surface of the scan geometry and the CAD nominal surface in XY plane. The larger values indicate a larger

<table><caption>**Table 7** Characteristics of the constructed planes on the scanned part</caption><thead><tr><th>Plane #</th><th>Number of points</th><th>Min. deviation (mm)</th><th>Max. deviation (mm)</th><th>Flatness</th></tr></thead><tbody><tr><td>Plane 1</td><td>1163</td><td>-3.71</td><td>0.93</td><td>1.54</td></tr><tr><td>Plane 2</td><td>1114</td><td>-0.70</td><td>0.79</td><td>1.33</td></tr><tr><td>Plane 3</td><td>880</td><td>-0.51</td><td>0.58</td><td>1.06</td></tr><tr><td>Plane 4</td><td>607</td><td>-0.55</td><td>0.52</td><td>0.98</td></tr><tr><td>Plane 5</td><td>3756</td><td>-0.67</td><td>0.84</td><td>1.45</td></tr></tbody></table>

material allowance for postprocess machining. The results of the analysis in Fig. 16 confirm that the manufactured bogie is symmetrical. Figure 16b also visually represents the machining allowance in detailed view of two bogie features.

Geometrical tolerancing of the bogie’s key features are further analyzed using GOM inspect software. Note that this geometrical tolerancing analysis was performed on the scan of the part prior to machining. The analysis was performed on the planes defined on functional surfaces (features) as shown in Fig. 2. The flatness of all five planes was calculated and reported in Fig. 16c. The minimum flatness tolerance

<figure><img src="image_27.png" /><figcaption>**Fig. 16** Part scan to CAD comparison: a and b material allowance for postprocessing machining, c geometrical tolerancing analysis</figcaption></figure>
was achieved in plane 4 equal to 0.98, and the maximum flatness tolerance was measured to be 1.54 for plane 1. Plane 3 and Plane 4 exhibit slightly better flatness compared to plane 1, 2 and 5. The parallelism of two functional planes on the left and right end of the bogie (planes 1 and 2) in Fig. 16c was analyzed according to each other and according to a reference plane of the bogie (plane 5). The parallelism of plane 1 and plane 2 are 1.82 and 1.45 mm, respectively. This means that plane 1 is in between two virtual parallel planes with a distance of 1.82 mm between each other. This tolerance zone allows to achieve much better parallelism tolerance after postprocess machining. Similarly, the parallelism of plane 3 and plane 4 compared to each other and their perpendicularity compared to reference plane 5 were also calculated and reported in Fig. 16.

## 4 Conclusions

An increase in the utilization of wire arc directed energy deposition (DED-ARC) for industrial applications depends on the ability to precisely control the design of the manufacturing process to ensure part quality, reproducibility, material efficiency, and reliability. The research demonstrated that DED-ARC with multi-material approach can effectively produce complex and large-scale structural components such as a railway bogie. The implementation of a digital design-to-manufacturing workflow serves the process optimization.

The shape optimization, design verification process, and material characterization demonstrated the potential improvements in the structural performance and an alternative manufacturing method of a railway bogie from an industrialization perspective. Through the three-step workflow, the original topologically optimized design was transformed from a bulky geometry with a high safety factor and increased mass to a DED-ARC-adapted design. The final version reduced the mass by approximately 8.24 kg compared to the original cast part but also incorporated high-strength steel to enhance stiffness and manufacturability. The resulting DED-ARC-adapted design exhibited a safety factor of 1.03 and a maximum displacement of 2.75 mm, indicating an effective balance between mass reduction and structural robustness. These results support the effectiveness of combining topology optimization with DED-ARC constraints to produce components that are both lightweight and structurally reliable.

The primary contribution and novelty of this research are as follows:

* Developing and implementing a digital-design-to-manufacturing workflow to topologically re-design a structural bogie using multiple materials in a monolithic construct.

* Demonstrating that advanced manufacturing techniques, such as DED-ARC, must be combined and closely coupled with topology optimization workflows to make the manufacturing technique technically and economically feasible and improve traditional design to manufacturing workflows.

* Using the characterization of additional walls, processed with similar manufacturing parameters than the actual part, to assess about the attained mechanical properties. The average yield strength (YS) of ER70S-6 was $488 \pm 20$ MPa for concentric and $428 \pm 2$ MPa for spiral toolpaths, while ultimate tensile strength (UTS) reached $558 \pm 15$ MPa and $522 \pm 7$ MPa, respectively. For ER100S-G, YS averaged $781 \pm 44$ MPa (concentric) and $713 \pm 5$ MPa (spiral), with UTS at $839 \pm 32$ MPa and $820 \pm 12$ MPa. All samples exhibited ductile fracture. The concentric toolpath results were used for finite element validation, matching the toolpath used in part manufacturing.

* Ultimately, the manufacturing strategy and part-to-CAD comparison confirmed that the optimized CAD model was successfully able to produce the part. This validated the precision of key geometric features, ensuring that the part met the design specifications before the final machining process.

