# Multi-scale mechanical properties of Al–Mg–Zr–Sc alloys fabricated by direct metal laser sintering: Towards single-material composites

Dominic Zettel, Piotr Breitkopf, Ludovic Cauvin, Pascal Nicolay, Roland Willmann

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Dominic Zettel, Piotr Breitkopf, Ludovic Cauvin, Pascal Nicolay, Roland Willmann. Multi-scale mechanical properties of Al–Mg–Zr–Sc alloys fabricated by direct metal laser sintering: Towards single-material composites. Journal of Materials Research and Technology, 2025, 35, pp.2887-2913. 10.1016/j.jmrt.2025.01.237. hal-04937472

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# Multi-scale mechanical properties of Al–Mg–Zr–Sc alloys fabricated by direct metal laser sintering: Towards single-material composites

Dominic Zettel<sup>a,b,*</sup>, Piotr Breitkopf<sup>b,c</sup>, Ludovic Cauvin<sup>b,c</sup>, Pascal Nicolay<sup>a</sup>, Roland Willmann<sup>a</sup>

## ARTICLE INFO

Handling editor: L Murr

### Keywords:

- Direct metal laser sintering

- Aluminum

- Printing parameters

- Brittle

- Ductile

- Material behavior

## ABSTRACT

This study proposes a methodology for experimentally deriving parameter-dependent mechanical properties of DMLS-printed aluminum. Eighty-five specimens were produced using seventeen distinct parameter sets, designed through a Design of Experiments approach, and subjected to non-destructive and destructive testing. The results reveal a strong correlation between printing parameters, porosity, and mechanical performance, with average pore areas ranging from 0.037% to 21.161% and varied pore types (spherical pores, keyhole pores, and lack of fusion). Correspondingly, mechanical properties showed a broad range: tensile strength (105.3 MPa–459.0 MPa), Young’s modulus (23.38 GPa–69.77 GPa), and yield strength (105.2 MPa–444.8 MPa). Single-material composites were fabricated by integrating dense and porous structures within a single geometry. Tensile testing of these composites showed that geometry and the cross-sectional ratio of ductile material influence mechanical properties significantly (tensile strengths ranging from 89.8 MPa to 112.2 MPa), with stress-strain responses displaying atypical behavior due to the brittle, porous matrix and embedded ductile truss-lattice structures.

## 1. Introduction

Direct Metal Laser Sintering (DMLS), a form of Laser Powder Bed Fusion (LPBF) technology, enables the production of high-performance metal components by selectively fusing layers of metal powder, typically ranging from 10 µm to 75 µm in size, depending on the alloy. The powder is deposited layer by layer on a metallic build platform by a recoater arm, and a laser beam fuses each layer based on the cross-section of a 3D Computer-Aided Design (CAD) model. The process occurs in a controlled atmosphere, usually argon or nitrogen, to prevent oxidation and stabilize the meltpool. DMLS parts exhibit physical properties such as density, tensile strength, and hardness comparable to conventionally manufactured materials, making the process widely used in aerospace, medical, automotive, and energy industries [1,2].

Producing dense, pore-free structures in DMLS requires precise control over the interaction between powder properties and processing parameters, which affect the meltpool dynamics and the resulting microstructure [3–5]. Standard parameter sets for each alloy are optimized to ensure reliable mechanical properties by minimizing voids and

defects. These parameters, including laser power (P), laser speed (v), hatch distance (h), and layer thickness (t), are fine-tuned to maximize material performance characteristics such as tensile strength, hardness, and thermal conductivity [6,7].

Although the primary focus of most research and industrial efforts is to optimize these parameters for enhanced mechanical performance — such as wear resistance [8], surface roughness [9–11], dimensional accuracy [12], fatigue resistance [13], and impact strength [14] — this approach overlooks the potential advantages of controlled porosity for specific applications. Certain applications, particularly those requiring enhanced thermal conductivity, damping, or energy absorption, could benefit from the deliberate introduction of porosity or microstructural heterogeneity. This adjustment explores a path for material optimization, allowing functional properties to be considered alongside mechanical performance. However, parameter sets designed to introduce controlled porosity remain relatively unexplored in DMLS research, as the focus has traditionally been on achieving dense, homogeneous structures. Exploring extreme variations in DMLS process parameters to induce porosity may reveal new possibilities for multi-physical

<footnote><sup>a</sup> Carinthia University of Applied Sciences (CUAS), Research Group AMAViS² and CiSMAT, Europastraße 4, 9524, Villach, Austria</footnote>

<footnote><sup>b</sup> Université de Technologie de Compiègne (UTC), Roberval (Mechanics, Energy, and Electricity), Centre de Recherche Royallieu, CS 60319, 60203, Compiègne, Cedex, France</footnote>

<footnote>* Corresponding author. Carinthia University of Applied Sciences (CUAS), Research Group AMAViS² and CiSMAT, Europastraße 4, 9524, Villach, Austria. E-mail address: d.zettel@cuas.at (D. Zettel).</footnote>
optimization, particularly in applications where a trade-off between mechanical strength and other functional properties is necessary.

However, it is essential to note that in DMLS, findings from studies on one base material or alloy cannot be straightforwardly applied to others due to alloy-specific process variables and boundary conditions. Additionally, variations in printing systems and powder materials — such as differences in particle geometry or sieve size — can significantly influence buildability and final part quality [15–17]. For the alloy used in this study, Aluminum–Magnesium–Zirconium–Scandium (Al–Mg–Zr–Sc), commercially known as “StrengthAl,” only one study, to our knowledge, has investigated and characterized an Al–Mg–Zr–Sc alloy with reduced scandium content for LPBF applications [18].

Several studies have concentrated on “Scalmalloy®,” an aluminum alloy with a composition closely resembling that of StrengthAl (Al–Mg–Zr–Sc), making these investigations particularly relevant to the present study. Topics explored include the effect of surface characteristics on fatigue properties [19], the influence of contour scans on surface roughness and pore formation [20], microstructural development and tensile properties [21], the response of mechanical properties to heat treatment durations [22], the impact of laser energy density on surface roughness [23], the influence of powder reuse on surface chemistry [24], optimized scan strategies for fatigue enhancement [25], and the establishment of a process parameter window by varying the Volumetric Energy Density (VED) [26]. These studies provide valuable insights into how DMLS processing conditions influence alloys’ microstructural and mechanical characteristics similar to Al–Mg–Zr–Sc.

Numerous metallurgical studies have explored various alloys within the DMLS field [27–30]. This study focuses specifically on the relationship between process parameters and resulting mechanical properties. We examine how significant variations of printing parameters influence the microstructure and mechanical characteristics — such as tensile strength, Young’s modulus, and yield strength — of an Al–Mg–Zr–Sc alloy, a material valued for lightweight applications across industries like aerospace and automotive. Building on existing research into the multi-physical optimization of DMLS structures (including enhancements in thermal conductivity and damping rates) [31–34], this study gathers material data from specimens across multiple scales — micro, meso, and macro — to support and expand application scenarios.

In the validation section, relevant parameter sets were selected from the experimental results to manufacture test specimens featuring two distinct printing configurations, creating dense lattice reinforcements and a porous matrix within each sample. This study explores the practical application of combining parameter-dependent structures to produce Single-Material Composites (SMCs), which represents the core novelty of this work. To the authors’ knowledge, this is the first approach to forming and testing structures composed of multiple distinct printing parameter sets. The integration of different structures with unique material characteristics marks an initial step toward the development of multi-physically optimized parts. Additionally, this approach is inherently sustainable, as the structures are made from the same material. This contrasts with conventional composites, where differing materials for the fiber and matrix complicate recycling [55].

Each SMC underwent tensile testing to evaluate how the integrated printing parameters influenced their mechanical behavior. Specifically, the specimens included refined truss-lattice structures formed with a higher energy density parameter set and a porous matrix produced under a lower energy density setting. The results of these tensile tests provide insights into the relationship between the selected parameter sets and the resulting mechanical characteristics, validating the feasibility of merging multiple parameter configurations within a single material.

The structure of the paper is as follows: Section 2 covers the preparation of 17 specific printing parameter sets using Design of Experiments (DoE) and the fabrication of various specimen types, including cuboid-shaped specimens and single laser tracks via DMLS, followed by their preparation for material characterization tests, such as Electron

Backscatter Diffraction (EBSD), Energy Dispersive X-ray Spectroscopy (EDX), and tensile testing. Section 3 examines the influence of these parameters on material characteristics, including pore area, pore morphology, tensile strength, Young’s modulus, yield strength, and melting behavior, while also categorizing material responses as ductile, semi-brittle, or brittle. Section 4 analyzes of the individual and interactive effects of the printing parameters on material properties through main effects and interaction plots, distinguishing dominant from non-dominant factors. Section 5 offers recommendations for developing robust predictive simulation models to optimize the DMLS process, to reduce development costs through improved efficiency. Section 6 presents a feasibility study on SMCs. Finally, Section 7 presents conclusions and directions for future research.

The primary objective of this work is to examine the correlation between modified printing parameter sets, their influence on the micro- and mesostructure, and the resulting mechanical properties — such as tensile strength, Young’s modulus, and yield strength — of Al–Mg–Zr–Sc specimens produced using DMLS.

## 2. Method and material

After the initial pre-campaign, four factors were identified as key parameters: laser power (P) [W], laser speed (v) [mm/s], hatch distance (h) [mm], and layer thickness (t) [mm]. These allow calculating the Volumetric Energy Density (VED), estimation of the energy input into the powder bed, as outlined in Equation (1):

$$VED = \frac{P}{v \cdot h \cdot t} \qquad (1)$$

Each factor level was then allowed to evolve within specific percentage bounds, identified as the extreme values feasible for manufacturing, relative to the corresponding settings in the aluminum standard parameter set, with P and h varying by ±20% and v by ±25%. In the case of t, no percentage variation was possible due to limitations of the printing system. As a result, multiples of 0.03 mm, specifically 0.06 mm and 0.09 mm, were chosen. Table 1 presents the individual settings of the standard parameter set for processing Al–Mg–Zr–Sc powder, along with the corresponding factor levels derived for the DoE.

An additional parameter set, serving as a “center point,” was established for the DoE using the mean values of the printing parameters. Because of variations in layer thickness, this set does not exactly match the standard parameter set.

The choice between different DOE layouts [35] is critical, as it impacts the experiment’s efficiency, accuracy, and ability to detect factor interactions or nonlinear effects. When considering a Full Factorial Design (FFD), the three-level per factor with center points (CP) layout repeated five times (abbreviated as 3⁴ FFD+4 CP) and a two-level layout with center points repeated five times (2⁴ FFD + CP) × 5 both utilize the

<table><caption>**Table 1**
Settings of individual printing parameters for the "m4p_StrengthAl_30 µm_Detailiregister_1.5" set, including the corresponding factor levels derived for the DoE [32].</caption><thead><tr><th rowspan="2">Parameter</th><th rowspan="2">Possible range</th><th rowspan="2">Standard settings (StrengthAl)</th><th colspan="3">DoE</th></tr><tr><th>Low level</th><th>High level</th><th>Center point</th></tr></thead><tbody><tr><td>Laser power [P]</td><td>0–370 W</td><td>250 W</td><td>200</td><td>300</td><td>250</td></tr><tr><td>Laser speed [v]</td><td>0–7000 mm/s</td><td>550 mm/s</td><td>412.5</td><td>687.5</td><td>550</td></tr><tr><td>Hatch distance [h]</td><td>0–xmm (0.05–0.25 mm)</td><td>0.18 mm</td><td>0.144</td><td>0.216</td><td>0.18</td></tr><tr><td>Layer thickness [t]</td><td>30–60 µm</td><td>0.03 mm</td><td>0.03</td><td>0.09</td><td>0.06</td></tr></tbody></table>
same budget of 85 specimens. 3<sup>4</sup> FFD+4 CP provides good coverage of the design space, leaving however little room for repetition (which is important to assess variability or error), potentially reducing the reliability of the results. On the other hand, (2<sup>4</sup> FFD + CP) × 5 allows for repetitions and more reliable estimate of the experimental variability, or so-called pure error, which can increase the robustness and reliability of the conclusions drawn from the experiment.

