Additive Manufacturing Machine Research Articles

Design complexity based flexible order dispatching for additive manufacturing production

Understanding design complexity for additive manufacturing (AM) is essential in AM production planning since conventional make-to-order production for individual AM orders of complex designs can amplify operational uncertainty in an entire AM production system. As a response, this study aims not only to demonstrate the impact of design complexity on AM production but also to propose a novel order dispatching approach based on design complexity that mitigates operational uncertainty in an AM production system. First, a design complexity measure was developed using an information theoretic approach. Next, a discrete-event simulation model to represent an AM production system consisting of parallel AM machines for jet-engine bracket designs was built to identify the impact of design complexity on average order lead time and total production cost through regressions. Finally, a flexible order dispatching rule that reflects operational attitudes toward design complexity was proposed to determine part-processing priorities by tracking both part- and system-level design complexity states in a centralized queue for AM production. The proposed dispatching rule was compared with relevant static dispatching rules to assess its performance in operational efficiency under varied attitudes toward design complexity. The findings from this study clearly showed the negative impact of design complexity on operational performance for AM production. Moreover, the proposed dispatching rule resulted in lead time reduction and balanced lead time performance in AM production against alternative static dispatching strategies. This study demonstrates the importance of design complexity-based flexible operations to properly handle latent uncertainties in an AM production system.

Influence of reactive diluent composition on properties and bio-based content of itaconic acid-based additive manufacturing materials

Among the additive manufacturing techniques, UV-curing processes are of special interest, as they allow for the fabrication of thermosetting materials with high resolutions and optical clarity. Traditionally, these processes require the use of formulations, consisting of polyester or polyurethane oligomers, combined with photoinitiators and reactive diluents. The latter are usually vinyl monomers such as acrylates or acrylamides, used to reduce the viscosity of the formulation and render it processable by additive manufacturing machines. In this work, we investigate whether a combination of diluents can be used to tune the thermomechanical properties of the printed materials, and if it can be exploited to increase their overall bio-based content without compromising their performance. To do so, a series of itaconic acid-based polyesters with different chemical structures (aliphatic–aromatic) were synthesized and formulated with reactive diluents acryloyl morpholine (ACMO) and isobornyl acrylate (IBOA). The physicochemical properties of the prepared formulations, together with their reactivity towards UV-light were assessed via photo differential scanning calorimetry (photo-DSC) and photo-rheology measurements. The same formulations were then used to fabricate test specimen via digital light processing (DLP) 3D printing, which were characterized on their thermomechanical properties by means of dynamic mechanical analysis (DMA) and thermogravimetric analysis (TGA) measurements. The glass transition temperature of the printed samples reached a maximum of 98 °C, while the diluent ratio could be used to manipulate the Tg in a linear trend. All materials of this study exhibited Td,5% above 250 °C, suggesting good thermal stability. These results show that materials with a very high bio-based content (up to 85%) and very promising thermomechanical properties could be obtained by employing a diluent mixture during the formulation phase.

Design and implementation of a machine log for PBF-LB/M on basis of IoT communication architectures and an ETL pipeline

Powder Bed Fusion with Laser Beam of Metals (PBF-LB/M) has gained more industrial relevance and already demonstrated applications at a small series scale. However, its widespread adoption in various use cases faces challenges due to the absence of interfaces to established Manufacturing Execution Systems (MES) that support customers in the predominantly data-driven quality assurance. Current state-of-the-art PBF-LB/M machines utilize communication architectures, such as OPC Unified Architecture (OPC UA), Message Queuing Telemetry Transport (MQTT) and Representational State Transfer Application Programming Interface (REST API). In the context of the Reference Architecture Model Industry 4.0 (RAMI 4.0) and the Internet of Things (IoT), the assets, particularly the physical PBF-LB/M machines, already have an integration layer implemented to communicate data such as process states or sensor values. Missing is an MES component acting as a communication and information layer. To address this gap, the proposed Extract Transform Load (ETL) pipeline aims to extract relevant data from the fabrication of each build cycle down to the level of scan vectors and additionally to register process signals. The suggested data schema for archiving each build cycle adheres to all terms defined by ISO/TC 261—Additive Manufacturing (AM). In relation to the measurement frequency, all data are reorganized into entities, such as the AM machine, build cycle, part, layer, and scan vector. These scan vectors are stored in a runtime-independent format, including all metadata, to be valid and traceable. The resulting machine log represents a comprehensive documentation of each build cycle, enabling data-driven quality assurance at process level.

