Abstract Additive Manufacturing Research Articles

The role of unit cell topology in modulating the compaction response of additively manufactured cellular materials using simulations and validation experiments

Abstract Additive manufacturing has enabled a transformational ability to create cellular structures (or foams) with tailored topology. Compared to their monolithic polymer counterparts, cellular structures are potentially suitable for systems requiring materials with high specific energy-absorbing capability to provide enhanced damping. In this work, we demonstrate the utility of controlling unit-cell topology with the intent of obtaining a desired stress-strain response and energy density. Using mesoscale simulations that resolve the unit-cell sub-structures, we validate the role of unit-cell topology in selectively activating a buckling mode and thereby modulating the characteristic stress-strain response. Simulations incorporate a linear viscoelastic constitutive model and a hyperelastic model for simulating large deformation of the polymer under both tension and compression. Simulated results for nine different cellular structures are compared with experimental data to gain insights into three different modes of buckling and the corresponding stress-strain response.

A Manufacturability Evaluation of Complex Architectures by Laser Powder Bed Fusion Additive Manufacturing

Abstract Additive manufacturing (AM) enables new possibilities for the design and manufacturing of complex metal architectures. Incorporating lattice structures into complex part geometries can enhance strength-to-weight and surface area-to-volume ratios for valuable components, particularly in industries such as medical devices and aerospace. However, lattice structures and their interconnections may result in unsupported down-skin surfaces, potentially limiting their manufacturability by metal AM technologies, such as laser powder bed fusion (LPBF). This study aimed to examine the correlation between down-skin surface area and the manufacturability of lattice structures fabricated using LPBF. Image processing algorithms were used to analyze down-skin surface areas of seven unique lattice designs and to devise quantitative metrics (such as down-skin surface area, discrete surface count, surface interconnectivity, down-skin ratio, over-print/under-print volumes, etc.) to evaluate LPBF manufacturability. The seven lattice designs were subsequently manufactured using maraging steel via LPBF and then examined using imaging using X-ray micro-computed tomography (XCT). The geometric accuracy of the lattice designs was compared with XCT scans of the manufactured lattices by employing a voxel-based image comparison technique. The results indicated a strong relationship between down-skin surface area, surface interconnectivity, and the manufacturability of a given lattice design. The digital manufacturability evaluation workflow was also applied to a medical device design, further affirming its potential industrial utility for complex geometries.

Design Optimization of Lattice Structures Under Impact Loading for Additive Manufacturing

Abstract Additive manufacturing (AM) has enabled the production of intricate lattice structures with excellent performance and minimal mass. Design approaches that consider static loading, including lattice-based topology optimization (TO), have been well-researched recently. However, to date, there appears to be no widely accepted method of optimizing lattice structures for high-strain rate loading, especially when the design for additive manufacturing (DFAM) principles are considered. This study proposes a computational framework for the design of lattice structures under specified impact loading. To manage dimensionality while achieving sufficient generality, a heuristic design space is developed that relies on traditional TO to govern the design's macrostructure and standard dimensioning to govern its mesostructure. DFAM principles are then incorporated into a Bayesian optimization scheme wrapped around traditional TO to achieve manufacturable designs that absorb high-impact loading. Because this approach does not require analytical gradient information, the framework can be used to optimize directly on complex objectives, such as injury metrics calculated from the acceleration curve. A series of case studies is formulated around a mass-performance tradeoff and involves individual unit cell design as well as full-part design. The proposed design parameterization is found to enable sufficient flexibility to achieve consistently good performance regardless of AM build orientation.

Operational wear behaviour of 3D-printed lightweight metal gears: EDS and oil analysis comparison

Abstract Additive manufacturing (AM) has come to the fore in recent years among manufacturing techniques. This technique, which has different advantages than traditional ones such as casting, forging and machining, is expected to be widely used in producing machine parts like gears in the coming years. Therefore, experimental data on AM parameters for lightweight metal gears are important for industrial production. In this study, a wear test was applied to AlSi10Mg and Ti6Al4V gears under operational conditions, and the wear behaviour of conventionally and additively manufactured gears was compared. The amount of abrasion elements was determined by analysing the oil in the gearbox. In addition, gear surfaces were analysed using scanning electron microscopy and an energy-dispersive spectrometer before and after wear. Thus, the wear behaviour of gears produced by conventional and AM under service conditions was demonstrated comparatively.