Three-level full factorial designs permit exploring nonlinear effects of the factors, as they allow for the detection of quadratic relationships. However, the complexity of interpreting interactions and higher-order effects increases, potentially making the analysis more difficult. A two-level factorial design with center points is efficient at detecting main effects and factor interactions. While it may not capture complex nonlinear effects, adding center points provides some ability to detect curvature in the response surface. For a limited budget of 85 specimens in this present study, 3<sup>4</sup> FFD+4 CP would use almost all the budget for a single exploration of the factor space (81 out of 85 specimens), leaving very little room for repetitions, which are essential for estimating experimental error and assessing the repeatability of results. (2<sup>4</sup> FFD + CP) × 5, by contrast, uses far fewer resources per specimen, allowing for both the detection of key effects and interactions and some assessment of potential curvature with the center points. With fewer runs per factor combination, more repetitions can be incorporated into the budget, increasing the reliability of the experiment and providing a better estimate of experimental noise. Given the budget constraint (2<sup>4</sup> FFD + CP) × 5 offers a more balanced and efficient approach allowing for exploration of main effects and interactions between factors, detection of moderate curvature with center points, better error estimation and reliability making more efficient use of the experimental budget, as it allows room for repetition without consuming all resources on a single high-level design. Therefore, for the same budget, the (2<sup>4</sup> FFD + CP) × 5 is selected. The parameter sets for the (2<sup>4</sup> FFD + CP) × 5 are shown in Table 2, along with including the respective VED s.

For the material characterization, two types of specimens were produced: cuboid-shaped specimens for tensile tests and microscopic examinations, and single laser tracks for examinations of the meltpools. Both types of specimens, which will be described later, were created using the same Al–Mg–Zr-Sc powder known commercially as “StrengthAl,” produced by m4p - material solutions GmbH - Austria. For printing, an EOS M290 printing system with a build volume of 250mm × 250mm × 325 mm was used, equipped with a 400 W Ytterbium (Yb) fiber laser with a standard spot size of 100 µm. The recoater arm was fitted with a ceramic blade for hard recoating, and argon was selected as the shielding gas. While both argon and nitrogen can be used as

<caption>**Table 2**</caption>

2<sup>4</sup>FFD + CP design of experiments to be repeated five times including volumetric energy densities.

<table><thead><tr><th rowspan="2">Parameter set</th><th colspan="5">Printing parameters</th></tr><tr><th>P [W]</th><th>v [mm/s]</th><th>h [mm]</th><th>t [mm]</th><th>VED [J/mm³]</th></tr></thead><tbody><tr><td>1</td><td>200</td><td>412.5</td><td>0.144</td><td>0.03</td><td>112.23</td></tr><tr><td>2</td><td>200</td><td>412.5</td><td>0.144</td><td>0.09</td><td>37.41</td></tr><tr><td>3</td><td>200</td><td>412.5</td><td>0.216</td><td>0.03</td><td>74.82</td></tr><tr><td>4</td><td>200</td><td>687.5</td><td>0.144</td><td>0.03</td><td>67.34</td></tr><tr><td>5</td><td>300</td><td>412.5</td><td>0.144</td><td>0.03</td><td>168.35</td></tr><tr><td>6</td><td>300</td><td>687.5</td><td>0.216</td><td>0.09</td><td>22.45</td></tr><tr><td>7</td><td>300</td><td>687.5</td><td>0.216</td><td>0.03</td><td>67.34</td></tr><tr><td>8</td><td>300</td><td>687.5</td><td>0.144</td><td>0.09</td><td>33.67</td></tr><tr><td>9</td><td>300</td><td>412.5</td><td>0.216</td><td>0.09</td><td>37.41</td></tr><tr><td>10</td><td>200</td><td>687.5</td><td>0.216</td><td>0.09</td><td>14.96</td></tr><tr><td>11</td><td>200</td><td>687.5</td><td>0.216</td><td>0.03</td><td>44.89</td></tr><tr><td>12</td><td>300</td><td>412.5</td><td>0.144</td><td>0.09</td><td>56.12</td></tr><tr><td>13</td><td>200</td><td>687.5</td><td>0.144</td><td>0.09</td><td>22.45</td></tr><tr><td>14</td><td>300</td><td>412.5</td><td>0.216</td><td>0.03</td><td>112.23</td></tr><tr><td>15</td><td>200</td><td>412.5</td><td>0.216</td><td>0.09</td><td>24.94</td></tr><tr><td>16</td><td>300</td><td>687.5</td><td>0.144</td><td>0.03</td><td>101.01</td></tr><tr><td>17</td><td>250</td><td>550</td><td>0.18</td><td>0.06</td><td>42.09</td></tr></tbody></table>

protective gases for this alloy, argon was preferred due to its higher density, which allows it to rest more effectively on the powder bed. The build platform temperature was set to 180 °C. During printing, the exposure sequence for fabricating the specimens was arranged to be in the opposite direction of the gas flow, as shown in Fig. 3 (a), to effectively minimize the risk of contamination from splashes.

### Single laser tracks

This type of specimen is produced through a single laser vector exposure, which results in the creation of a single weld bead. For this purpose, a special build platform was produced, which differs from the original one in terms of thickness and material. The custom-made build platform has a thickness of 6 mm to allow subsequent cutting out and embedding of fragments containing the single laser tracks. The material is Aluminum–Magnesium–Silicon 1 (AlMgSi1), chosen for its alloy components’ composition similar to that of the powder material. Ideally, the powder material and the platform should be made of the same material, as deviations in the alloys can lead to meltpool distortion. However, in this case, there is no conventional material that exactly matches the alloy composition of the powder material. To facilitate the installation of the platform in the printing system, special countersunk screws conforming to ISO 10642 - M8 × 20 specifications were necessary. The platform associated with the fabrication of single laser tracks, which involves three tracks for each printing parameter set, is illustrated in Fig. 1.

Fragments were then removed from the build platform for further processing into metallographic specimens. To achieve this, a wet cutting grinder (Discotom-100 — Struers GmbH) was utilized to remove a narrow section from the build platform across the laser tracks, as shown in Fig. 2 (a). Subsequently, the removed fragments were embedded with warm embedding material (CEM1000 blue powder — Cloeren Technology GmbH) using an embedding press (Citopress-10 — Struers GmbH). This process enabled the cross-section of the single laser tracks to be examined, Fig. 2 (b).

The embedded fragments were subsequently processed using a Sili-con Carbide (SiC) grind disc (grain size: 1200 - WS FLEX 18C — Hermes Schleifmittel GmbH), a diamond suspension (grain size: 3 µm - CT Dia Twin Two-In-One — Cloeren Technology GmbH), and a silicon dioxide suspension (grain size: 0.04 µm - Colloidal Silica Suspension 50 nm Alkaline — Cloeren Technology GmbH). Additionally, the specimens underwent etching using the Keller-Wilcox method. The cross sections of the single laser tracks were then examined using a Reflected Light Microscope (RLM) (Axio Imager.M2m — Zeiss AG), and geometric features were measured using image processing software (ZEN core — Zeiss AG). Furthermore, the longitudinal direction (top view) of the single laser tracks was examined via RLM (Axio Lab.A1 — Zeiss AG) with image processing software Pixel-fox (Dietermann & Heuser Solution GmbH). The remaining unprocessed part of the build platform was used for this purpose, Fig. 2 (a).

### Cuboid-shaped specimens

<figure><img src="image_4.png" /><figcaption>Fig. 1. Single laser tracks corresponding to the 17 printing parameter sets, with three tracks produced for each parameter set.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Fig. 2. Cut of build platform including single laser tracks of the 17 printing parameter sets (a). Embedded fragments of the build platform including single laser tracks for microstructure investigations (b).</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 3. Segmentation of the build platform including recoater motion and direction of the gas flow (top view) (a), based on [36]. Focal point path of the laser beam (side view) (b).</figcaption></figure>

For the cuboid specimens, measuring 12mm × 12mm × 75 mm in their as-printed condition, each parameter set was printed five times, resulting in a total of 85 specimens across 17 sets. To account for various influences in the process chamber, such as the direction of gas flow and the recoater's translational motion, as well as the changing focus point of the laser beam during printing, five groups of 17 specimens were arranged in the four corners and the center of the build platform, as shown in Fig. 3. The specimens were printed directly on the build platform without a support structure to mitigate vibrations that could tilt the recoater arm and prematurely terminate the print job.

After printing, the build platform, including the adhering specimens, was subjected to precipitation hardening in a hardening furnace (KM 10/13 — Thermconcept GmbH) at a temperature of 350 °C for 6 h, followed by slow cooling in air. Note that in the case of DMLS structures, only the aging is carried out in the hardening furnace, as the solution heat treatment already takes place during printing due to the rapid cooling of the meltpool. Fig. 4 illustrates a group of 17 cuboid-shaped specimens at various stages: during preprocessing (parameter assignment in EOSPRINT), printing, and post-hardening.

For the tensile tests, the specimens of sectors “a,” “c,” “g,” and “i” (see Fig. 3) were mechanically processed in accordance with DIN 50125:2016-12 - Form B [36]. The specimens from sector “e” were utilized for microstructural investigations, which are discussed in subsequent sections of this paper. The actual tensile tests were carried out in accordance with ISO 6892-1:2019 - Method A [37] using a tensile testing machine (AllroundLine Z020 — ZwickRoel AG) and extensometers. The fracture surfaces of selected specimens, designated as “i2,” “c10,” “a7,” “a12,” “a13,” and “a16,” were meticulously examined using a Scanning

Electron Microscope (SEM) model JSM-IT500 — JEOL GmbH. These specimens were selected due to their distinct material behaviors, with two examples each representing ductile characteristics, specifically “a7” and “a16,” semi-brittle characteristics, namely “i2” and “a12,” and brittle characteristics, including “c10” and “a13.” Additionally, fragments of specimens exhibiting varying material behaviors, specifically “i2,” “c10,” and “a16,” were embedded and prepared for EBSD analysis, as shown in Fig. 5.

EBSD analyses on porous structures are typically associated with significant challenges and limitations, making them uncommon unless there is a specific justification. In the context of this study, such a justification exists: the primary goal is to obtain information on the prevailing grain sizes and grain boundaries. This data is crucial for subsequent analyses, particularly when investigating mechanical properties such as those related to the Hall-Petch relationship, where grain size directly influences material strength.