Hence, the secondary findings focusing on the material characterizations suggest that:

* The micro-hardness measurements at the interface between materials reveal that in both configurations ER70S-6/ER100S-G and ER100S-G/ER70S-6 show a decrease or increase in hardness at the diluted layer, both influenced by the partial remelting of the previous layer. The hardness values did not indicate the formation of harder microstructures, which is beneficial at the part level.

* Considering the impacts test results, ER70S-6 exhibits superior impact toughness compared to ER100S-G, but this advantage diminishes as the temperature decreases down to –40 °C. The materials maintain relatively high toughness and show mixed ductile/brittle behavior at –40 °C and minimum average values of $126.56 \pm 39.81$ J and $81.16 \pm 1.67$ J for ER70S-6 and ER100S-G, respectively.

* The interface between materials did not lead to major defects nor reductions in mechanical properties confirming the material selection as a good candidate for such applications.

* Tensile tests of the multi-material system showed fractures occurring in the weaker ER70S-6 material away from the interface. These results confirm the good mechanical performance of the deposited multi-mate-

<figure><img src="image_28.png" /></figure>
rial, with average YS values of 453 ± 38 MPa for the concentric toolpath samples and 411 ± 11 MPa for the spiral toolpath samples. Likewise, the average UTS values were measured at 553 ± 28 MPa and 526 ± 3 MPa for the concentric and spiral toolpath samples, respectively. These mechanical properties are comparable to those observed in the longitudinal specimens of ER70S-6 in terms of yield and ultimate strength.

* The toolpath strategy (spiral vs. concentric) has minimal impact on the hardness and grain size at the interface of the fabricated parts in ER100S-G/ER70S-6.

This research acknowledges limitations that require further studies. For example, this demonstrator topology optimized multi-material design cannot be used in direct industrial application. Hence, significant effort is needed to adapt this design for industrial use, requiring both static and dynamic mechanical simulations and validation experimental tests, including fatigue testing for component validation. Nevertheless, the analysis and redesign of the bogie, utilizing topology optimization and DED-ARC-specific adjustments, underscores the importance of a multi-material approach for enhancing performance. The current study used a linear elastic static model for topology optimization and simulating the structural behavior of the bogie. The future research should focus on dynamic analysis of the case study and its effect on topology optimization and performance results. The iterative design modifications, such as the addition of stiffeners and the substitution of low carbon steel with high-strength steel, led to a significant reduction in mass while maintaining acceptable levels of stress and deformation. The FEA validation further confirmed the theoretical robustness of the DED-ARC-adapted design, with contact pressures remaining below the yield strengths of both selected materials.

Future research should concentrate on concurrently addressing structural topology optimization and toolpath design including simultaneously design and manufacturing constraints. The research presented a sequential structural optimization followed with toolpath design seen as a separate step. This sequential workflow compromises the optimal manufacturability of parts and limits the development of additive manufacturing (AM). This study highlights the potential of integrated digital design and advanced manufacturing techniques combined with topology optimization workflows including multi-material systems to innovate and improve traditional manufacturing processes, paving the way for more efficient and resilient load-bearing mechanical components.

**Acknowledgements** The authors would like to acknowledge all the consortium partners from the Grade2XL project. Special thanks to RAMLAB staff for providing their manufacturing knowledge and capabilities. Additionally, the authors would like to thank Mohammad

Hallaji for his assistance with material characterization and Kati Mökkönen for carrying out the impact tests.

**Author contribution** Antoine Queguineur focused on conceptualization, investigation, methodology, validation, visualization, and writing—original draft. Amirmohammad Daareyni worked on formal analysis, investigation, software, visualization, and writing—original draft. Hossein Mokhtarian worked on conceptualization, investigation, methodology, software, validation, visualization, and writing—original draft. Matti Isakov focused on data curation, investigation, software, visualization, and writing—original draft. Constantinos Goulas focused on investigation, validation, and writing—review and editing. Jean-Yves Hascoët focused on supervision, validation, and writing—review and editing. Iñigo Flores Ituarte worked on conceptualization, investigation, supervision, validation, and writing—review and editing. Investigation, resources, validation, and contributing to writing the original draft was carried out by Remco Rook and Wei Ya, along with the manufacturing of the part. All authors read and approved the final manuscript.

**Funding** Open access funding provided by Tampere University (including Tampere University Hospital). This research has been supported by project Multi-disciplinary Digital Design and Manufacturing—D2M (346874) Academy of Finland/Academy Research Fellow grant and partially by the project TANDEM (4056/31/2021) Business Finland under the SMART EUREKA cluster on Advance Manufacturing Program. The Grade2XL project, involved in the part manufacturing, has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 862017. Characterization work has been conducted at Tampere University in Finland.

**Data availability** The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study. Data can be made available upon request.

## Declarations

**Competing interests** The authors declare no competing interests.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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