Determination of the Effective Fused Deposition Modeling Process Parameters on the Mechanical Properties of Carbon Fiber–Reinforced Polypropylene Parts

Abstract Additive manufacturing is gaining popularity for producing components in industries such as automotive, aerospace, and medicine due to its potential to minimize material waste. Because the strength of the 3-D-printed part is so important, it’s crucial to do research and optimize process parameters to make the printed parts as strong as possible. This work focuses on the experimental investigation and discussion of the tensile and impact strength of parts made from carbon fiber–reinforced polypropylene (PP-CF) using the fused deposition modeling (FDM) technique. Various process parameters, including infill pattern, infill density, layer thickness, and build orientation, are examined on three different levels to determine their influence on the tensile and impact strength of the printed part. The outcomes of the analysis of variance (ANOVA) analysis reveal that infill density primarily affects impact strength, whereas layer thickness significantly influences tensile strength. The optimal combination of parameters leading to the maximum tensile and impact strength consists of a grid infill pattern, 60 % infill density, 0.36-mm layer thickness, and a 45° build orientation. Furthermore, fracture surface analysis is consistent with mechanical test results.

Manipulating ionic conductivity through chemical modifications in solid-state electrolytes prepared with binderless laser powder bed fusion processing

Additive manufacturing of solid-state batteries is advantageous for improving the power density by increasing the geometric complexity of battery components, such as electrodes and electrolytes. In the present study, bulk three-dimensional Li1+x Al x Ti2−x (PO4)3 (LATP) electrolyte samples were prepared using the laser powder bed fusion (L-PBF) additive manufacturing method. Li3PO4 (LPO) was added to LATP to compensate for lithium vaporization during processing. Chemical compositions included 0, 1, 3, and 5 wt. % LPO. Resulting ionic conductivity values ranged from 1.4 × 10−6–6.4 × 10−8 S cm−1, with the highest value for the sample with a chemical composition of 3 wt. % LPO. Microstructural features were carefully measured for each chemical composition and correlated with each other and with ionic conductivity. These features and their corresponding ranges include: porosity (ranging from 5% to 19%), crack density (0.09–0.15 mm mm−2), concentration of residual LPO (0%–16%), and concentration and Feret diameter of secondary phases, AlPO4 (11%–18%, 0.40–0.61 µm) and TiO2 (9%–11%, 0.50–0.78). Correlations between the microstructural features and ionic conductivity ranged from −0.88 to 0.99. The strongest negative correlation was between crack density and ionic conductivity (−0.88), confirming the important role that processing defects play in limiting the performance of bulk solid-state electrolytes. The strongest positive correlation was between the concentration of AlPO4 and ionic conductivity (0.99), which is attributed to AlPO4 acting as a sintering aid and the role it plays in reducing the crack density. Our results indicate that additions of LPO can be used to balance competing microstructural features to design bulk three-dimensional LATP samples with improved ionic conductivity. As such, refinement of the chemical composition offers a promising approach to improving the processability and performance of functional ceramics prepared using binderless, laser-based additive manufacturing for solid-state battery applications.

Predicting the Mechanical Behavior of Additively Manufactured Mechanical Metamaterials Using Point Cloud Representation Learning

Abstract Additive manufacturing (AM) has been widely adopted to produce mechanical metamaterials for load bearing, energy absorption, and other applications in various industries such as aerospace, automotive, and healthcare. However, geometric imperfections largely exist in AM. Since the mechanical behavior of metamaterials is primarily dependent on their geometries, it is critical to evaluate how process-induced geometric imperfections affect the mechanical behavior of fabricated metamaterials. Most of the existing approaches for AM quality control concentrate on the detection of defects and are limited in their ability to assess defect-altered mechanical behavior of finished builds. Some studies leverage destructive tests or numerical methods for mechanical behavior assessment, which are costly and time-consuming, and impractical for high-throughput routine quality control. In this paper, a new machine learning framework is developed to predict the mechanical behavior of fabricated metamaterials based on their as-built geometries (represented as high-resolution point clouds). Specifically, the point cloud is first converted into an image profile, which preserves detailed geometric patterns. then, a deep neural network is constructed to encode salient features of the image profile and associate them with the load-deflection curve of the fabricated metamaterial. The effectiveness of the developed framework is experimentally validated through a case study with auxetic mechanical metamaterial. This work has great potential to be extended for in-process prediction of AM builds’ mechanical behavior based on layer-wise point cloud scanning.