The specimens' fragments were embedded on the side of the thread using epoxy resin (Epilox® T19-34/700 - LEUNA-Harze GmbH/CeTePox 1027 H — Aditya Birla Chemicals Ltd.), as this area is not pre-stressed by the tensile tests (resulting in no change in the structure), as depicted in Fig. 5. Subsequently, the embedded fragments underwent wet grinding (up to P1200 SiC) and diamond polishing (with a grain size of 1 µm) in a multi-stage process. Following this, the specimens underwent polishing with a vibration polisher (using silica suspension MasterMet® 2, with a grain size of 0.02 µm) for 18 h, before being cleaned under running water with a small amount of detergent and rinsed with ethanol. The EBSD analysis itself was conducted using a SEM (XL 30 — Philips N.V.) and its associated EBSD system (INCA — Oxford Instruments PLC). A

<figure><img src="image_7.png" /><figcaption>Fig. 4. Preprocessing stage of cuboid-shaped specimens in EOSPRINT: The different colors represent the 17 different printing parameter sets (a). Specimens during printing (b) and after heat treatment (c).</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Fig. 5. Schematic representation of metallographic specimens from fragments of the tensile test (a). Embedded specimen for EBSD analysis (b). Specimen fragment after tensile test (c).</figcaption></figure>

tungsten cathode (thermal emission), an acceleration voltage of 30 kV, and a spot size of 4.5 for “i2” and 5 for “c10” and “a16” were used.

The specimens from sector “e” were utilized for metallographic ex-aminations. To achieve this, the specimens were cut transversely to their longitudinal axis (“cutting plane”), embedded with warm embedding material (CEM1000 blue powder — Cloeren Technology GmbH) using an embedding press (Citopress-10 — Struers GmbH), and then roughly ground down to their center (“grinding plane”), as illustrated in Fig. 6.

Side “A” consistently denotes the top side of the specimens, which also includes the specimen markings. Following coarse grinding to the specimens’ center, additional grinding and polishing were conducted to prepare them for microscopic examination. Initially, a SiC grinding disc (grain size 1200 - WS FLEX 18C — Hermes Schleifmittel GmbH) was employed, followed by a diamond suspension (grain size 3 µm - CT Dia Twin Two-In-One — Cloeren Technology GmbH), and ultimately a sil- icon dioxide suspension (grain size 0.04 µm - Colloidal Silica Suspension 50 nm Alkaline — Cloeren Technology GmbH). Both sides of the pol- ished specimens (Side “A” and “B”) were subsequently examined using a RLM (Axio Imager.M2m — Zeiss AG) and a SEM (JSM-IT500 — JEOL GmbH). Besides capturing microscopic images in both the etched (Kel- ler-Wilcox) and unetched states of the specimens, porosity measure- ments were conducted with the RLM using image processing software (ZEN core — Zeiss AG). Furthermore, EDX investigations (JSM-IT500 — JEOL GmbH) were performed on three different specimens (“c4,” “c8,” and “c10”) to analyze the distribution of the alloying elements.

### 3. Material characteristics

#### • Single laser tracks

The investigation of the cross-sections of the single laser tracks re-veals different types of weld beads, namely “keyhole formation,” “semi- circular U-shape,” and “insufficient substrate penetration” [38], as illustrated in Fig. 7.

In general, a semi-circular U-shaped meltpool is preferred for the efficient production of solid components, as it ensures an optimal bal- ance between material consolidation and build rate, thereby enhancing economic efficiency. In contrast, keyhole formation and insufficient substrate penetration result in the formation of voids and reduced

mechanical strength due to excessive or insufficient energy input, respectively. Among the 17 different single laser tracks analyzed, eight exhibit keyhole formation, corresponding to the printing parameter sets “5,” “6,” “7,” “8,” “9,” “12,” “14,” and “16.” Seven laser tracks display the desirable semi-circular U-shape, corresponding to parameter sets “1,” “2,” “3,” “4,” “10,” “11,” and “13.” Only two tracks, associated with parameter sets “15” and “17,” show insufficient substrate penetration.

A clear correlation is evident between the energy input, calculated for single laser tracks according to Equation (2), and the resulting meltpool morphology, as illustrated in Fig. 8.

$$El = \frac{P}{v \cdot t} \qquad (2)$$

The Linear Energy Density of single laser tracks (El) [J/mm²], calculated using laser power (P) [W], scan speed (v) [mm/s], and layer thickness (t) [mm], allows for the classification of the tracks based on their geometric shape, as evidenced by the increasing energy values shown in the table in Fig. 8. When comparing the 17 different VEDs calculated using Equation (1) to the Els calculated using Equation (2), it is evident that only seven distinct energy densities are obtained for the latter. Consequently, parameter sets with the same El tend to produce similar geometries in single laser tracks. However, a closer inspection of the microscopic images in Fig. 8 (image reference “3”) shows that the cross-sections of printing parameter sets “2” and “15” differ, despite both being fabricated with the same El. This variation is due to distinct parameter-dependent meltpool formations, specifically categorized as “optimum continuous” and “non-continuous,” as discussed in the following sections.

When comparing the printing parameter sets presented in the table in Fig. 8 with the corresponding microscopic images, it is evident that the El alone does not determine the geometry of the individual laser tracks. This is particularly illustrated by the comparison of printing parameter sets “1” and “3,” both of which have an El of 16 J/mm², and printing parameter sets “7” and “16,” which exhibit nearly the same El of 15 J/mm². The assumption that a higher energy input results in a larger meltpool and, consequently, wider weld beads is not supported by this analysis. In fact, it is observed that a lower El is associated with a larger cross-section of the individual laser track.

The variation in the characteristics of the weld beads can be attrib- uted to the method of energy application to the powder bed. In printing

<figure><img src="image_9.png" /><figcaption>Fig. 6. Schematic representation of metallographic specimens (a) and representation of an embedded specimen (b), based on [33]. Picture of an embedded specimen (c).</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Fig. 7.** RLM images of the cross-section of single laser tracks showing keyhole formation of printing parameter set “14” (a), semi-circular U-shape of printing parameter set “13” (b), and insufficient substrate penetration of printing parameter set “15” (c).</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Fig. 8.** Table with the individual printing parameter sets, sorted in ascending order according to El. The individual groupings of the RLM images of the cross-sections of the single laser tracks show the influence of El on the cross-sectional geometry.</figcaption></figure>

parameter sets “1” and “3,” a lower energy input (laser power) was applied at a slower speed (laser speed), whereas in printing parameter sets “7” and “16,” a higher energy input was delivered over a shorter duration. Additionally, the properties of the powder material — such as bulk density, flowability, humidity content, oxidation level, and thermal conductivity — also play a significant role in influencing how energy is introduced into the powder bed. These powder characteristics, along with other influencing factors such as the flow velocity of the gas stream, introduce complexity into the process. This complexity complicates the prediction of process and material properties and diminishes the informational value of simulation models.

Additionally, Fig. 8 illustrates that keyhole formation, as exemplified in image references “9” and “5,” is associated with a high energy input characterized by elevated laser power (300 W), reduced laser speed (412.5 mm/s), and a minimal layer thickness (0.03 mm). Conversely, instances of inadequate substrate penetration correspond to very low energy inputs; for instance, printing parameter set “15” features low laser power (200 W), the same laser speed (412.5 mm/s), but a significantly larger layer thickness (0.09 mm). In contrast, an optimal energy input is represented by a semi-circular U-shape, yielding the desired weld bead geometry, as seen in printing parameter sets “1” and “3.” In

this scenario, a low laser power (200 W), combined with a low laser speed (412.5 mm/s) and minimal layer thickness (0.03 mm), results in an optimal meltpool geometry.

Additionally, longitudinal cracks are observed in single laser tracks with a large cross-sectional area, as depicted in Fig. 7 (a). It is presumed that these cracks are caused by stress resulting from the rapid cooling of the meltpools. The stress may also be exacerbated by variations in alloy composition between the build platform and the powder material, given that no cracking issues were noted in the cuboid-shaped specimens. The measurements of the geometrical characteristics of the individual cross-sections of the single laser tracks were conducted as outlined in Fig. 9 (a).

The measurements reveal a wide range of results: width: 119.6 µm-317.7 µm; depth: 33.0 µm-225.7 µm; height: 18.0 µm-66.4 µm; surface area: 4614.0 µm²-54911.3 µm². No correlation between the geometric properties and the VED can be identified. Different types of melting behavior were also observed in the investigations of the longitudinal sections (top view) of the single laser tracks, namely “optimum continuous” and “non-continuous” [38], as illustrated in Fig. 10.

Nearly half of the 17 printing parameter sets in this study — specifically “5,” “6,” “7,” “8,” “9,” “12,” and “16” — exhibit “optimum
<figure><img src="image_12.png" /><figcaption>Fig. 9. Illustration of the geometric characteristics of cross-sections of individual laser tracks (a). Sketch of single laser tracks showing the influence of a non-continuous laser track on its cross-sections (b).</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>Fig. 10. RLM images of single laser tracks (top view) showing different kinds of meltpool formation, namely “optimum continuous” laser track of printing parameter set “5” (a) and “non-continuous” laser track of printing parameter set “1” (b).</figcaption></figure>

continuous” meltpool formation, while the remaining sets display “non-continuous” meltpool formation. To exclude incorrect results and misinterpretations when categorizing the cross-sections of single laser tracks, it is essential to consider the different melting behaviors in the longitudinal section (top view), Fig. 9 (b).

This sketch illustrates that irregular melting paths (top view) also result in different cross-sections, depending on where the laser track is cut. Therefore, it is recommended to create multiple cross-sections for each single laser track, particularly in instances of “non-continuous” melting behavior. This method enables more precise descriptions of the geometry, including aspects such as keyhole formation, semi-circular U-shapes, or inadequate substrate penetration. It also facilitates the measurement of geometric characteristics, including the mean value and the

range of fluctuations within the cross-sections.

### Cuboid-shaped specimens

The investigations of the cross-sections of the cuboid-shaped specimens via RLM and SEM reveal a significant influence of the printing parameter sets on the structure and porosity of the specimens, as can be seen by the comparison between “c1” (112.23 J/mm³) and “c10” (14.96 J/mm³) in Fig. 11.

While “c1” represents an “ideal” DMLS topology due to its homogeneous, dense structure with small and closely spaced weld beads, “c10” exhibits high porosity. Although this porosity is also evenly distributed throughout the entire structure, the cavities themselves

<figure><img src="image_14.png" /><figcaption>Fig. 11. Microscopic images of the cross-sections of specimens “c1” (dense structure) and “c10” (porous structure). RLM image of side “A” in polished and unetched condition (a), SEM image at 50 × magnification in unetched condition (b), and RLM image of side “A” in polished and etched condition (c).</figcaption></figure>
display asymmetrical geometries with wider and more irregular weld beads. Furthermore, unmelted powder particles are visible within the cavities of "c10," indicating insufficient energy input during processing.

Various parameter-dependent pore types and anomalies such as "spherical pores," "keyhole pores," "lack of fusion," and "hot cracks" were also detected in the different specimens, as summarized in Fig. 12. The definitions of these anomalies are as outlined below:

* "Spherical pores" are circular pores with a diameter of less than 100 µm. They may be explained by protective gas trapped in the meltpool, which cannot escape during the process. Furthermore, gases may become trapped within individual particles during the powder material production process or as a result of the interplay between gas flow and meltpool dynamics, such as the presence of vapors within the meltpool during printing. In general, spherical pores form when there is high energy input into the powder bed [3]. This type of pore was observed in all specimens in this study.

* "Keyhole pores" have an almost circular shape and are larger than spherical pores. This type of pore is also observed when a high energy input is applied to the powder bed, and may be explained by vaporizing metal trapped within the meltpool that cannot escape due to the rapid cooling rate. During the printing process, the meltpool assumes a keyhole-like configuration, characterized by a depth that exceeds half its width, which generates significant dynamics, including circulation. These dynamics, arising from interactions between solid, liquid, and gaseous phases, lead to a collapse of the meltpool and the formation of large pores and cavities [3]. In this study, all specimens contain keyhole pores, which were fabricated with a VED of ≥101.01 J/mm³.

