Laser-based Additive Manufacturing Processes Research Articles

Genetic Algorithm-Based Framework for Optimization of Laser Beam Path in Additive Manufacturing

In this study, a genetic algorithm-based laser beam (LB) path optimization method is presented to improve laser-based additive manufacturing (LBAM). To emulate the LBAM process, LB irradiation of a thin metal substrate is applied. The LB path generation is formulated as the search for the optimal sequence of LB irradiation into the cells on the substrate that minimizes the fitness function, which is composed of two components, i.e., thermal fitness and process fitness. The thermal fitness is expressed by the average thermal gradient, and a simple thermal model is developed to simulate the effects of laser-induced heat input on the temperature distribution in the substrate. The process fitness regulates the suitability of the proposed LB path for the implementation of the LBAM process. In addition to standardized tool paths (i.e., raster, spiral, etc.), novel LB path generators are proposed to define the initial population of LB path solutions. To implement a genetic algorithm-based LB path optimization, a framework is proposed, and custom initialization, crossover, and mutation operators are developed for application in LBAM. The effectiveness of the proposed approach is demonstrated through a simulation case study aiming to identify LB paths that minimize the fitness function and thus provide more suitable LB path solutions with respect to the defined fitness function. Compared with the traditional trial-and-error LB path formulations, the proposed approach provides an improved and automated method for an efficient laser beam path selection in LBAM.

Understanding the role of laser processing parameters and position-dependent heterogeneous elastocaloric effect in laser powder bed fused NiTi thin-walled structures

To reduce the driving load and enhance the heat exchange capacity and elastocaloric refrigeration efficiency, increasing interests in porous structure design and laser-based additive manufacturing (LAM) of NiTi materials with a large specific surface area have been emerging. As a type of characteristic unit of porous components, we mainly focused on the LAM process optimization and elastocaloric effect of NiTi-based thin-walled structures (TWSs) in this work. Firstly, we systemically studied the influence of laser processing parameter on the forming quality and phase transformation behavior of NiTi-based TWS samples. Results showed that high relative density (>99.0%) was inclined to be obtained in a range of 67–133 J mm−3 (laser energy density). Besides, the transformation temperatures (TTs) and enthalpy change roughly showed a positive linear relationship with the applied laser energy density. At an optimized parameter (P = 100 W and v = 1000 mm s−1), the sample exhibited a high relative density (99.88%), good dimensional accuracy, and the lowest TTs. Then, this work emphatically unveiled the position-dependence of phase transformation behavior and elastocaloric effect (eCE) in a NiTi-based TWS sample. It was found that both the TTs and enthalpy change monotonously decreased along the building direction, while the transformation strain kept an increase trend. As a result, the middle portion of the sample exhibited the largest adiabatic temperature change which reached 6.5 K at the applied stain of 4%. The variation in TTs and eCE could be attributed to the heterogeneous solidification microstructure induced by the thermal cycle nature of LAM process.

Effects of Oxidized Metal Powders on Pore Defects in Powder-Fed Direct Energy Deposition.

Laser-based additive manufacturing processes, particularly direct energy deposition (DED), have gained prominence for fabricating complex, functionally graded, or customized parts. DED employs a high-powered heat source to melt metallic powder or wire, enabling precise control of grain structures and the production of high-strength objects. However, common defects, such as a lack of fusion and pores between layers or beads, can compromise the mechanical properties of the printed components. This study focuses on investigating the recurrent causes of pore defects in the powder-fed DED process, with a specific emphasis on the influence of oxidized metal powders. This research explores the impact of intentionally oxidizing metal powders of hot work tool steel H13 by exposing them to regulated humidity and temperature conditions. Scanning electron microscopy images and energy-dispersive X-ray spectroscopy results demonstrate the clumping of powders and the deposition of iron oxides in the oxidized powders at elevated temperatures (70 °C for 72 h). Multi-layered depositions of the oxidized H13 powders on STD61 substrate do not show significant differences in cross sections among specimens, suggesting that oxidation does not visibly form large pores. However, fine pores, detected through CT scanning, are observed in depositions of oxidized powders at higher temperatures. These fine pores, typically less than 250 µm in diameter, are irregularly distributed throughout the deposition, indicating a potential degradation in mechanical properties. The findings highlight the need for careful consideration of oxidation effects in optimizing process parameters for enhanced additive manufacturing quality.