Investigation of bio-based and recycled printing materials for additive manufacturing

Additive manufacturing (AM) processes are becoming increasingly important alongside conventional processes. As a result, the consumption of materials is also increasing. The most widespread process in polymer AM is Fused Layer Modelling (FLM). Today, the FDM process often uses synthetically produced materials based on petrochemical processes. However, there is little knowledge about which bio-based and recycled polymer materials are suitable for sustainable polymer AM. The aim of this paper is to carry out investigations of eight selected materials, which are already commercially available, to gain insights into their suitability as materials for polymer AM. These materials are divided into four categories: conventional, recycled, bio-based and fibre-reinforced thermoplastics. The evaluation model consists of a point system in which the materials are evaluated according to various weighted criteria. For technical, economic and ecological evaluation meaningful criterions were developed and applied. Based on the evaluations, three two-dimensional strength diagrams were developed, from which the results of the materials, on two of the evaluations in each case, can be read. These results are combined in a three-dimensional diagram. This representation provides the ability to make a precise selection of bio-based or recycled materials for polymer AM.

Experimental and statistical investigation on flexural properties of FDM fabricated PLA specimens applying response surface methodology

Additive manufacturing (AM) is a modern technology currently adopted by manufacturing industries to benefit from its low-cost applications, versatility and fabrication of complex parts. Fused deposition modeling (FDM) is distinguished among the different AM technologies due to its fast, yet accurate operations. However the properties of fabricated components are strongly depended by FDM-related parameter settings. This work examines the effect of FDM-related parameters namely flow rate, printing speed and printing temperature on the response of flexural strength. Experiments according to L9 orthogonal array and a custom response surface experimental design were performed to obtain the results necessary for further examination and analysis corresponding to parameter effects on flexural strength and statistical outputs. Experiments were designed as per the ASTM D790 standard whilst failure modes of experimental samples were observed for correlating the independent printing parameters with the response of flexural strength. The full quadratic regression model generated for predicting results concerning flexural strength was found adequate for explaining the variation of FDM-related parameters on flexural strength response.

Inkjet printing P(VDF-TrFE-CTFE) actuators for large bending strains

Additive manufacturing of sensors and actuators together with structural materials and electronics will make it possible to fabricate innovative system designs that are overly laborious to realise with conventional methods. While printing of the structural materials and electronics are advancing fast, the additive manufacturing methods for actuators and sensors are in an earlier stage of development. This research will develop a manufacturing process for entirely inkjet printed electroactive polymer (EAP) actuators basing on the P(VDF-TrFE-CTFE) relaxor ferroelectric polymer and Ag electrodes. The process consists of (1) printing an Ag layer on a polyethylene terephthalate (PET) substrate for the bottom electrode; (2) formulating, printing and annealing a P(VDF-TrFE-CTFE) ink for the EAP layer; and (3) printing and sintering an Ag layer on the plasma-treated EAP surface to form the top electrode. Two actuator variations, addressed as DMC and KM512, are manufactured and characterised by their: (a) response to quasi-static excitation (1 Hz sine wave); (b) hysteresis behaviour; (c) actuation amplitude variation with the input voltage; and (d) frequency response. The 18 mm long actuators showed 91.4 µm (DMC, 200  Vpp ) and 224 µm (KM512, 275  Vpp ) deflections in response to 1 Hz sinusoidal excitation, and 1.10 mm (DMC, 113 Hz, 200  Vpp ) and 1.72 mm (KM512, 114 Hz, 200  Vpp ) deflections in resonant operation. It is 55% more quasi-static strain and 470% more resonant strain than in earlier fully inkjet-printed polyvinylidene fluoride (PVDF) -based actuators, and comparable to similar partially inkjet-printed actuators. This is the first time that inkjet printing of all three layers of a relaxor ferroelectric actuator have been achieved.