Design and Fabrication of an Acetylene Fuel Vehicle

Abstract: The transition towards sustainable transportation necessitates exploring alternative fuels with reduced environmental impacts. Acetylene has emerged as a promising candidate due to its clean combustion characteristics and high energy density. This abstract outline the design and fabrication process of an acetylene fuel vehicle, focusing on its feasibility and performance. The design phase involves meticulous material selection to ensure compatibility with acetylene's properties while prioritizing safety measures. Key components such as the fuel storage system, combustion chamber, and propulsion system are engineered to maximize energy conversion efficiency and minimize emissions. Safety protocols for acetylene handling and storage are integrated into the design to mitigate risks associated with its high reactivity. Fabrication incorporates advanced manufacturing techniques like additive manufacturing and precision machining to create a robust vehicle prototype. The integration of selected components is optimized to enhance structural integrity and overall performance. Rigorous testing procedures are conducted to validate the vehicle's functionality, including performance metrics, emissions analysis, and safety evaluations. The outcomes of this study provide valuable insights into the feasibility of acetylene as a sustainable transportation fuel. The design and fabrication processes contribute to ongoing research efforts aimed at reducing the carbon footprint of the transportation sector. The results also offer a pathway for future developments in alternative fuel technologies, driving towards a more environmentally friendly and energy-efficient transportation ecosystem.

ADDITIVE MANUFACTURING OF FLUOROELASTOMER LATEX: A DIRECT-INK-WRITING APPROACH

ABSTRACT Fluoroelastomers (FKM) are beneficial materials with desirable properties such as resistance to chemical environments, aging, fluid, and high temperatures. However, FKM processing is restricted to the use of conventional tooling equipment to produce goods, in which energy-intensive procedures including mixing, shaping, and vulcanization are a part of manufacturing. Because fossil fuels are used as the main energy source, these processes are carbon intensive. Our strategy is predicated on using additive manufacturing (AM) as a tool-less manufacturing process exhibiting customizability, flexibility, and sustainability. AM machines consume energy only while building objects in contrast to energy-intensive machinery. Thus, our plan of action is to combine AM with a newly developed FKM ink that can be 3D printed with a direct-ink-write (DIW) process. The rheological characteristics, printing parameters, and mechanical properties of the formulated ink are investigated. We believe that this adaptable method will make it easier to produce 3D-printed FKM components with DIW, which could have a wide range of applications in engineering and consumer goods.

Deformation and retentive forces variations of the additively manufactured cobalt-chromium and titanium alloys dental clasps