* "Lack of fusion" describes an anomaly in the microstructure where irregular cavities filled with unfused powder material are associated with very low energy input into the powder bed. The sharp edges of the disc-shaped pores result in increased stress peaks when subjected to mechanical loads, enhancing the notch effect. Additionally, the low energy input results in poor bonding between the individual layers. Incomplete melting can have several causes, including lack of meltpool overlap due to insufficient energy input, excessive hatch distance, or inadequate penetration of the meltpool [39]. All specimens fabricated in this study with a VED of ≤22.45 J/mm³ exhibit this microstructural anomaly.

* "Hot cracks" are structural anomalies involving fine cracks in the material. These cracks, occurring during the rapid cooling of the meltpool, are predominantly found in high-strength aluminum alloys. As the meltpool cools, columnar grains form in the direction of the temperature gradient, leaving interdendritic liquid (liquid phase that exists between growing dendritic structures during the solidification of an alloy) at the interfaces. This, combined with material shrinkage during cooling, leads to crack formation. The presence of pores, especially those with sharp and irregular edges, can facilitate crack growth due to the notch effect [40]. In all microscopic images captured for this study, only one crack was identifiable in a single image.

The wide range of porosities measured, which spans from 0.037% to 21.161%, illustrates the substantial impact of the printing parameter sets on the mesostructure of the specimens, as shown in Fig. 13 (a).

The trend of the data points indicates that the average total pore area decreases up to a VED of ≤33.67 J/mm³ before stabilizing within the range of 1.28%-0.037%. Further evaluations show that the porosity is

<figure><img src="image_15.png" /><figcaption>**Fig. 12.** SEM images of DMLS specimens showing different types of pores and anomalies. Specimen "c9" with spherical pores (a), specimen "c5" with keyhole pores (b), specimen "c10" with lack of fusion (c), and specimen "c15" with hot cracks (d). The SEM images (a), (c), and (d) show a magnification of 500 × while the SEM image (b) shows a magnification of 50 × .</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>Fig. 13. Average total pore area as a function of the applied VED (a). Total pore area of side “A” and “B” of the metallographic specimens (b).</figcaption></figure>

not homogeneously distributed over the entire build height of the specimens, as demonstrated by comparing the two halves of the specimens (labeled “A” and “B”) in Fig. 13 (b).

On one hand, there are specimens (e.g., “c1”: side “A” = 0.042%, side “B” = 0.033%) in which porosity is homogeneously distributed; on the other hand, there are specimens (e.g., “c10”: side “A” = 22.148%, side “B” = 20.175%) for which this is not the case. For specimens that exhibit different porosities in both halves, further differences are observed — in some, the upper half is more porous, while in others, the lower half shows higher porosity. It is assumed that these fluctuations are random process variations, only partially linked to the specific printing parameter sets. The specimens exhibit varying parameter-dependent pore geometries and sizes, which can influence cooling rates during printing. In the DMLS process, increasing build height generally leads to heat accumulation within a structure, particularly in regions with constant or increasing cross-sections. This heat buildup can, in turn, impact the development of the micro-/mesostructure, including the formation of pores or cavities, which further affect cooling rates. Additionally, irregular heat accumulation among the specimens is attributed to differences in the printing parameter sets, particularly in terms of energy input per time/volume. For example, some specimens are exposed in every layer, while others are exposed only every third layer.

The EDX analysis conducted on specimens with varying porosities, specifically those labeled “c4” (0.22%), “c8” (1.11%), and “c10” (21.16%), aimed to investigate whether the alloy components are distributed unevenly throughout the microstructure, particularly at the edges of the pores. The results, illustrated by the example of specimen

“c10” in Fig. 14, reveal no abnormalities. All three specimens exhibit a uniform distribution of the alloying elements aluminum and magnesium. However, other alloying elements such as silicon, scandium, titanium, zirconium, manganese, and chromium could not be detected because they were below the resolution limit of the testing apparatus, which is set at 0.2% by mass. The results of the tensile tests, including the mechanical parameters tensile strength, Young’s modulus, and yield strength, are presented in Fig. 15.

All three diagrams illustrate strong reproducibility, characterized by a narrow bandwidth within their respective grids (“a,” “c,” “g,” and “i”), along with a consistent trend in the progression of the data points. The position of the specimens on the build platform appears to have a negligible impact on the mechanical characteristics, as no discernible trend can be observed. The measurement results indicate a substantial impact of the printing parameter sets on the mechanical strength values of the specimens, along with a broad range of results. The average tensile strength varies from 105.3 MPa to 459.0 MPa, the Young’s modulus spans from 23.38 GPa to 69.77 GPa, and the yield strength ranges from 105.2 MPa to 444.8 MPa.

With increasing VED, there is a drastic increase in the strength values until a VED of approximately 34 J/mm³ is reached. From this value onwards, the mechanical properties remain almost constant. A similar trend is observed in the specimens’ porosity, as shown in Fig. 13 (a), indicating a potential correlation between porosity and mechanical properties. A clear relationship between the strength values shown in Fig. 15 and the sizes or types of the meltpools depicted in Fig. 8 — particularly when comparing parameter set “2” — cannot be identified.

<figure><img src="image_17.png" /><figcaption>Fig. 14. EDX analysis of “c10”, showing the regular distribution (same color intensity over the entire measurement area) of the alloying elements Aluminum and Magnesium.</figcaption></figure>
<figure><img src="image_18.png" /><figcaption>**Fig. 15.** Measurement results of the tensile strength (a), Young's modulus (b), and yield strength (c) of Al-Mg-Zr-Sc specimens as a function of the **VED**. The symbolic distinction between printing parameter sets that have the same **VED** is indicated by red circles and black crosses. Average tensile strength as a function of the total number of pores (d).</figcaption></figure>

Therefore, it is crucial to examine additional microstructural properties, such as grain size and crystal orientation, as these factors typically have a significant impact on the strength behavior of metallic solids. The investigations pertinent to this work are discussed in greater detail in the following sections.

It is also noteworthy that specimens fabricated with the same **VED** but different parameter settings (factor levels) result in different strength values, indicating material property divergence under constant volumetric energy conditions. This discrepancy can be observed by comparing the red circles and black crosses in the diagrams. From this, it can be concluded that **VED** alone is not sufficient for characterizing parameter-dependent material properties. Rather, it is the combination of the four key parameters that governs both the meltpool formation and the resulting microstructure, which ultimately determines the associated mechanical properties. Consequently, the main effects and interactions of the key parameters considered in this study were thoroughly analyzed in Section 4.

Furthermore, based on the characteristic stress-strain curves and the corresponding fragments of the tensile specimens, three different material behaviors could be identified: brittle, semi-brittle, and ductile, as illustrated in Fig. 16.

Eleven printing parameter sets — specifically “1,” “3,” “4,” “5,” “7,” “8,” “9,” “11,” “14,” “16,” and “17” — with **VED** s ranging from 33.67 J/mm³ to 168.35 J/mm³, exhibit ductile material behavior. In contrast, three sets — “2,” “12,” and “15” — with **VED** s between 24.94 J/mm³ and 56.12 J/mm³, show semi-brittle behavior. The remaining three sets — “6,” “10,” and “13” — with **VED** s ranging from 14.96 J/mm³ to 22.45 J/mm³, result in brittle material behavior.

To the authors' best knowledge, there are currently no available studies that comprehensively address the various material behaviors associated with the DMLS process. Consequently, a comparison with existing research is not feasible. However, the various material behaviors are influenced by the average total pore area, as illustrated by the upward trend shown in Fig. 16 (a).

In a ductile forced fracture scenario, plastic deformation precedes the specimen's rupture. Both normal and shear stresses manifest within the specimen in response to the externally applied tensile force. Displacement movements are stimulated in regions experiencing maximum shear stresses. Moreover, deformation stress induces an increase in dislocations, complicating dislocation movements and resulting in material hardening. Dislocations accumulate at obstacles such as grain boundaries, inclusions, and foreign phases, leading to the formation of microcracks and micropores, which subsequently grow into small cavities. As the remaining material is sheared off, these cavities coalesce and expand until only residual material remains in the form of bars surrounding the cavities. With the remaining material in the bars diminishing, the specimen can no longer withstand deformation, causing the bars to undergo plastic separation in the direction of the highest effective shear stress. Shear stresses arise during tensile testing as a result of the complex internal stress distribution and the material's structural inhomogeneities. Factors such as non-homogeneous deformation, the Poisson effect, microstructural obstacles, necking, and microcracks play a significant role in influencing the deformation and failure behavior of the material. The material contracts macroscopically and narrows perpendicular to the loading direction. If the bars diminish below a certain minimum thickness, atomic bonds are entirely disrupted, resulting in structural failure. The fracture surfaces exhibit a distinctive “honeycomb” structure, characterized by torn bar edges known as “tearing ridges” [41,42].

Specimens with an average total pore area of ≤1.113% exhibit features characteristic of ductile fracture. This is evident from the pronounced elastic-plastic range of the stress-strain curves, as well as the classic necking and the fracture line oriented 45° to the longitudinal axis, as shown in Fig. 16 (b).

However, further studies are required to investigate the microstructural processes, such as dislocation movements, that occur in the material during tensile test. These investigations are necessary to fully explain the resulting properties, including stress-strain curves, necking,
<figure><img src="image_19.png" /><figcaption>**Fig. 16.** Total pore area of the 17 specimens from grid “c”, including their classification into three different material behaviors (ductile, semi-brittle, and brittle) (a). Different material behaviors are each represented by typical stress-strain curves as well as by corresponding fractured specimens: Ductile (b), semi-brittle (c), and brittle (d).</figcaption></figure>

and fracture line at 45°.

In the case of a brittle forced fracture, the component fails without prior plastic deformation. The fracture surface is usually smooth, and no macroscopic deformations, such as necking, are visible. Brittle forced fracture occurs when the maximum normal tensile stress exceeds the separating strength of the material. In this uniaxial stress condition, the greatest amount of normal stress acts in the tensile direction, leading to a brittle fracture perpendicular to this direction. At the microscopic level, cleavage fractures occur in the microstructure at points where dislocation movement is impeded, such as foreign atoms, grain boundaries, inclusions, precipitates, or accumulated dislocations. Materials can only be plastically deformed if dislocations can move freely through the lattice. However, if dislocation movement is impeded, the lattice expands in front of the obstacle, leading to the destruction of atomic bonds. This lattice expansion then serves as the nucleus of a microcrack that grows into a cleavage crack. The resulting crack propagates at such a high speed that it is visible to the naked eye, ultimately causing the component to fail [43].

Specimens with an average total pore area of ≥3.491% exhibit

features characteristic of brittle fracture. In such cases, the stress-strain curves consist solely of an elastic region (Hooke's law) without any plastic component, and the fragments of the tensile specimens do not exhibit any necking. Additionally, the fracture surfaces are oriented at 90° to the longitudinal axis, as shown in Fig. 16 (c).

As with ductile fracture, further microstructural investigations are necessary to explain the macroscopic properties, such as stress-strain curves, absence of necking, and a fracture line of 90°. It is assumed that brittle material behavior is correlated with porosity. In contrast to a dense structure exhibiting ductile material behavior, where micropores (spherical in shape) form and grow together only under load and associated plastic deformation, the material portion of a porous structure is already weakened by the number and size of existing pores, rendering plastic deformation unachievable. This phenomenon is exacerbated by the various parameter-dependent pore sizes and geometries, such as star-shaped pores with sharp edges, which can accelerate material failure due to an increased notch effect.