Optimal configurations of Minimally Intelligent additive manufacturing machines for Makerspace production environments

Abstract Additive manufacturing (AM) has transformed job shop production and catalysed the growth of Makerspaces, FabLabs, Hackspaces, and Repair Cafés. AM has enabled the handling and manufacturing of a wide variety of components, and its accessibility has enabled more individuals to make. While smaller than their production-scale counterparts, the objectives of minimizing technician overhead, capital expenditure, and job response time remain the same. The typical First-Come First-Serve (FCFS) operating model, while functional, is not necessarily the most efficient and makes responding to a-typical or urgent demand profiles difficult. This article reports a study that investigated how AM machines configured with Minimally Intelligent agents can support production in these environments. An agent-based model that simulated 5, 10, 15, and 20 AM machines operating a 9 am−5 pm pattern and experiencing a diverse non-repeating demand profile was developed. Machines were configured with minimal intelligence – FCFS, First-Response First-Serve (FRFS), Longest Print Time (LPT), Shortest Print Time (SPT), and Random Selection logics – that governed the selection of jobs from the job pool. A full factorial simulation totaling 15,629 configurations was run until convergence to a ranked list of production performance – min Job Time-in-System. Performance changed as much as 200%. Performant configurations featured a variety of logics, while the least performant were dominated by FCFS and LPT. All FCFS (a proxy for today’s operations) was one of the least performant configurations. The results provide an optimal set of logics and performance bands that can be used to justify capital expenditure and AM operations in Makerspaces.

Heat treatment and tensile test of 3D-printed parts manufactured at different build orientations

Abstract Additive manufacturing has been gaining popularity in many industries and has made significant growth over the last 5 years. Many industries use additive manufacturing three-dimensional (3D) printing to produce complex shape objects that is a challenge to be manufactured by casting or conventional methods. In this study, the impact of heat treatment and build orientation is examined for the mechanical characteristics of 3D-printed parts. The study used samples constructed of titanium alloy Ti-6Al-4V, which is frequently used in AM applications. The parts were printed at various build orientations such as 0°, 45°, and 90°. Following printing, the samples underwent four distinct heat-treatments at 0, 700, 800, and 900°C. The variation in mechanical properties (Young’s modulus, strain-stress, and tensile strength) has been monitored to determine the best heat treatment and tilt orientation to obtain the best mechanical properties. These findings provide a systematic analysis and support the 3D printing of the parts used with a desired mechanical strength.

Virtual Reality-Based Training in Additive Manufacturing: Developing a Service Concept that Adapts to Company-Specific Requirements

Additive manufacturing (AM), commonly known as 3D printing, has vast amount of technological opportunities, but it also necessitates specialized expertise among its specialists, adopters, users etc. As AM continues to demonstrate its potential and has high growth rates in fields of its use, applications, systems etc., the importance of AM education and training becomes increasingly evident. This paper serves as a preliminary study outlining a novel training concept for industrial AM training including microlearning units and conducted in virtual reality, focusing on the accessibility and flexibility of users. The concept has been developed as part of the ongoing industrial training project. Microlearning and virtual tool-assisted training methods offer effective and agile approaches to enhance the continuous learning of new industrial skills, enabling companies to keep pace with evolving technologies. These tools facilitate extensive customization of training, making company-specific training even more crucial for effective skill development and knowledge transfer. The design of this training concept leverages the user story mapping (USM) method, enabling a deeper understanding of company-specific requirements and needs concerning additive manufacturing training. Moreover, through questionnaires and interviews conducted during the USM process helps identify technology readiness levels (TRLs) of companies in relation to their AM adoption. Measuring TRLs provides valuable insights into the technical maturity of companies regarding additive manufacturing, as readiness levels tend to vary significantly among different organizations. The preliminary framework provided serves as a foundation for designing a modern training concept that cater to the unique requirements of different companies, fostering their successful adoption of AM technologies.

Options for Improving Performance of Additively Manufactured Nickel-Base Superalloys for Gas Turbine Applications

Abstract Additive manufacturing (AM) has been increasingly used for gas turbine (GT) components over the last decade. Many different components can be successfully designed, printed, and used in the gas turbine. However, there still exist questions on the use of AM components in hot-section areas. These components are typically fabricated from nickel-base superalloys that are known to have superior mechanical properties at elevated temperatures (e.g., creep and fatigue). Research over the last decade has been primarily focused on the printability of nickel-base superalloys, and there still exists a gap in understanding the high temperature processing–structure–properties–performance relationships of these alloy systems. This study evaluates the effect of processing methods, such as laser-based powder bed fusion (LBPBF) and electron beam powder bed fusion (EBPBF), on the resulting microstructure and time dependent mechanical properties of a nickel-base superalloy (ABD®900). Material after each build was subsequently heat treated using both near-solvus (at or slightly below the gamma prime solvus temperature) and super-solvus (above gamma prime solvus temperature) conditions. Multistep aging was then carried out to produce a bi-modal distribution of gamma prime precipitates as is typical in similar alloys. Microstructure was evaluated in both the as-built and fully heat-treated conditions for each processing technique. Mechanical testing was conducted to evaluate the effects of AM build methods, microstructure, and heat treatment on high temperature mechanical properties. The results show that there are several methods which can be used to improve the performance of components built using AM. The creep testing results for ABD900-AM clearly show an improvement in properties (rupture life and ductility) at all test conditions compared to testing in the prior AM alloy of the same class. A super-solvus heat treatment improved creep rupture strength by ∼3× in the LBPBF material compared to the near-solvus heat treatment. These findings provide directions for future studies to advance the overall state of gas turbine technology by enabling ABD®900-AM material and other AM alloys to be used in more innovative hot-section components.