This in vitro study aimed to evaluate the additive manufacturing (AM) of cobalt chromium Co-Cr and titanium Ti alloy clasps for clinical use. After scanning the Ni-Cr die of the first molar, Akers' clasps were designed using computer-aided design/ computer-aided manufacturing (CAD/CAM). The clasps were manufactured from Co-Cr-W dental alloy and Ti-6Al-4V alloy powder using AM machines. Then, they were divided into two groups. The initial retentive force of the clasps was measured using a universal testing machine. Cyclic loading of the clasps was carried out by a specially designed insertion-removal testing apparatus in wet condition up to 5000 cycles. Retentive force was measured at 1000, 2000, 3000, 4000, and 5000 cycles. Moreover, the intaglio surface of each clasp was scanned using the scanner; and superimposition between the pre- and post-cycling clasp files was performed to evaluate deformation after cyclic loading. The fitting surfaces of retentive clasp tips were examined with a scanning electron microscope (SEM). Finally, it has been found that the initial retentive force for the Co-Cr group was 10.81 ± 0.37 N, and for the Ti group was 5.41 ± 0.18 N. Additionally, during the testing periods, both Co-Cr and Ti clasps continued to lose retentive force within the cycles of placement and removal. This effect was more prominent in the Co-Cr than in the Ti clasps. The distances between pre- and post-cycling in the retentive arm were −0.290 ± 0.11 mm and −0.004 ± 0.01 mm in Co-Cr and Ti alloys, respectively, and in the reciprocal arm were −0.072 ± 0.04 mm and −0.032 ± 0.04 mm in Co-Cr and Ti alloys, respectively. The retentive force required to remove the Ti clasps was found to be significantly lower than those required to dislodge the Co-Cr clasps. Co-Cr and Ti clasps lost significant amounts of retentive force from the initial use to the 3.5-year periods of simulated clinical use.

Iterated variable neighborhood search for integrated scheduling of additive manufacturing and multi-trip vehicle routing problem

This study addresses a novel problem structure for integrated scheduling of additive manufacturing and delivery processes. In the manufacturing process, we consider batch production of parts, simulating Selective Laser Melting technology in parallel additive manufacturing machines with heterogeneous specifications. A batch production, referred to as a build, must consist of parts with homogenous material. During the production run, sequence-dependent material setup times occur in between builds. In the delivery process, parts are delivered in batches using a limited number of homogenous capacitated vehicles. Each vehicle might perform multi-trips. Thus, the route for each trip and departure schedule among trips must be decided. In this regard, batch delivery is independent to batch production. The problem objective function is to minimize the total completion time of all parts. For optimally solving the problem, a mixed-integer linear programming model is devised. To solve larger problems sizes under a lower computation time, an iterated variable neighborhood search algorithm with embedded rule-based heuristics is proposed. The computational experiment denotes that the proposed algorithm with route minimization rule effectively solves small-scale problems, while capacity usage maximization rule performs the best in large-scale problems and outperforms other existing metaheuristics. Further result analysis is also performed to provide managerial insights.

Compressive and flexural mechanical responses of components obtained through mSLA vat photopolymerization technology

In recent years, Additive Manufacturing (AM) has become an increasingly popular method in industrial applications for fabricating components with complex geometries, offering several benefits over traditional (subtractive) manufacturing methods. Among all available AM technologies, “vat photopolymerization” is still a reliable approach for manufacturing high-resolution components at relatively small costs. This particular AM technology is based on the photopolymerization process where 3D objects are created by light-induced solidification and it has been broadly developed and used in the past decades. Depending on the employed light source there are three different subtypes of this technology, namely: SLA, DLP, and mSLA. Among all three listed, the Masked Stereolithography Apparatus (mSLA) technology has emerged as a promising approach due to the much simpler AM machine construction compared to the other two. However, the mechanical properties of mSLA components have not been studied extensively, hence there is a lack of knowledge of how AM process parameters and post-processing treatments affect the final mechanical properties of mSLA components. This work presents an experimental investigation of the compressive and flexural mechanical responses of components produced through this relatively new AM technology. A series of static and cyclic tests were conducted with varying layer thickness and post-curing times. It is demonstrated that a thorough optimization of the mentioned variables is required to obtain parts with the desired mechanical properties.