Printed layers height calibration curve and porosity in laser melting deposition of Ti6Al4V combining experiments, mathematical modelling and deep neural network

PurposePorosity is a commonly analyzed defect in the laser-based additive manufacturing processes owing to the enormous thermal gradient caused by repeated melting and solidification. Currently, the porosity estimation is limited to powder bed fusion. The porosity estimation needs to be explored in the laser melting deposition (LMD) process, particularly analytical models that provide cost- and time-effective solutions compared to finite element analysis. For this purpose, this study aims to formulate two mathematical models for deposited layer dimensions and corresponding porosity in the LMD process.Design/methodology/approachIn this study, analytical models have been proposed. Initially, deposited layer dimensions, including layer height, width and depth, were calculated based on the operating parameters. These outputs were introduced in the second model to estimate the part porosity. The models were validated with experimental data for Ti6Al4V depositions on Ti6Al4V substrate. A calibration curve (CC) was also developed for Ti6Al4V material and characterized using X-ray computed tomography. The models were also validated with the experimental results adopted from literature. The validated models were linked with the deep neural network (DNN) for its training and testing using a total of 6,703 computations with 1,500 iterations. Here, laser power, laser scanning speed and powder feeding rate were selected inputs, whereas porosity was set as an output.FindingsThe computations indicate that owing to the simultaneous inclusion of powder particulates, the powder elements use a substantial percentage of the laser beam energy for their melting, resulting in laser beam energy attenuation and reducing thermal value at the substrate. The primary operating parameters are directly correlated with the number of layers and total height in CC. Through X-ray computed tomography analyses, the number of layers showed a straightforward correlation with mean sphericity, while a converse relation was identified with the number, mean volume and mean diameter of pores. DNN and analytical models showed 2%–3% and 7%–9% mean absolute deviations, respectively, compared to the experimental results.Originality/valueThis research provides a unique solution for LMD porosity estimation by linking the developed analytical computational models with artificial neural networking. The presented framework predicts the porosity in the LMD-ed parts efficiently.

Additive manufacturing of graphene reinforced 316L stainless steel composites with tailored microstructure and mechanical properties

Since its inception, additive manufacturing has facilitated the processing of novel metal matrix composites that are challenging to fabricate using conventional manufacturing methods. The laser-based additive manufacturing process is a promising technology to fabricate uniformly dispersed graphene-reinforced stainless steel nanocomposite. In this study, the effect of energy density modulation on densification, microstructure, and mechanical properties of (316L) stainless steel reinforced with 0.2 wt% multi-layered graphene nanosheets was investigated. The highest relative densification (94.7% ± 0.5) has been achieved at the volumetric energy density of 240 J/mm3. The formation of dislocation cells, inclusions and graphene-steel interaction have been identified in the microscopic analysis. Addition of graphene as reinforcement and nano-inclusions formed (in situ) during solidification promotes the pinning of dislocations around the cellular sub-grains. Alteration of volumetric energy density is an essential technique to tune the texture and microstructure of the additively manufactured samples. As the volumetric energy density increased to 240 J/mm3, the tensile strength achieved is around 1165 ± 5 MPa.

Machine learning based prediction of melt pool morphology in a laser-based powder bed fusion additive manufacturing process

Laser-based powder bed fusion (L-PBF) has become the de facto choice for metal additive manufacturing (AM) processes. Even after considerable research investments, components manufactured using L-PBF lack consistency in their quality. Realizing the crucial role of the melt pool in controlling the final build quality, we predict the morphology of the melt pool directly from the build commands in an L-PBF process. We leverage machine learning techniques to predict quantitative attributes like the size as well as qualitative attributes like the shape of the melt pool. The area of the melt pool is predicted using an LSTM network. The outlined LSTM-based approach estimates the area with 90.7 % accuracy. The shape is inferred by synthesising the images of the melt pool by using a Melt Pool Generative Adversarial Network (MP-GAN). The synthetic images attain a structural similarity score of 0.91. The precision and accuracy of the results showcase the efficacy of the outlined approach and pave the way for real-time monitoring and control of the melt pool to build products with consistently better quality.