Application of composite material models to determine the Young’s modulus of the grid pattern of partially infilled 3D printed samples

Abstract Additive manufacturing makes it possible to fabricate samples with partial internal infill. This type of sample has a different Young’s modulus than a completely filled sample. In this work, the dependence of the apparent Young’s modulus of samples manufactured by 3D printing on the infill percentage has been experimentally determined, for a given pattern and using a non-destructive technique. Young’s modulus was assumed as an apparent modulus and values were found between 3.39 GPa for the sample with 100% infill and 1.32 GPa for the sample with 20% infill. In particular, a non-linear variation of the apparent Young’s modulus was observed. The specific Young’s modulus presents a minimum for an intermediate infill percentage. The use of a model of composite materials was proposed, as a first approach to determine the apparent Young’s modulus of the parts. The mixture law, the Halpin-Tsai equation generalized by Kerner, a model of foams and the Mori Tanaka method were applied to the dependence of the apparent Young’s modulus on the infill percentage, giving all, except the mixture law, acceptable results. The advantage of applying each model was discussed. This type of analysis would allow a fast semi-empirical approach of the apparent Young’s modulus in partially filled samples with a grid pattern.

3D biomimetic scaffold’s dimensional accuracy: a crucial geometrical response for bone tissue engineering

Abstract Additive manufacturing has emerged as a trending methodology for producing different simple to complex geometries in minimum lead time, which in turn gives better quality attributes when compared to conventional manufacturing procedures. Fabrication of polylactic acid-based porous scaffold prototypes by 3-dimensional printing has been extensively performed successfully by many researchers. The dimensional accuracy of the 3-dimensional printed part is a very crucial aspect of bone tissue engineering. Dimensional precision of 3-dimensional biomimetic scaffolds has been a response characteristic somehow less focused on by researchers, though it is essential as it acts as a stereotype for defect recuperation while consequently developing extracellular matrix and bone regeneration. The present paper fosters re-tuning the process parameters of a fused deposition modeling based 3-dimensional printer while considering the dimensional precision as a response parameter by the Taguchi optimization technique using a full factorial design L27 orthogonal array set of design of experiments. The crystallinity of the polylactic acid filament material was assessed using differential scanning calorimetry and X-ray diffraction. The thermal breakdown of filament material was investigated utilizing a thermogravimetric analyzer. According to Taguchi’s signal-to-noise ratios, the optimum values were 0.14 mm of layer thickness, 20 mm s−1 of printing speed, and 80 % of infill percentage. In order to justify the results, response surface methodology was employed. R-square values for Taguchi and the response surface models were 88.61 % and 68.71 %, respectively.

Creation and Assessment of a Novel Design Evaluation Tool for Additive Manufacturing

Abstract Additive manufacturing (AM) is a rapidly growing technology within the industry and education sectors. Despite this, there lacks a comprehensive tool to guide AM novices in evaluating the suitability of a given design for fabrication by the range of AM processes. Existing design for additive manufacturing (DfAM) evaluation tools tend to focus on only certain key process-dependent DfAM considerations. By contrast, the purpose of this research is to propose a tool that guides a user to comprehensively evaluate their chosen design and educates the user on an appropriate DfAM strategy. The tool incorporates both opportunistic and restrictive elements, integrates the seven major AM processes, outputs an evaluative score, and recommends processes and improvements for the input design. This paper presents a thorough framework for this evaluation tool and details the inclusion of features such as dual-DfAM consideration, process recommendations, and a weighting system for restrictive DfAM. The result is a detailed recommendation output that helps users to determine not only “Can you print your design?” but also “Should you print your design?” by combining several key research studies to build a comprehensive user design tool. This research also demonstrates the potential of the framework through a series of user-based studies, in which the opportunistic side of the tool was found to have significantly improved novice designers’ ability to evaluate designs. The preliminary framework presented in this paper establishes a foundation for future studies to refine the tool’s accuracy using more data and expert analysis.