Besides ductile and brittle fracture behavior, another material behavior was identified in the course of this study, namely semi-brittle.
This material behavior, which is a mixture of ductile and brittle char-
acteristics, was observed in specimens with an average total pore area
between >1.114% and <3.490%. The stress-strain curves consist of the
elastic range (Hooke's law), which terminates abruptly with a sharp,
short curve representing the plastic range, as depicted in Fig. 16 (d). In
addition to the basic necking of the fracture pieces, the fracture surfaces
display an inclination that varies between 45° for ductile fractures and
90° for brittle fractures in relation to the longitudinal axis of the spec-
imens. Similar to brittle material behavior, it is presumed that the de-
gree of porosity is crucial for semi-brittle material behavior. Here,
existing voids also appear to contribute to a reduction in plastic defor-
mation, although not as prominently as in the case of brittle material
behavior. The amount of remaining material and the number of bars are
still sufficient to ensure at least partial plastic deformation. The different
structures and the associated material behavior are also reflected in the
measured strength values. Specimens with ductile material behavior
exhibit the highest tensile strengths (404.2 MPa–459.0 MPa), followed
by specimens with semi-brittle material behavior (326.1 MPa–318.5
MPa), and specimens with brittle material behavior (105.3 MPa–330.4
MPa).

The influences of the various printing parameter sets on the material
behavior are also evident in the fracture surfaces of the tensile speci-
mens, as depicted in the SEM images of specimens "c10," "i2," and "a7"
in Fig. 17. The microstructure of specimen "a7," categorized as ductile,
shows a minimal presence of pores and cavities, with no unmelted
powder material due to the small pore size. It also displays visible
tearing ridges that formed as a result of plastic deformation during the
tensile test. In contrast, the microstructure of specimen "i2," which is
semi-brittle, has fewer tearing ridges, larger pores, and some unfused
powder particles. Specimen "c10," identified as brittle, lacks any tearing
ridges and exhibits a greater number and size of pores compared to both
specimens "i2" and "a7." Consequently, the amount of unfused powder
material is also higher in "c10."

With regard to material behavior and strength, it is not only the
average total pore area — presented in Fig. 13 (a) — that plays a role,
but also the total number of pores, as can be seen in Fig. 15 (d). This
diagram — Fig. 15 (d) — illustrates a correlation between the total
number of pores and tensile strength, albeit with greater dispersion
around the trend line in the data points. It is presumed that this scat-
tering can be attributed to the different types of pores and their related
notch effect characteristics, as small spherical pores tend to exhibit a
lower notch effect in comparison to larger, irregularly shaped pores with
sharp edges.

When conducting the EBSD analysis of specimens “i2,” “c10,” and

"a16," difficulties arose regarding the structure of the specimens. The porosity, including aspects such as pore sizes, distributions, and geometries, created difficulties in detecting the surfaces of the specimens. Additionally, the variability in grain sizes, especially the presence of numerous small grains, further complicated the situation. As a result, a balance had to be achieved between measurement duration, which was approximately 200 h per specimen in this study, and resolution to obtain the most accurate results possible. Nevertheless, results regarding grain boundaries and sizes could only be determined for specimens "a16" and "c10," as depicted in Fig. 18.

Alongside substantial differences in porosity, specimens “c10,” with an average total pore area of 21.161%, and “a16,” with an average total pore area of 0.159%, also display significant variations in their grain boundaries and sizes. Whereas the structure of “a16” mainly comprises very small grains, “c10” displays grain sizes that are several orders of magnitude larger and distributed non-uniformly. Moreover, the grain boundary maps and grain size maps indicate that the grain sizes at the periphery of the meltpool are smaller than those at its center, a phenomenon also observed in other studies using the same base material [18].

A noticeable transition from finely distributed to coarse grains is also apparent, particularly evident in Fig. 18 (b). This phenomenon arises due to the specific grinding plane and the resulting perspective of various melted beads. Generally, the depiction of melted beads is primarily influenced by the rotation angle and the hatch distance. The rotation angle specifies the extent to which the exposure vectors, which represent the precise path the laser beam takes to selectively melt and fuse powder particles, are rotated after each layer. In this study, the rotation angle is 67°. To provide conclusive insights into grain sizes and melted beads, further investigations via EBSD are necessary, utilizing different grinding planes of specimens with a consistent rotation angle.

As part of the EBSD analysis, in addition to the grain boundary maps and grain size maps, Inverse Pole Figures (IPFs) were also generated, as shown in Fig. 19. Since these images all use the same scaling with color bars and display the same view of “Contours log scale relative intensity,” they can be directly compared. It is notable that the images of “c10” and “i2” are very similar. In both cases, a mixture of different crystal orientations can be observed. Specifically, not only are the faces of the crystals (001 - red) oriented parallel to the specimen surface, but also the edges of the crystals (101 - green). Conversely, in the case of “a16,” the faces of the crystals (001 - red) are predominantly oriented parallel to the specimen surface.

In addition to the three different material behaviors, the Portevin-Le
Chatelier (PLC) effect was also observed during tensile tests. This

<figure><img src="image_20.png" /><figcaption>**Fig. 17.** SEM images of fracture surfaces of specimens “c10”, “i2”, and “a7” at 100× magnification, showing the different microstructures that are decisive for the respective material behavior (ductile, semi-brittle, and brittle).</figcaption></figure>
<figure><img src="image_21.png" /><figcaption>Fig. 18. Results of the EBSD analysis of specimen “a16” (dense structure) and specimen “c10” (porous structure), showing the grain boundaries (a), grain sizes (b), and the respective grain size distributions (c) of the specimens.</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>Fig. 19. Inverse pole figures of the EBSD analysis of specimens “a16” (a), “c10” (b), and “i2” (c) which all show the contours log scale relative intensity and the same scaling (color bar).</figcaption></figure>

phenomenon occurs in low or medium-strength metallic materials, including both substitutional and interstitial alloys, and it manifests as abrupt or unsteady changes in tensile strength during plastic deformation. This sudden change in strength is reflected in a jagged pattern, characterized by a zigzag-shaped curve in the stress-strain diagram, as seen in the stress-strain curves of printing parameter set “14” and its enlarged representations in Fig. 20(a) and (b).

The PLC effect can have both negative and positive impacts on mechanical properties, such as increased ductility and elasticity, or an increased tendency to failure. This phenomenon can be explained by several microstructural mechanisms, including diffusion-controlled dislocation movement, phase transformations and dislocation hardening, or interactions between dislocations and other defects. These mechanisms and their interactions have varying effects on the structure's behavior during tensile tests, resulting in different manifestations

of stress-strain curves.

Schematic representations of the “serrated flow” behaviors, characterized by intermittent or jerky plastic deformation, are categorized into five groups labeled A to E and presented in Fig. 20 (c). However, these five types of serrations occur not only individually but also in mixed forms [44]. In this study, all specimens demonstrating ductile material behavior characterized by elastic-plastic deformation were identified as having mixed forms that include types B, D, and A + D, as illustrated in Fig. 20 (d).

Type A bands are characterized by periodic serrations, which are regular, repeating pattern of stress drops in the stress-strain curve. They originate from continuous deformation bands that initiate at the end of the specimen and propagate along its entire length. Typically, the stress-strain curve of this type exhibits an abrupt increase in flow stress followed by a drop below the average stress level [46]. Type B serrations,
<figure><img src="image_23.png" /><figcaption>**Fig. 20.** Stress-strain curves of specimens fabricated with printing parameter sets “14” (a) and their enlarged representation (b) showing the PLC effect (jagged curves). Schematic representation of different types of serrations of the PLC effect, taken from Ref. [45] (c). Stress-strain curve of specimen “a14” showing different types of PLC band types (d).</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>**Fig. 21.** Schematic representation of the influence of different settings of printing parameters on a DMLS structure. Different hatch distances (a), different meltpools (b), and different layer thicknesses (c) leading to dense and porous structures. SEM image showing the inner structure of specimen “c10” (d).</figcaption></figure>
which occur with increasing strain rate or temperature, are characterized by rapid stress fluctuations oscillating around the average stress level. Type D serrations represent a stepped form of the stress-strain curve, culminating in a plateau with minimal or no work hardening [44,45]. Identifying the various types of serrations and their underlying causes is often challenging due to the multitude of microstructural phenomena involved, including alloy composition, crystal lattice structure, type and quantity of solutes, mobile dislocation density, grain size, and type of obstacles [47].

Even within the scope of this study, it remains uncertain how the different mixed forms arise. Therefore, comprehensive investigations of the microstructure, such as measuring dislocation movements using Transmission Electron Microscopy (TEM), are crucial for thoroughly analyzing the various types of serrations.

## 4. Analysis

To achieve desired material characteristics using the DMLS process, assuming that porosity is a key factor, further analyses are necessary to understand how various parameters affect the meltpool and, consequently, the structure, as illustrated schematically in Fig. 21. A comparison of dense and porous structures demonstrates that modifying even a single printing parameter can significantly influence porosity. Adjusting one printing parameter impacts the meltpool formation, resulting structure, and associated material properties, such as tensile strength. However, because all four key parameters were changed simultaneously in the DoE, it is challenging to isolate the effects of individual parameters on the specimen structure. Furthermore, this overall complexity is increased by the fact that printing parameter sets, which result in non-continuous meltpool formation, lead to inhomogeneous structures with gaps that are difficult to model, as shown schematically in Fig. 22. Despite this, the study identifies dominant and non-dominant key parameters essential for the multi-physical optimization of DMLS structures. Various strategies and methods are employed in research and industry to investigate the interactions and effects of factors in a process or system as part of DoE. These include approaches such as one factor at a time, several factors one at a time, several factors simultaneously, and fractional factorial experimental designs like those proposed by Taguchi [48].

Statistical software solutions address essential aspects of DoE, from

the design phase to evaluation. They facilitate tasks like plotting main effects diagrams and interaction diagrams [49]. The main effects represent the direct influence that each factor has on the response variable. The main effect of a factor reflects how the changes in the factor's levels affect the response. It is calculated by determining the difference between the average response values at the different levels of the factor, as shown in Equation (3).

$$ME_A = \frac{Y_{A2} - Y_{A1}}{2} \qquad (3)$$

$ME_A$ represents the main effect of factor A, where $Y_{A1}$ is the mean response value at the lowest level (level 1), and $Y_{A2}$ is the mean response value at the highest level (level 2).

Interactions describe how two or more factors jointly affect the response. If the effect of one factor varies depending on the levels of another factor, an interaction exists. Interactions are calculated by analyzing the differences in the mean response values across different combinations of factor levels, as shown in Equation (4).

$$IE_{A,B} = \frac{1}{4} [(Y_{A1,B1} + Y_{A2,B2}) - (Y_{A1,B2} + Y_{A2,B1})] \qquad (4)$$

$IE_{A,B}$ represents the interaction effect between factors A and B. $Y_{A1,B1}$, $Y_{A2,B2}$, $Y_{A1,B2}$, and $Y_{A2,B1}$ denote the mean response values for the different combinations of levels for factors A and B [50].

For the creation of simulation models, it is important to understand the influence and interactions of various printing parameters on the microstructure and, consequently, on the resulting material properties. Consequently, the impact of the printing parameters, including laser power, laser speed, hatch distance, and layer thickness, on the measurement results such as tensile strength, Young's modulus, yield strength, and average total pore area was examined and assessed using Minitab software from Minitab Inc. The focus here is on the "main effects plots" and the "interaction plots," as shown from Figs. 23-26, specifically in subfigures (a) and (b), respectively.