High frequency beam oscillation keyhole dynamics in laser melting revealed by in-situ x-ray imaging

The metal additive manufacturing industry is actively developing instruments and strategies to enable higher productivity, optimal build quality, and controllable as-built microstructure. A beam controlling technique, laser oscillation has shown potential in all these aspects in laser welding; however, few attempts have been made to understand the underlying physics of the oscillating keyholes/melt pools which are the prerequisites for these strategies to become a useful tool for laser-based additive manufacturing processes. Here, to address this gap, we utilized a synchrotron-based X-ray operando technique to image the dynamic keyhole oscillation in Ti-6Al-4V using a miniature powder bed fusion setup. We found good agreement between the experimental observations and simulations performed with a validated Lattice Boltzmann multiphysics model. The study revealed the continuous and periodic fluctuations in the characteristic keyhole parameters that are unique to the oscillating laser beam processing and responsible for the chevron pattern formation at solidification. In particular, despite the intrinsic longer-range fluctuation, the oscillating technique displayed potential for reducing keyhole instability, mitigating porosity formation, and altering surface topology. These insights on the oscillating keyhole dynamics can be useful for the future development and application of this technique.

Investigations of process by-products by means of Schlieren imaging during the powder bed fusion of metals using a laser beam

To produce geometrically complex parts with good mechanical properties, various industries increasingly apply the powder bed fusion of metals using a laser beam. Sufficient control of this laser-based additive manufacturing process is necessary to achieve a reliable development as well as reproducible part properties. Besides adjusting the standard process parameters, such as the laser power or the scanning speed, the process gas is an important parameter that influences the part characteristics. In addition to the inertization of the atmosphere, process by-products can be removed during the manufacturing with a constant gas flow across the build plate. Typically, argon or nitrogen is applied. This study investigated the effects of various argon-helium mixtures in comparison to pure argon gas on the heat balance of process by-products during the processing of a high-strength aluminum alloy by means of Schlieren imaging. The method enables visualizations of the process by-products and is further capable of studying evaporation phenomena. For this purpose, a Schlieren imaging setup was designed and installed in an AconityMINI machine. The experimental results were exploited to validate a simplified heat transfer model introduced in this work. The results indicate that the addition of helium to the process gas lowers the amount of fumes and incandescent spatters and thus could decrease evaporation. Also, it was found that the higher thermal conductivity of helium compared to argon leads to significantly higher cooling of process by-products.

Continuous feeding of powders with vibration and pneumatic pressure assistance for laser-based additive manufacturing processes

Continuous feeding of powders is critical to ensure manufacturing quality in laser-based additive manufacturing processes. However, current powder delivery systems are either not capable of continuous feeding of powders or cannot guarantee that the powders achieve a certain motion velocity to assure their uninterrupted motion into the melt pool. Therefore, a new powder delivery system is developed which use the advantages of vibration and pneumatic feeders. In this powder delivery system, vibration is used to disperse agglomerated powders and control the powder federate while pneumatic pressure is used to increase powder velocity to guarantee that the powder enters the melt pool. Powder flow simulation has been conducted to reveal powder flow characteristics. To test the feasibility of the developed powder delivery system for powder delivery and verify the correctness of powder flow simulation results, powder delivery experiments have been conducted. Effects of argon gas flowrate and vibration parameters on powder flowrate have been investigated. Experimental results show that a stable and repeatable powder flowrate can be controlled by adjusting the vibration parameters and the argon gas flowrate with the developed delivery system.

Influence of processing on the microstructure of nickel aluminum bronze (NAB)

Nickel-aluminum bronze (NAB) alloys are commonly used for marine applications such as propellers by the U.S. Navy. These NAB components are conventionally manufactured using casting techniques, but recent interest has shifted to the possibility of additive manufacturing (AM) processes. The following literature review discusses the microstructural evolution of (nominally) Cu-9Al-5Ni-5Fe-1Mn NAB alloys in the cast, wrought, friction stir processed, arc-welded, electron beam welded and laser-welded conditions. NAB alloys exhibit a complex microstructure, consisting of a mixture of five κ precipitates distributed in an α or β’ martensitic matrix. The size, morphology, and distribution of these phases are sensitive to changes in cooling rate and thermal history such as those imposed by AM and welding. Although the microstructure is well documented under casting conditions, no continuous cooling diagrams have been developed to describe phase transformations under arc and laser processing conditions for NAB. As such, this review investigates recent reports related to arc, electron beam and laser-based additive manufacturing processes and the associated microstructures and properties. Studies have reported additively manufactured NAB alloys with improved properties compared to cast material, associated with differences in microstructure. At moderate cooling rates, such as those associated with arc-based processing, the microstructure consists of Widmanstätten α and various κ phases, which become more refined with increasing cooling rate. This leads to an improvement in the tensile properties of wire-arc direct energy deposited NAB, with > 17% increase in yield strength and > 90% increase in ductility when compared to cast material. At rapid cooling rates representative of laser-based and electron beam processing, the microstructure consists primarily of a twin-related β’ martensite with nanoscale κ phases. This leads to a significant increase in yield strength (>60–100%) at the expense of ductility (<−30%) when compared to cast material. Additionally, previous studies have reported that the refined microstructure from these high cooling rate processes leads to an improvement in the corrosion resistance of as deposited NAB. There is also evidence that the conventional heat treatments for cast NAB alloys need to be optimized for additively manufactured materials. The report includes the methods and challenges associated with characterizing NAB alloys under these processing conditions and provides recommendations for future research.

Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing

Finding actionable trends in laser-based metal additive manufacturing process monitoring data is challenging owing to the diversity and complexity of the underlying physical interactions. A single monitoring solution that captures a particular process phenomenon, such as a photodiode that tracks melt pool intensity, is not alone capable of evaluating process stability or detecting flaw formation with sufficient precision for routine application in industry. In this work, to improve flaw detection performance, we adopted a data fusion approach that captures multiple process phenomena. To demonstrate this, we acquired data from laser powder bed fusion (LPBF) builds of cylindrical specimens produced with different laser spot sizes, emulating defocusing due to process faults such as thermal lensing. The resulting specimens had porosity of varying types and severity, quantified by post-build non-destructive X-ray computed tomography, Archimedes density measurements, and destructive metallographic characterization. During the build, the melt pool state was monitored with two coaxial high-speed video cameras and a temperature field imaging system. Physically intuitive low-level melt pool signatures, such as melt pool temperature, shape and size, and spatter intensity were extracted from this high-dimensional, image-based sensor data. These process signatures were subsequently used as input features in relatively simple machine learning models, such as a support vector machine, which were trained to detect laser defocusing, and in addition, predict porosity type and severity. The results show that the data fusion approach significantly enhanced system performance by reducing the overall false positive rate from ∼ 0.1 to ∼ 0.001 without sacrificing the true positive rate (∼0.90). These results were at par with a black-box, deep machine learning approach (convolutional neural network).

Orientation-selected micro-pillar compression of additively manufactured 316L stainless steels: Comparison of as-manufactured, annealed, and proton-irradiated variants

Irradiation response and deformation mechanisms of additively manufactured (AM) 316 L stainless steel were studied by atomic scale characterization and micro-pillar compression. The AM 316 L stainless steels were fabricated by direct energy deposition, a laser-based additive manufacturing process. Irradiation with 2 MeV protons at 360 °C was performed to create ∼1.8 displacements-per-atom (dpa) damage in AM 316 L. Deformation behaviors of the as-manufactured, annealed, and proton-irradiated variants were studied, focusing on the effects of manufacturing-induced pores, residual stress, and irradiation-introduced defects (dislocation loops and voids). Micro-pillars were prepared from grains of pre-selected orientation, avoiding contributions of grain boundaries and allowing determination of resolved shear stress on {111} glide planes. Transmission electron microscopy was used to characterize the pre- and post-deformation microstructure. It was found that in the as-manufactured alloy variant, moving dislocations were the major deformation carrier, with noticeable blocking by fabrication-induced pores, In the annealed variant, hardness was reduced, and deformation was also accomplished by dislocation gliding. In the proton-irradiated variant, significant twinning was observed. Comparing measured resolved shear stress and predicted critical stress for dislocation dissociation, we conclude that irradiation hardening became high enough to activate twinning. Therefore, the deformation mechanism changes from dislocation gliding to twinning. The study is important for both processing optimization and performance evaluation of AM alloys for reactor applications.

Influence of tool path planning on process stability and deposition accuracy in laser material deposition with coaxial wire feed