Advancements in Lightweight Materials for Aerospace Structures: A Comprehensive Review

The aerospace engineering field is witnessing an evolution towards lightweight materials driven by the unwavering pursuit of improved performance and efficiency. Lightweight materials play a critical role in aerospace structures, and this thorough examination explores their significant influence on mission success, design, and fuel efficiency. Key developments in several material categories such as composites, metals, and polymers are methodically examined in this study, revealing their special qualities and potential uses. The historical viewpoint lays out a chronology of the development of materials, charting significant turning points that have influenced the use of lightweight materials in aerospace. The following sections delve into each lightweight material in great detail, explaining composites, alloys (including titanium, aluminium, and high-strength steel), and sophisticated polymers, along with their features, production methods, and uses. An examination of particular varieties and production nuances in the context of aerospace composite materials highlights their benefits in achieving unmatched strength-to-weight ratios. By elucidating their qualities and expanding uses in aircraft constructions, advanced metallic alloy research reveals the most recent advancements in materials like titanium and aluminium. A thorough examination of polymeric materials and nanocomposites reveals their critical role in the creation of lightweight structures. Their distinctive qualities and new uses are emphasized, highlighting their role in the ongoing revolution in aeronautical design. With a comprehensive examination of additive manufacturing and sophisticated machining methods essential to the realization of lightweight aircraft structures, innovative manufacturing technologies take center stage. Additionally, the assessment thoroughly evaluates the longevity and structural integrity of lightweight materials, addressing issues related to corrosion, fatigue, and other crucial elements. Investigations from actual applications provide concrete instances of lightweight materials being successfully used in aerospace projects, shedding light on the materials' performance and tangible advantages in various situations. Looking ahead, the paper's conclusion forecasts forthcoming developments in lightweight materials and clarifies possible obstacles and future research avenues that may influence the course of aerospace engineering. To underscore the critical role that lightweight materials play in aeronautical constructions, this paper synthesizes a variety of knowledge. It highlights the revolutionary potential of lightweight materials in influencing the future of aeronautical engineering, not only synthesizing current knowledge but also laying out a path for further study and innovation.

Dimensional Characterization and Hybrid Manufacturing of Copper Parts Obtained by Atomic Diffusion Additive Manufacturing, and CNC Machining.

The combination of Atomic Diffusion Additive Manufacturing (ADAM) and traditional CNC machining allows manufacturers to leverage the advantages of both technologies in the production of functional metal parts. This study presents the methodological development of hybrid manufacturing for solid copper parts, initially produced using ADAM technology and subsequently machined using a 5-axis CNC system. The ADAM technology was dimensionally characterized by adapting and manufacturing the seven types of test artifacts standardized by ISO/ASTM 52902:2019. The results showed that slender geometries suffered warpage and detachment during sintering despite complying with the design guidelines. ADAM technology undersizes cylinders and oversizes circular holes and linear lengths. In terms of roughness, the lowest results were obtained for horizontal flat surfaces, while 15° inclined surfaces exhibited the highest roughness due to the stair-stepping effect. The dimensional deviation results for each type of geometry were used to determine the specific and global oversize factors necessary to compensate for major dimensional defects. This also involved generating appropriate over-thicknesses for subsequent CNC machining. The experimental validation of this process, conducted on a validation part, demonstrated final deviations lower than 0.5% with respect to the desired final part, affirming the feasibility of achieving copper parts with a high degree of dimensional accuracy through the hybridization of ADAM and CNC machining technologies.

Insights into the assessment of spreadability of stainless steel powders in additive manufacturing

This work investigates the link between the single particle and bulk flow properties of stainless steel (SS316L) powders and their spreading behaviour for additive manufacturing (AM) application. Two different batches of SS316L powders, noted to have significantly different spreading behaviour in AM machines, were investigated. The two batches had almost identical particle size, morphology and bulk flow properties as quantified by angle of repose, Hausner ratio, Carr index and basic flow energy; however one of the batches did not consistently flow during Hall and Carney flow tests and also had very poor spreadability when evaluated using an in-house spreading rig. The flow and spreading behaviours of the poorly spreading batch significantly improved and became almost identical to the good batch when tested following drying, either in a vacuum oven or a freeze dryer. Elemental analysis revealed that batches had some small but significant differences in chemical composition. This study revealed the complexity of defining a correlation between powder properties and spreading behaviour, and the importance of considering the environmental conditions.