In the main effects plots, it is clear that the various factor levels significantly affect the mechanical and structural properties of the specimens. This is indicated by the large distance between the individual factor levels relative to the Y-axis (Mean), as shown schematically in Fig. 23 (a). It is also apparent that the trends of the factor levels in the

<figure><img src="image_25.png" /><figcaption>Fig. 22. Schematic representation of two superimposed layers showing the influence of non-continuous meltpool formation on the structure: rotation angle of 90° (a) and 20° (b).</figcaption></figure>
<figure><img src="image_26.png" /><figcaption>(a) Main Effects Plot for Tensile strength (Rm) [MPa]
Data Means</figcaption></figure>

large distance indicating high
influence of factor levels on
material characteristic

<figure><img src="image_27.png" /><figcaption>(b) Interaction Plot for Tensile strength (Rm) [MPa]
Data Means</figcaption></figure>

overlap of trend lines
indicating interaction of
factors

<figure><img src="image_28.png" /><figcaption>(e) Histogram</figcaption></figure>

<figure><img src="image_29.png" /><figcaption>(g) Pareto Chart of the Standardized Effects</figcaption></figure>

<figure><img src="image_30.png" /></figure>

(response is Tensile strength (Rm) [MPa]; α = 0,05)

<figure><img src="image_31.png" /><figcaption>(f) Versus Order</figcaption></figure>

<figure><img src="image_32.png" /><figcaption>**Fig. 23.** Evaluation of the average tensile strength using Minitab: main effects plot (a), interaction plot (b), normal probability plot (c), versus fits plot (d), histogram (e), versus order plot (f), and pareto chart of the standardized effects (g).</figcaption></figure>
<figure><img src="image_33.png" /><figcaption>(b)
Interaction Plot for Elastic modulus
Data Means</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>(c)
Normal Probability Plot</figcaption></figure>

<figure><img src="image_35.png" /><figcaption>(d)
Versus Fits</figcaption></figure>

<figure><img src="image_36.png" /><figcaption>(e)
Histogram</figcaption></figure>

<figure><img src="image_37.png" /><figcaption>(f)
Versus Order</figcaption></figure>

<figure><img src="image_38.png" /><figcaption>(g)
Pareto Chart of the Standardized Effects
(response is Elastic modulus; α = 0,05)</figcaption></figure>

<figure><img src="image_39.png" /><figcaption>**Fig. 24.** Evaluation of the average Young's modulus using Minitab: main effects plot (a), interaction plot (b), normal probability plot (c), versus fits plot (d), histogram (e), versus order plot (f), and pareto chart of the standardized effects (g).</figcaption></figure>
<figure><img src="image_40.png" /><figcaption>(c) Normal Probability Plot</figcaption></figure>

<figure><img src="image_41.png" /><figcaption>(e) Histogram</figcaption></figure>

<figure><img src="image_42.png" /><figcaption>(g) Pareto Chart of the Standardized Effects
(response is Yield strength (re) [MPa]; α = 0,05)</figcaption></figure>

<figure><img src="image_43.png" /><figcaption>**Fig. 25.** Evaluation of the average yield strength using Minitab: main effects plot (a), interaction plot (b), normal probability plot (c), versus fits plot (d), histogram (e), versus order plot (f), and pareto chart of the standardized effects (g).</figcaption></figure>

<figure><img src="image_44.png" /><figcaption>(d) Versus Fits</figcaption></figure>

<figure><img src="image_45.png" /><figcaption>(f) Versus Order</figcaption></figure>

<figure><img src="image_46.png" /></figure>
<figure><img src="image_47.png" /><figcaption>(c) Normal Probability Plot</figcaption></figure>

<figure><img src="image_48.png" /><figcaption>(e) Histogram</figcaption></figure>

<figure><img src="image_49.png" /><figcaption>(g) Pareto Chart of the Standardized Effects</figcaption></figure>

<figure><img src="image_50.png" /><figcaption>**Fig. 26.** Evaluation of the average total pore area using Minitab: main effects plot (a), interaction plot (b), normal probability plot (c), versus fits plot (d), histogram (e), versus order plot (f), and pareto chart of the standardized effects (g).</figcaption></figure>

<figure><img src="image_51.png" /><figcaption>(d) Versus Fits</figcaption></figure>

<figure><img src="image_52.png" /><figcaption>(f) Versus Order</figcaption></figure>

<figure><img src="image_53.png" /></figure>
main effects plots for the mechanical properties presented in Figs. 23 to 25 (a) are entirely contrary to those observed in the main effects plot for the average total pore area shown in Fig. 26 (a). This further demonstrates that the mechanical properties are closely linked to the microstructure, specifically to the porosity.

Additionally, in the main effects plots for the mechanical properties shown in Figs. 23–25(a)–(c), it is evident that the center point of the individual factors yields the highest characteristic values, with the exception of the factor of layer thickness concerning tensile strength and yield strength. This is because the center points for the factors laser power, laser speed, and hatch distance correspond to the settings of the standard parameter set, which typically ensures maximum density and strength.

In the interaction plots shown from Figs. 23–25, subfigures (b), similarities in the trends of the factor levels for the mechanical properties can also be observed, as the trend lines for tensile strength, Young's modulus, and yield strength are nearly congruent. Similar to the main effects plots, the trend lines for the factor levels of the mechanical properties in the interaction plots in Figs. 23–25, subfigures (b), also move in the opposite direction compared to the trend lines for the factor levels of the average total pore area, as shown in Fig. 26 (b). A comparison between the various interaction plots shows that the layer thickness factor has the greatest interaction with the other factors, as evidenced by the contact or overlap of the respective trend lines, as shown schematically in Fig. 23 (b).

Additional diagrams generated during the evaluation of the DoE using Minitab, alongside the main effects and interaction plots, include “normal probability plots,” “versus fits” plots, “histograms,” “versus order” plots, and “Pareto charts of standardized effects.” These diagrams are presented in Figs. 23–26, specifically in subfigures (c) through (g).

The “normal probability plot” is used to evaluate whether a data set follows a normal distribution, which is crucial for making valid statistical inferences and interpreting the experimental results. The “versus fits” plot compares predicted values to actual observations, allowing for an assessment of the model’s quality and highlighting areas for potential improvement. The “histogram” provides insights into the statistical distribution of the data, enabling the identification of trends, underlying patterns, or potential issues. The “versus order” plot is used to detect any correlation between the observed responses and the order in which data were collected, helping to assess the stability and consistency of the experimental results throughout the study. Additional details on the DoE and these associated diagrams can be found in Ref. [49].

The normal probability plots for all three mechanical properties — presented from Figs. 23–25, specifically in subfigures (c) — reveal a similar distribution pattern of the data points. Most of the data points fall closely along the red line, indicating that the residuals are approximately normally distributed. This suggests that the assumptions of the statistical model used in the DoE are valid, and the conclusions drawn from the model are reliable. A few points deviate significantly from the line at both ends, specifically in the lower left and upper right regions, as highlighted in red in Fig. 23, subfigure (c). These deviations may indicate potential outliers or non-normality in the tails of the distribution. However, since the majority of the points are close to the red line, these deviations might not significantly affect the overall validity of the model.

Furthermore, the versus fits plots for tensile strength, Young's modulus, and yield strength, presented in subfigures (d) of Figs. 23–25, all exhibit a similar distribution pattern of data points. Ideally, in a well-fitted model, the residuals should be randomly scattered around the horizontal axis (the zero line) with no clear pattern. This randomness indicates that the model accurately predicts the response across all levels of the factor combinations and that the errors are distributed equally for all fitted values. In these plots, the points are somewhat spread out across the fitted value range, and most are close to the zero line. However, a few larger residuals are evident, particularly on the left side (at low fitted values) and some on the right, as highlighted in Fig. 23,

subfigure (d), suggesting potential areas where the model may underfit or overfit. There is no obvious pattern (such as a curve or funnel shape) in the residuals, which is good. A clear pattern could suggest issues like non-linearity, non-constant variance (heteroscedasticity), or the need for a different model structure. The absence of such patterns here suggests that the model does not violate key assumptions like constant variance. While most residuals are close to zero, some extreme points (high or low residuals) are visible, particularly at lower fitted values. This could suggest that the model does not predict well for certain data points, potentially due to underfitting for those specific conditions.

Similar to the previous DoE diagrams, the histograms for all three mechanical properties — depicted in Figs. 23–25, subfigures (e) — exhibit a comparable distribution. The bulk of the residuals are concentrated near zero, indicating that for most of the data points, the model’s predictions are close to the actual observed values. This is a good sign that the model is generally accurate in predicting the response. The histogram should ideally be symmetric, resembling a normal distribution (bell curve). In this case of the histograms for the mechanical properties, the distribution is fairly centered around zero, although slightly skewed to the left. Most residuals are small, suggesting that the errors are minimal and the model performs well. The smaller bars on the far left and far right — highlighted in Fig. 23 (e) — represent larger residuals (errors), which indicate a few cases where the model’s predictions deviate more significantly from the actual values. These outliers might need to be investigated further, as they could indicate specific conditions under which the model does not perform well.

In the versus order plots of the three mechanical properties, shown in Figs. 23–25 as subfigures (f), there are identifiable similarities in the distribution of the data points. Ideally, the residuals should be randomly scattered around zero, with no obvious pattern or trend over the observation order. This would indicate that the model’s errors are consistent and there is no systematic drift or variation in the data collection process over time. In the plots of tensile strength, Young’s modulus, and yield strength, the residuals do not seem to follow a clear trend but fluctuate above and below zero. While the points are somewhat evenly spread, there is a bit of variability, but nothing that indicates a severe issue like a systematic error or time-dependent trend. The presence of spikes, especially around observations 60 and 65 — highlighted in red in Fig. 23 (f), indicates a few points where the prediction error is larger, but this can happen sporadically in real-world data. This plot is particularly useful for detecting patterns that might be related to the sequence in which the data was gathered. For example, if there were trends, drifts, or cycles in the residuals, it could suggest that external factors such as machine wear or environmental conditions might have affected the results as the experiment progressed. In this case, no such systematic behavior is strongly visible. A few points, particularly around observations 25 and 65 — highlighted in green in Fig. 23 (f) — exhibit significant residuals, both positive and negative, suggesting the presence of outliers. These may require further investigation to understand if there were anomalies in the data collection process at those points.

Moreover, the Pareto charts of standardized effects for all three mechanical properties, presented in subfigures (g) of Figs. 23–25, reveal consistent results. Factor “D” (layer thickness) has the strongest influence on the Young’s modulus, followed by the interaction “AD” (laser power and layer thickness). Additional significant effects come from “A” (laser power) and “B” (laser speed), as well as the interaction “BD” (laser speed and layer thickness). Interactions such as “ABC” (laser power, laser speed, and layer thickness) have a smaller influence as they fall below the red significance line.

In contrast to the various plots for tensile strength, Young’s modulus, and yield strength, which exhibit consistent patterns in the data points, the plots for average total pore area display no such resemblance to those for the mechanical properties. It can be observed that the data points for the average total pore area do not follow the typical patterns expected in a DoE analysis. This irregularity is primarily attributed to
the limited number of repeated measurements — in this study, only a single measurement of the average total pore area was conducted for each specimen. The constraint on multiple measurements is a result of the high costs associated with both specimen production and testing.

In the normal probability plot shown in Fig. 26 (c), the majority of the residuals are clustered around zero and generally follow the red line, suggesting that the residuals are mostly normally distributed. However, one data point deviates significantly from the line (on the left, highlighted in red), indicating an outlier. This point corresponds to printing parameter set “10,” which has a very low VED, leading to high porosity (21.161%). Given the limited number of repeated measurements (only one per specimen), the residuals exhibit minimal variation, resulting in little scatter and indicating a very small variance in the errors.