Laser material deposition (LMD) is a laser-based additive manufacturing process that is widely used for fabrication, diversification, and repair of parts in various industries. LMD processes can be distinguished by the form of the filler material. In terms of availability, storage, safe handling, and price, filler material in wire form often has advantages over powdered materials. In this work, the influence of tool path planning on the deposition accuracy and the process stability of LMD with coaxial wire feed is studied. The laser beam and wire are arranged coaxially to each other, with the wire being fed through the inside of an annular shaped laser beam without any shadowing. While the process is completely directionally independent, the stability is linked to the wire feed rate, laser power, and process feed rate. Additionally, the ratio between the wire and the laser beam diameter affects the stability. Movement of the wire inside the annular beam may result in shadowing and process abort. Changes in the working distance (defocusing) lead to changes in the beam diameter, which affects the process stability. This can result in imperfections, i.e., bonding defects, or pores in the tracks and layers that will add up in the built components and must be avoided for imperfection-free three-dimensional geometries. On even surfaces, the geometry of the deposited tracks is controlled by changing the laser power, the wire feed speed, and the process speed. When building three-dimensional parts, critical points of the tool path planning are acute angled corners and crossing points. In the experiments, boundaries for the process stability in critical points are established and track geometries are compared to previous results. Furthermore, different strategies for the avoidance of material accumulation at the start, end, and crossing points are investigated. The different approaches are discussed, and finally an outlook for further use and possible applications is given.

Dynamic optical beam shaping system to generate Gaussian and top-hat laser beams of various sizes with circular and square footprint for Additive Manufacturing applications

The speed of production in laser-based additive manufacturing processes is rather low compared to conventional manufacturing methods and could be improved by using a top-hat laser beam intensity profile, a variable spot size or a combination of both, enabling wider melt pools during processing. This while retaining or possibly even improving part quality. In this work, the design and implementation of a dynamic optical beam shaping system is presented to enable different shapes and sizes of top-hat beams at the build platform of a Laser Powder Bed Fusion machine which is equipped with a fiber laser emitting at 1070nm. The system has been demonstrated by assembling custom-made beam shapers with commercially available beam expanders and by measuring the intensity profiles. Initial results indicate the possibilities for tuning of the spot size at the build platform by a magnification factor ranging from 1x to 4x. Parameter optimization for the Gaussian beam with 4x magnification was performed and a first 316L demonstrator part is further discussed in this work to illustrate a gain in productivity of 44%, while retaining >99% density.

Dimensions of Smart Additive Manufacturing

Dimensions of Smart Additive Manufacturing

In Situ Monitoring of Powder Bed Fusion Homogeneity in Electron Beam Melting.

Increasing attention has been devoted in recent years to in situ sensing and monitoring of the electron beam melting process, ranging from seminal methods based on infrared imaging to novel methods based on backscattered electron detection. However, the range of available in situ monitoring capabilities and solutions is still quite limited compared to the wide number of studies and industrial toolkits in laser-based additive manufacturing processes. Some methods that are already industrially available in laser powder bed fusion systems, such as in situ detection of recoating errors, have not yet been investigated and tested in electron beam melting. Motivated by the attempt to fill this gap, we present a novel in situ monitoring methodology that can be easily implemented in industrial electron beam melting machines. The method is aimed at identifying local inhomogeneity and irregularities in the powder bed by means of layerwise image acquisition and processing, with no external illumination source apart from the light emitted by the hot material underneath the currently recoated layer. The results show that the proposed approach is suitable to detect powder bed anomalies, while also highlighting the link between the severity of in situ detected errors and the severity of resulting defects in the additively manufactured part.

Diffraction-Based Residual Stress Characterization in Laser Additive Manufacturing of Metals

Laser-based additive manufacturing methods allow the production of complex metal structures within a single manufacturing step. However, the localized heat input and the layer-wise manufacturing manner give rise to large thermal gradients. Therefore, large internal stress (IS) during the process (and consequently residual stress (RS) at the end of production) is generated within the parts. This IS or RS can either lead to distortion or cracking during fabrication or in-service part failure, respectively. With this in view, the knowledge on the magnitude and spatial distribution of RS is important to develop strategies for its mitigation. Specifically, diffraction-based methods allow the spatial resolved determination of RS in a non-destructive fashion. In this review, common diffraction-based methods to determine RS in laser-based additive manufactured parts are presented. In fact, the unique microstructures and textures associated to laser-based additive manufacturing processes pose metrological challenges. Based on the literature review, it is recommended to (a) use mechanically relaxed samples measured in several orientations as appropriate strain-free lattice spacing, instead of powder, (b) consider that an appropriate grain-interaction model to calculate diffraction-elastic constants is both material- and texture-dependent and may differ from the conventionally manufactured variant. Further metrological challenges are critically reviewed and future demands in this research field are discussed.