Optimization of Motor Bracket for Enhanced Performance in Electric Vehicles - A Review

As electric vehicles (EVs) continue to gain prominence in the automotive industry, the demand for efficient and lightweight components becomes crucial for overall vehicle performance. This study focuses on the optimization of the bracket used for mounting the motor in electric vehicles, aiming to enhance structural integrity, reduce weight, and improve overall efficiency. The research employs a multi-faceted approach, combining advanced materials analysis, structural simulations, and manufacturing techniques to achieve an optimal design. Initially, a comprehensive materials selection process is conducted, considering factors such as strength, weight, and cost. Finite Element Analysis (FEA) is then employed to simulate the bracket's structural behavior under various loading conditions, ensuring that it meets stringent performance and safety standards. The optimization process involves fine-tuning the bracket's geometry to strike a balance between weight reduction and structural robustness, thereby improving the overall efficiency of the electric motor system. Additionally, advanced manufacturing methods, such as additive manufacturing and precision machining, are explored to facilitate the production of the optimized bracket. These techniques not only contribute to the reduction of material waste but also allow for complex geometries that further enhance the bracket's performance.

Design and techno economic optimization of an additively manufactured compact heat exchanger for high temperature and high pressure applications

Additive manufacturing (AM) has tremendous potential to produce high-power-density heat exchangers. However, manufacturing AM-heat exchangers at a competitive cost impedes their commercial adoption. In this paper, a novel design of an AM counter flow pin–fin compact recuperator, and its manufacturing costs when using laser powder bed fusion method, are presented. The compact recuperator, which can operate at a high temperature of 800 °C, a high fluid pressure (250 bar) on the cold side and withstand a differential pressure of 170 bars across the fluid streams, as seen in supercritical carbon dioxide power cycles, is designed for a lifetime of 40,000 h. Numerical models to predict the thermo-fluidic performance, and a simplified mechanical model based on plate theory to predict the mechanical stresses, of the recuperator are presented. The recuperator's thermal performance and compactness are studied, and its manufacturability is evaluated via a multi-objective techno-economic optimization. The optimization process considers thermo-fluidic, structural, and manufacturing constraints. The recuperator was fabricated using Haynes 282, and the printability of the design features using a laser-powder-bed-fusion machine is demonstrated. The choice of employing two different AM machines, with different build plate dimensions, number of lasers, and design constraints, on the recuperator's cost/unit, cost/UA, and cost/kW-th o are examined. It is observed that the cost/UA and cost/kW-th decrease exponentially from 13,839 USD-K/kW to 1,994 USD-K/kW and from 80 USD/kW-th to 18 USD/kW-th, respectively, with the size and thermal rating of the recuperator. The exponential decrease in cost/UA and cost/kW-th with increase in power rating opens the possibility of manufacturing large-scale additively manufactured recuperators at a competitive cost. The recuperator, including headers, can achieve a maximum volumetric heat density of 200 MW/m3. The optimization framework can be used to obtain optimal pin-array heat exchanger dimensions for other applications and materials using laser powder bed fusion method.

Ensuring additive manufacturing quality and cyber–physical security via side-channel measurements and transmissions

In order to securely monitor and validate additive manufacturing (AM) processes,the authors present a novel data transmission approach using a disconnected side-channel monitoring system. This approach is centered on the concept of embedding process-quality information into the print toolpath such that it may be detected by an in-situ side-channel monitoring system. This system can then parse these side-channel measurements to decode the embedded information, effectively transmitting process quality information to the air-gapped monitoring system. This enables in-situ comparison against the covertly transmitted specification for validation of the part build. This approach allows the monitoring system to remain disconnected from the network while still enabling the validation of parts never-before-seen by the system. In this paper, the concept of, and considerations for, transmitting part quality specification information over process side-channels is presented. An implementation, consisting of a disconnected side-channel monitoring system and communication protocol, is constructed for a material extrusion AM machine. Validation of the overall concept and AM process embodiment is provided through demonstration of successful detection of attacks on part geometry and process parameters.