In the versus fits plot of the average total pore area shown in Fig. 26 (d), most data points lie very close to the zero residual line (y = 0), suggesting that the model generally fits the observed values well. The small scatter of residuals may be attributed to the limited number of measurements (only one per specimen), which reduces the statistical significance. One point stands out with a distinctly negative residual (around a fitted value of 0, highlighted in red), indicating a potential outlier. This outlier corresponds to printing parameter set “10,” which has high porosity. The predicted values (x-axis) span a narrow range from 0 to approximately 22, with most points clustered in the lower range, particularly near 0. This distribution suggests that the model performs well for small values but may exhibit weaknesses with higher values. Due to the single measurement per specimen, the true scatter or variability of the residuals is difficult to assess, limiting the conclusions that can be drawn about the overall model quality, as statistical fluctuations are not well represented.

The histogram for the average total pore area shown in Fig. 26 (e) exhibits significant deviations not only when compared to the histograms of the mechanical properties but also from the typical patterns expected in a Design of Experiments (DoE) analysis. The majority of the residuals are concentrated around 0, indicating that the model performed well in predicting most of the measurements. However, a few significantly negative residuals (approximately -2.5, highlighted in red) suggest potential measurement errors. In reality, these residuals correspond to consistently high measured values associated with printing parameter “10.” Given that only one measurement was taken per specimen, detecting outliers is challenging.

In the versus order plot shown in Fig. 26 (f), the majority of data points lie precisely at a residual of 0. This is a direct result of the low number of measurement replicates (one per specimen), which leads to minimal variability in the residuals. The final data point (bottom right, highlighted in red) exhibits a significantly negative residual (around -2.5), indicating that the model’s prediction for this observation was notably inaccurate. This corresponds to the outlier previously identified in printing parameter set “10.” Beyond this, there is no evident trend in the residuals over the order of observations, suggesting no temporal dependency or systematic variation during the experiment. The residuals remain constant across most observations. Given the single measurement per specimen, residual scatter is minimal, limiting the detection of potential systematic errors or random fluctuations. Conducting multiple measurements per specimen would allow for a more detailed and reliable analysis of residual variability.

The Pareto chart of standardized effects related to the average total pore area, as shown in Fig. 26 (g), displays certain similarities to the corresponding charts for mechanical properties. The factor “D” (layer thickness) exhibits the strongest and most significant influence on the pore area, as indicated by its bar being the highest and well above the red significance threshold. This highlights that thickness plays a critical role in determining pore structure. The interaction “BD” (the combination of laser speed and layer thickness) is also statistically significant, suggesting that the concurrent variation of these two factors substantially impacts the pore area. Similarly, the interaction “AD” (laser power and layer thickness) has a significant effect, although weaker than “BD.”

Both “B” (laser speed) and “A” (laser power) exhibit significant effects, though their influence is not as pronounced as that of “D.”

To better identify dominant and non-dominant factors, the results of the main effects plots and the interaction plots were summarized, as shown in Fig. 27.

The table in Fig. 27 (a) illustrates the influence of the individual factors (key parameters) on the respective material characteristics. The rating (“1”-“4”) of each material characteristic, determined using the main effects plots, represents the distance between the respective factor levels relative to the Y-axis (mean), as depicted schematically below the table.

Fig. 27 (a) illustrates that the individual factors have an equal effect on the mechanical properties. The layer thickness has the most significant effect, serving as the dominant factor, followed by laser power and laser speed. In contrast, the hatch distance has the least influence and is considered a non-dominant factor. The layer thickness stands out as the dominant factor due to its broadest range relative to the volume of powder material being exposed. In other words, this parameter offers the greatest flexibility in regulating the absorption of thermal energy input per unit volume over time. The hatch distance, on the other hand, has the smallest flexibility of altering the thermal energy input. The laser power and laser speed factors, which are both essential for generating the meltpool, are interdependent. Depending on their respective values, they can produce a wide range of energy inputs per unit volume over time. For instance, high laser power combined with a slow laser speed results in a large energy input, while low laser power paired with a fast laser speed leads to a minimal energy input.

To substantiate these statements regarding dominant and non-dominant factors, the interactions of the key parameters under consideration were summarized and evaluated as a whole, as shown in Fig. 27 (b). This table summarizes all interactions of the key parameters across all material characteristics. The interactions were identified from the underlying interaction plots. Parameter combinations where the trend lines do not touch are classified as “no interaction,” while trend lines that touch or overlap are classified as “interaction,” as schematically shown under the table in Fig. 27 (b).

The findings from Fig. 27 (b) reinforce those from Fig. 27 (a), indicating that layer thickness has the most significant influence on material characteristics, making it the dominant parameter. This is evident from the high number of interactions with other key parameters. Additionally, the conclusion about the non-dominant key parameter, hatch distance, is confirmed, as it shows the fewest interactions.

The classification of printing parameters into dominant and non-dominant categories must always be considered individually and depends on numerous factors, including the range of the selected factor levels. These levels are crucial for the energy input into the powder bed, specifically determining the manner in which the energy is introduced. The energy input into the powder bed depends on both a temporal and a spatial component, as summarized in Equation (1).

The temporal component includes the factors laser power and laser speed. These factors are directly interrelated, meaning different combinations can yield similar results, such as porosity and related material characteristics. For instance, high laser power combined with high laser speed can produce the same results as low laser power combined with low laser speed [50]. The spatial component encompasses the factors layer thickness and hatch distance. Layer thickness typically has a greater influence on the volume to be exposed compared to hatch distance, as it must be appropriately sized to match the grain size of the powder material. Additionally, when varying layer thicknesses are used within a single print job, they must always be multiples of a base value, which inherently increases the volume of powder material to be exposed.

The hatch distance generally has the least impact on the structure and is affected by various factors, including the rotation angle and temporal components such as laser power and laser speed. The rotation angle significantly affects the resulting structure, as it determines the
<figure><img src="image_54.png" /><figcaption>**Fig. 27.** Summary of the results from the main effects plots (a) and the interaction plots (b) in relation to tensile strength, Young's modulus, yield strength, and average total pore area.</figcaption></figure>

orientation of the laser vectors across different layers. The relative shift of the vectors, whether 90° or 0°, can markedly alter the final structure. Additionally, the temporal component affects the hatch distance by enabling compensation for the spacing between laser vectors through the thickness of the melt bead [3].

## 5. Recommendations

To generate meaningful predictive simulation models of the DMLS process, which can lead to a reduction in development costs, a number of factors need to be taken into account:

* Consider simulation models for the DMLS process structured in two stages. First, the behavior of the meltpool and resulting geometry corresponding to the single laser tracks should be simulated based on the printing parameter sets generated in this study. Following this, the simulation should extend to three-dimensional topologies with a focus on their structures, considering different degrees of porosity and the resulting material properties.

* The results of experimental studies relating to a certain base material, which serve as the basis for data-driven simulation models, may not be readily transferred to other materials or alloys due to various material-dependent process influences and framework conditions. The printing systems and powder materials available on the market are also highly unlikely to deliver the same results in terms of quality and buildability, making direct comparisons difficult [15-17].

* To maintain a low overall complexity in the simulation models for the material examined in this study [51], it seems adequate to concentrate on the key dominant parameters, which include layer thickness, laser power, and laser speed. The hatch distance can be disregarded because of its minimal effect on the structure and the related material properties, as discussed in Section 4.

* To increase the informative value of simulation models while simultaneously reducing complexity [52], it is recommended to use printing parameter sets that result in an "optimum continuous" meltpool formation. These include printing parameter sets "5," "6," "7," "8," "9," "12," and "16" of Table 2 (see Section 3).

* The creation of comprehensive databases to securely store and provide access to experimentally collected data in the field of additive manufacturing (AM) is essential. Centrally collected, well-organized, and rigorously validated data should contribute to the improvement and expansion of simulation models, enhancing their accuracy and reliability by reducing the need for assumptions and approximations.

* It is advisable to validate this experimental study, which involves the fabrication and testing of specimens, by encouraging other research or industrial institutions to replicate and extend the work. Conducting additional material tests on various scales will not only strengthen the reliability of the findings but also improve the overall data quality. Reproducing existing studies enhances the robustness of results, while further material tests can provide deeper insights into complex phenomena, such as the PLC effect discussed in Section 3, potentially uncovering new aspects and contributing to more comprehensive models.

## 6. Feasibility study of single-material composites

This section focuses on validating the combination of parameter-dependent structures in one single geometry and their significance in mechanical topology optimization. Various test specimens were manufactured, each incorporating two distinct sets of printing parameters to create a SMC. These specimens underwent tensile testing to evaluate their mechanical performance. The primary aim of these experiments is to establish the practicality of merging multiple printing parameter sets into a single structure, while also analyzing their effects on the mechanical properties of the resulting materials.

Different types of specimens were produced, each featuring two structural categories: dense and porous. In particular, the specimens include a fine truss-lattice structure formed using printing parameter set "3," which is embedded in a porous matrix made with parameter set "10." This configuration is depicted in Fig. 28 (a).

The two printing parameter sets were chosen based on their VED, resulting porosity, and corresponding average mechanical properties. VED was a critical factor, as large variations in energy input could create challenges during specimen fabrication. Parameter set "3," with a VED of 74.82 J/mm³, produces a structure with very low porosity of 0.26% and a high tensile strength of 448.4 MPa. In contrast, parameter set "10," with a VED of 14.96 J/mm³, results in a porosity of 21.16%, which significantly reduces tensile strength to 105.3 MPa.

By combining various printing parameter sets within a unified geometry, a SMC is created, where the same powder material is employed throughout all structures. This approach enables precise adjustment and control of material characteristics at targeted locations, thus enhancing both performance and functionality. For the filigree truss-lattice designs, three unique variations were conceptualized: "fiber," "helix," and "rope," as illustrated in Fig. 28 (b).

The test specimens include dense areas at their ends, specifically
<figure><img src="image_55.png" /><figcaption>Fig. 28. Schematic illustration of the fabrication process for a SMC using DMLS (a). 3D models of the structures “fiber,” “helix,” and “rope,” which are embedded as dense structures within a porous structure during printing (b).</figcaption></figure>

designed to allow for secure attachment of threads for tensile testing. To assess the influence of different structural configurations on the mechanical properties, particularly tensile strength, two versions were fabricated for each design. These variants differ in the cross-sectional area of the filigree structures: one with an area of 5 mm² and the other with a larger area of 20 mm².

A total of 33 specimens were produced, with 30 of them distributed across five distinct grid positions (“a,” “c,” “e,” “g,” and “h”) on the build platform, where each variation of the geometry was printed once per grid location. The remaining three specimens, printed separately on grid “f,” were reserved for metallographic examination. All specimens were created using the same powder material and printing system, ensuring consistency with the settings used for the cuboid-shaped specimens and single laser tracks. After printing, the specimens underwent precipitation hardening, the same heat treatment applied to the cuboid-shaped specimens. To prepare for tensile testing, M10 threads were machined onto the ends of the specimens, as shown in the fractured examples in Fig. 29.

The tensile tests were then performed using the same testing machine and adhered to the same standard (ISO 6892-1:2019, Method A [37]) applied to the cuboid-shaped specimens. The results, including the average tensile strength values for the different specimen configurations, are displayed in Fig. 30.

The measurement data in Fig. 30 clearly highlights the variation in tensile strength, ranging from 89.8 MPa to 112.2 MPa, across the different geometries — namely “fiber,” “helix,” and “rope.” Additionally, there is a significant disparity in the respective cross-sectional areas of the specimen variations, which are either 5 mm² or 20 mm². These observations suggest that the mechanical properties of a structure are markedly affected by both its geometric configuration and the proportion of ductile components present. Furthermore, the orientation of the embedded filigree (ductile) geometry relative to the load direction plays a crucial role in determining the measurement outcomes.