Simulation of Porosity Regrowth during Heat Treatment after Hot Isostatic Pressing in Titanium Components

Abstract Additive manufacturing (AM) is driven by design freedom, having fewer process constraints than traditional manufacturing processes. It requires careful process control and qualified parameters to create dense metal parts. However, defects in the form of cavities can be detected in as-built specimens by computed tomography. Post-processing techniques such as hot isostatic pressing (HIP) are applied to eliminate porosity, but regrowth of argon gas pores is observed after additional heat treatment. In this work, a mesoscopic heat treatment simulation of an argon-filled gas pore in titanium components is presented. A combination of HIP and high-temperature heat treatment for β-annealing is simulated. Calculated pore regrowth is qualitatively consistent with experimental observation from the literature. Simulation results support the hypothesis of argon not dissolving in the titanium matrix by assuming a constant amount of argon particles in the pore. Mesoscopic heat treatment simulations may be a part of a simulation-driven optimization of thermal post-processing to improve the quality and performance of AM components.

Heat Transfer in an Injector-Scaled Additively Manufactured Fuel Passage

Abstract Knowledge of heat transfer in fuel wetted passages is important for informing injector design and life estimates due to the effects of temperature on fuel degradation. Future injectors will be manufactured using additive methods in an effort to reduce production costs and time, while also facilitating more agile design practices. Additive manufacturing (AM) is known to result in increased surface roughness compared to conventional manufacturing techniques, however limited data exist on how this roughness impacts heat transfer, particularly in liquid flows. This paper solves the inverse heat conduction problem for heat transfer coefficient in liquid flows through rough 90 deg channel bends typical of the pilot gallery in a lean direct-injection fuel spray nozzle. Heat transfer distributions across two rough surfaces are compared to an equivalent smooth surface. The two rough surfaces have different morphologies but have the same relative effective sand grain roughness which is matched to a prototype AM fuel injector. The sand grain roughness is predicted from a correlation that has been adapted for the high relative roughness scales characteristic of additively manufactured fuel passages. The effective sand grain roughness estimated from surface measurements of a prototype AM fuel gallery was ∼13% of the passage hydraulic diameter. For the two rough surfaces, the heat transfer enhancement is up to three times the smooth surface value for the straight section preceding the bend and up to four times around the bend. Heat transfer distributions across the two rough surfaces are similar, but the magnitudes differ by ∼17% depending on the surface morphology. This highlights the importance of the heat transfer effectiveness of surface features, which unlike the sand grain roughness is not matched for the two surfaces considered. Adjusting the data for differences in heat transfer effectiveness corrects the average heat transfer for the rough surfaces to within 7%.

AUDIT: Functional Qualification in Additive Manufacturing via Physical and Digital Twins

Abstract Additive manufacturing (AM) has revolutionized the way we design, prototype, and produce complex parts with unprecedented geometries. However, the lack of understanding of the functional properties of 3D printed parts has hindered their adoption in critical applications where reliability and durability are paramount. This paper proposes a novel approach to the functional qualification of 3D-printed parts via physical and digital twins. Physical twins are parts that are printed under the same process conditions as the functional parts and undergo a wide range of (destructive) tests to determine their mechanical, thermal, and chemical properties. Digital twins are virtual replicas of the physical twins that are generated using finite element analysis (FEA) simulations based on the 3D shape of the part of interest. We propose a novel approach to transfer learning, specifically designed for the fusion of diverse, unstructured 3D shape data and process inputs from multiple sources. The proposed approach has demonstrated remarkable results in predicting the functional properties of 3D-printed lattice structures. From an engineering standpoint, this paper introduces a comprehensive and innovative methodology for the functional qualification of 3D-printed parts. By combining the strengths of physical and digital twins with transfer learning, our approach opens up possibilities for the widespread adoption of 3D printing in safety-critical applications. Methodologically, this work presents a significant advancement in transfer learning techniques, specifically addressing the challenges of multi-source (e.g., digital and physical twins) and multi-input (e.g., 3D shapes and process variables) transfer learning.