Solid-state additive manufacturing of tantalum using high-pressure cold gas-dynamic spray

Additive manufacturing (AM) of refractory metals, including tantalum (Ta), is highly valued due to the wide application of these materials in different industrial sectors where outstanding mechanical properties at elevated temperatures are required. Among metal AM processes, newly introduced cold gas-dynamic spray or, more commonly, cold spray (CS) offers a unique opportunity for solid-state consolidation of refractory metals. This research presents the CS process as a method for additive manufacturing of Ta. Following the successful manufacturing of free-standing Ta, an extensive mechanical characterization at the macro and nano levels has been carried out to evaluate the material's structural integrity. Anisotropy in the mechanical properties, which is one of the major concerns in the AM-produced materials, was extensively analyzed at both the macro and nano levels. The produced Ta's resistance against the pre-existing crack was studied by evaluating the far-field J-integral versus the crack extension (J-R curve). The influence of microstructural characteristics and process-induced defects such as pores and micro-cracks on the Ta's mechanical and fracture properties was studied to explain the performance-microstructure linkage. The macro and nanomechanical testing results indicated an elastic modulus and ultimate tensile strength in the range of vacuum-arc melted or electron-beam melted Ta ingots following cold-working. The microstructure analysis demonstrated a mixture of ultrafine grains and highly elongated coarse grains, explaining the CS-produced Ta high strength. Moreover, excellent isotropy in the mechanical properties was observed at both the macro and nano levels. This finding distinguishes the CS process from the laser-based AM process in which mechanical properties highly depend on the build direction. On the other hand, the CS-produced Ta exhibited brittle characteristics during uniaxial tensile loading and ductile behavior during the uniaxial compression test. Also, stable crack growth accompanied by crack branching was observed in the CS-produced Ta. The crack branching and formation of secondary cracks have been identified as the mechanisms retard crack extension. Overall, this research revealed that the CS process is a promising AM technique for producing tantalum-based components.

The significant impact of grain refiner on γ-TiAl intermetallic fabricated by laser-based additive manufacturing

Intermetallic γ-TiAl alloy has been considered as a promising structural material for high temperature use, owing to their advantages such as low density, high creep strength and good toughness. However, due to its intrinsic brittleness, γ-TiAl alloy could not accommodate the high thermal stress generated by fast cooling during laser-based additive manufacturing (LAM) process, resulting in cracking and distortion. To surmount the roadblock of low processability associated with γ-TiAl alloy, grain refinement technology was integrated to LAM. In the present work, LaB 6 nanoparticles were identified and experimentally verified, for the first time, as an effective inoculant for LAMed γ-TiAl alloy. The results showed that the grain size of the as-printed alloy drastically decreased from 39.81 ± 9.12 µm to 1.5 ± 2.07 µm through 0.5 wt% LaB 6 addition. Accordingly, the microstructural morphology transformed from coarse near-lamellar colonies into equiaxed and fine grains. The LaB 6 inoculation treatment not only led to the fabrication of crack-free alloy but concurrently increases in compressive yield strength, ultimate strength and strain to fracture by 29%, 12.4% and 61.9%, respectively. Such an enhanced performance is comparable with or even better than the γ-TiAl alloys produced using conventional processing techniques.

Effects on the distortion of Inconel 718 components along a hybrid laser-based additive manufacturing process chain using laser powder bed fusion and laser metal deposition

The combination of laser powder bed fusion (LPBF), known for its geometrical freedom and accuracy, and the nozzle-based laser metal deposition process (LMD), known for its high build-up rates, has great potential to reduce the additive manufacturing times for large metallic parts. For the industrial application of the LPBF-LMD hybrid process chain, it is necessary to investigate the influence of the LMD process on the LPBF substrate. In addition, the build plate material also has a significant impact on the occurrence of distortion along the additive manufacturing process chain. In the literature, steel build plates are often used in laser-based additive manufacturing processes of Inconel 718, since a good metallurgical bonding can be assured whilst reducing costs in the production and restoration of the build plates. This paper examines the distortion caused by LMD material deposition and the influence of the build plate material along the hybrid additive manufacturing process chain. Twin cantilevers are manufactured by LPBF and an additional layer is subsequently deposited with LMD. The distortion is measured in the as-built condition as well as after heat treatment. The effect of different LMD hatch strategies on the distortion is determined. The experiments are conducted using the nickel-base alloy Inconel 718. The results show a significant influence of LMD path strategies on distortion, with shorter tool paths leading to less distortion. The remaining distortion after heat treatment is considerably dependent on the material of the build plate.