<figure><img src="image_56.png" /><figcaption>Fig. 30. Average tensile strength of different specimen geometries (“fiber,” “helix,” and “rope”) and different cross-sections (5 mm² and 20 mm²) of the ductile material component.</figcaption></figure>

The analysis of the corresponding stress-strain curves was conducted alongside the evaluation of tensile strength values. Notably, while the curves demonstrated strong reproducibility, they also exhibited unusual behavior for a ductile material like aluminum, lacking plastic deformation. This atypical response is particularly illustrated by the stress-strain curve associated with the “rope” geometry, as shown in Fig. 31.

The behavior exhibited by the stress-strain curves can likely be traced to the large cross-sectional area of the porous structure, which promotes a brittle response. Despite the presence of a denser material component (either 5 mm² or 20 mm²) within the cross-section, it remains insufficient to induce noticeable plastic deformation. This lack of a plastic region in the stress-strain curves is also likely a result of the sudden failure of the brittle outer layer, which leads to an abrupt load transfer to the finer, more ductile inner structure, overwhelming its capacity to absorb the stress. This sudden failure is evident not only in

<figure><img src="image_57.png" /><figcaption>Fig. 29. Fracture fragments (a) and fracture surface (b) of tensile specimen which consists of two different structures (dense structure embedded in porous structure).</figcaption></figure>
<figure><img src="image_58.png" /><figcaption>Fig. 31. Stress-strain curves of the “rope” geometry with a ductile cross-section of 5 mm² (a) and 20 mm² (b).</figcaption></figure>

the stress-strain curves but also in the jagged, uneven fracture surfaces of the broken specimens, as shown in Fig. 29 (b).

The uneven and cracked fracture surfaces are likely due to localized irregularities in the porous structure. These imperfections may arise from factors such as inconsistent distribution of alloying elements, variations in pore dimensions, or differences in grain sizes. Each of these factors can create areas of concentrated stress, leading to crack initiation under tensile load. Fig. 32 illustrates an example of a crack within the porous matrix at varying magnifications.

Additionally, it is important to highlight that the intricate truss-lattice structures embedded within the porous matrix are distinctly visible on the fracture surfaces. This is evidenced by the microscopic images shown in Fig. 33, taken at different magnifications using the VHX-7000 Digital Microscope from Keyence Corporation.

Although made from the same alloy, the dense truss-lattice structures display noticeable visual differences compared to the surrounding porous matrix, allowing for easy identification.

Metallographic specimens were then prepared to evaluate how effectively different printing parameter sets could be distinguished within the specimen structures. The remaining three specimens, one from each category — “fiber,” “helix,” and “rope” — were sectioned longitudinally, embedded in epoxy resin (VersoCit-2 - Struers GmbH), and polished using a multi-step process with LaboPol-5 equipment

(Struers GmbH). This process involved three grinding stages followed by two polishing stages.

At the beginning of the grinding process, the specimens were treated using a carrier disk (MD-Gekko - Struers GmbH) outfitted with a 120 µm Silicon Carbide (SiC) film, rotating at 300 rpm under a contact pressure of 15 N, with water acting as the lubricant. The second grinding stage maintained identical operational settings but employed a finer 320 µm SiC film. In the third phase, the grinding tool was changed to an MD-Largo disk (Struers GmbH) featuring a 9 µm grain size, with DiaPro Allegro/Largo (Struers GmbH) polishing suspension taking the place of water as the lubricant. For this stage, the speed was lowered to 150 rpm, and the contact pressure was increased to 30 N to further refine the surface.

In the next stage, the specimens underwent polishing using an MD-Mol carrier disk (Struers GmbH) combined with a 3 µm abrasive and Diapro Mol R3 lubricant (Struers GmbH). The process operated at 150 rpm with a contact pressure of 25 N. For the final polishing phase, an MD-Nap-T carrier disk (Struers GmbH) with a 1 µm abrasive was utilized, alongside Diapro Nap R1 lubricant (Struers GmbH), maintaining the same speed but reducing the contact pressure to 15 N for a finer surface finish. Fig. 34 displays an image of the embedded specimen, a schematic of its cross-section, and the corresponding RLM images.

The distinct, parameter-specific structures (dense and porous) of the

<figure><img src="image_59.png" /><figcaption>Fig. 32. Various magnifications of a crack within the porous matrix of a SMC formed during tensile testing.</figcaption></figure>
<figure><img src="image_60.png" /><figcaption>Fig. 33. Microscopic images of fracture surfaces of different fragments of the SMC after the tensile test: Angled view (a), and (b). Top view (c).</figcaption></figure>

<figure><img src="image_61.png" /><figcaption>**Fig. 34.** Schematic representation of a specimen cross-section and associated RLM images showing the dense structure embedded in a porous structure, including transition zones (a). Image of a micrograph showing the dense structure embedded in a porous structure visible to the naked eye (b).</figcaption></figure>

specimens, as seen in Fig. 34 (a), are easily visible to the naked eye, much like the fracture surface images depicted in Fig. 33. A particularly noteworthy feature is the pronounced boundary between the two structures, with the transition zone standing out clearly due to its contrasting textures and colors.

The RLM images in Fig. 34 (b) also distinctly reveal the different structures and their respective transition zones. Interestingly, the visibility of these zones varies considerably. For example, in the filigree structure ("detail 1"), the transition to the porous matrix is characterized by a broad and clearly defined boundary. On the other hand, areas with higher material volume ("detail 2") display a much narrower transition, where the shift between structures is more direct, lacking visible cavities.

In the course of this validation process, SMCs were produced using two distinct sets of printing parameters to determine their technical viability and thoroughly assess their influence on the mechanical properties of the material. The findings highlighted that varying printing parameter sets within a single geometry can significantly affect the material's characteristics, including tensile strength and ductility, which are critical factors for successful topology optimization. These insights not only reinforce the importance of parameter selection in additive manufacturing but also guide future efforts in optimizing material performance for diverse applications.

Composites in which both the matrix and the reinforcing phase consist of the same material or alloy offer a distinct advantage over conventional multi-material composites, which typically combine different substances (e.g., glass fibers in an epoxy resin matrix). The main advantage of these SMCs lies in their superior sustainability. When produced using DMLS, these materials achieve the same recyclability level as that of traditionally manufactured metals, making material recovery more straightforward and significantly lowering their environmental footprint.

Due to their heterogeneous nature, conventional composite materials

often end up being disposed of through methods such as landfilling, incineration, or costly recycling practices, which can pose significant environmental challenges [53]. The recycling processes involved tend to be energy-intensive and can include various techniques like mechanical recycling, cement kiln processing, thermal recycling, and chemical recycling. Each of these methods has its own set of complexities and limitations, often leading to additional environmental impact and increased costs [54]. As a result, there is a growing need for more sustainable alternatives that can reduce waste and improve resource recovery in the field of composite materials.

The later stages of this research highlighted the potential of employing combined printing parameter sets for creating complex SMCs. As a result, a thorough examination of the material properties remains unfinished. There is a need for further investigation, particularly into the bonding characteristics of surface layers across different structures and how mechanical properties are influenced by geometric factors, to gain a more comprehensive understanding of the observed phenomena.

## 7. Conclusion

Single laser tracks (3 × 17 tracks) and cuboid-shaped specimens (5 × 17 specimens) were manufactured using 17 different printing parameter sets from aluminum powder (Al–Mg–Zr-Sc) on an EOS M290 printing system. The printing parameter sets were generated using a full factorial experimental design, incorporating laser power (P) [W], laser speed (v) [mm/s], hatch distance (h) [mm], and layer thickness (t) [mm] as factors, each with two factor levels (high and low). A center point was also included, representing the mean values of the low and high factor levels. The cuboid-shaped specimens underwent a hardening process (precipitation hardening — 6 h at 350 °C with slow cooling in the air). One set (17 specimens) was further analyzed for microstructure investigations using RLM and SEM, while four other sets (68 specimens) were prepared
for tensile tests. The single laser tracks were subjected to metallographic examinations via RLM without heat treatment.

The microstructure investigations of the cuboid-shaped specimens revealed a diverse range of porosities spanning from 0.037% to 21.161%. These porosities were found to be homogeneously distributed and comprised various pore sizes and geometries, including spherical pores, keyhole pores, and lack of fusion. Only one microcrack was identified in the specimens' SEM images, rendering this phenomenon negligible. Furthermore, EDX analysis of three specimens with varying degrees of porosity — 0.22%, 1.11%, and 21.16% — indicated a homogeneous distribution of alloying elements.

The microscopic examinations of the single laser tracks revealed a substantial influence of the different printing parameters on the melt-pool. Various types and sizes of weld beads were identified in both the cross-sections, including keyhole formation, semi-circular U-shape, insufficient substrate penetration, and in the top view, which revealed optimum continuous and non-continuous weld beads.

The results of the tensile tests also demonstrate a wide range of strength values dependent on parameters (tensile strength: 105.3 MPa-459.0 MPa; Young's modulus: 23,38 GPa - 69,77 GPa; yield strength: 105.2 MPa-444.8 MPa), along with good reproducibility. Based on stress-strain curves and fractured surfaces, three porosity-dependent material behaviors were identified: ductile (porosity $\le 1.113\%$), semi-brittle (porosity between $>1.114\%$ and $<3.490\%$), and brittle (porosity $\ge 3.491\%$). Ductile specimens exhibited the PLC effect and various mixed serration types (B, D, and A + D) in the stress-strain curves. EBSD analyses conducted on fragments from three tensile specimens with different material behaviors were challenging due to the structure of the specimens and the very small grains. Grain size measurements could only be conducted on two of the three fragments, revealing significant differences. Conversely, the inverse pole figures did not exhibit such large disparities between the three specimens.

In future studies, EBSD analyses should encompass all specimens to precisely determine the microstructure, including grain sizes and grain boundaries, for all 17 printing parameter sets. Ensuring representative results requires producing new specimens with a consistent rotation angle and examining several grinding planes per specimen. Such a meticulous analysis of microstructures should also enhance understanding of the various material behaviors (brittle, semi-brittle, and ductile) and the different types of serrations associated with the PLC effect.

SMCs were successfully fabricated using two distinct sets of printing parameters within a uniform geometry to validate the impact of parameter variation on mechanical topology optimization. Test specimens featuring combinations of dense and porous regions were printed, hardened, and subjected to tensile testing, revealing that the mechanical properties — particularly tensile strength values, which ranged from 89.8 MPa to 112.2 MPa — are closely tied to the geometric arrangement and cross-sectional dimensions of the embedded truss-lattice structures. The observed brittle failure with limited plastic deformation is likely due to the high proportion of the porous matrix in these structures. This study highlights the potential of SMCs as a sustainable alternative to traditional multi-material composites, primarily due to their recyclability. Future work is required to further elucidate the influence of structural geometry on bonding and mechanical properties, advancing the development of optimized, eco-friendly composite materials.

## Statements and declarations

This work was conducted as part of a research collaboration between the Additive Manufacturing in Agile Virtual Systems for Product Design and Production Process Design (AMAViS²) research group, the Carinthia Institute for Smart Materials (CiSMAT), and the Laboratoire ROBERVAL of the Université de Technologie de Compiègne (UTC). Both AMAViS² and CiSMAT are affiliated with the Carinthia University of Applied Sciences (CUAS). The primary funding for this work was provided by

AMAViS².

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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