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.

Mechanical properties of additively manufactured lattice structures designed by deep learning

Lattice structures, characterized by their repetitive lightweight cellular forms, enable more effective load distribution compared to solid bodies. Designing lattice structures with tailored mechanical properties remains challenging due to the numerous design variables and their complex relationship with mechanical performance. This paper presents a novel approach employing a deep learning-based Generative Adversarial Network (GAN) model to address this engineering challenge. With its potential for creativity and innovation, GAN provides design diversity that cannot be achieved with traditional design methods or other generative design models. Distinct from previous studies, the GAN training data set consists of lattice structures with improved mechanical properties obtained using parametric design and simulated annealing method. This data set enables the GAN model to create lattice structures with high strength-to-weight ratio. These lattice designs were fabricated using a commercial Material Jetting Additive Manufacturing (MJ-AM) machine, allowing for the production of complex structures. The mechanical performance of the 3D-printed unit cell samples was evaluated through Finite Element Analysis (FEA), compression, and impact testing. The results reveal that the lattice structures generated using the GAN model demonstrated improved mechanical strength (i.e. up to 108 % and 150 % improved strength and elongation performance, respectively). This study shows AI's potential to widen lattice structure design space and create tailored parts with improved mechanical properties. The research also paves the way for future exploration of deep learning techniques in revolutionizing the design and fabrication of parts with tailored mechanical properties.

Progress in Additive Manufacturing

The article analyses the current trends in the development of additive manufacturing (AM) technologies. In recent years, the development of additive technologies is one of the industry priorities. Additive technologies, first of all, make it possible to implement effectively any design and engineering ideas in high-tech industries, such as aircraft construction, engine and engine building, rocket engineering, modern electronic devices, etc. The expansion of the range of materials for additive technologies will facilitate their introduction into mass production. Meanwhile, the development of breakthrough scientific and technical solutions in the field of AT is impossible without new powder materials. Currently, there is an evident fundamental problem, namely, the lack of comprehensive scientific research aimed at developing new powder materials for additive technologies, adapting these materials to the requirements of modern additive manufacturing machines and studying the properties of products obtained by additive technology with various variations of technical parameters.

Powder bed defect classification methods: deep learning vs traditional machine learning

PurposeThis paper aims to discuss the development of a defect classification system that can be used to detect and classify powder bed surface defects from captured layer images without the need for specialised computational hardware. The idea is to develop this system by making use of more traditional machine learning (ML) models instead of using computationally intensive deep learning (DL) models.Design/methodology/approachThe approach that is used by this study is to use traditional image processing and classification techniques that can be applied to captured layer images to detect and classify defects without the need for DL algorithms.FindingsThe study proved that a defect classification algorithm could be developed by making use of traditional ML models with a high degree of accuracy and the images could be processed at higher speeds than typically reported in literature when making use of DL models.Originality/valueThis paper addresses a need that has been identified for a high-speed defect classification algorithm that can detect and classify defects without the need for specialised hardware that is typically used when making use of DL technologies. This is because when developing closed-loop feedback systems for these additive manufacturing machines, it is important to detect and classify defects without inducing additional delays to the control system.

Analytical models for flow time estimation of additive manufacturing machines

The use of Additive Manufacturing (AM) technology has largely increased in the last years. Because of its large differences from conventional technologies, the use of AM in production systems might call for new strategies in production planning and control. To this aim, this paper proposes analytical models to predict aggregate performance measures such as flow time, work in process, and production throughput, for production systems characterised by Laser Powder Bed Fusion AM technology. These indicators could be used both in operations strategy development and in technology comparison. The proposed models differentiate for their detail of the analysis and the number of input parameters that need to be estimated. The results show that the level of detail of the model affects the analysis leading to quite different values of the performance measures, especially in the case of highly saturated systems. Also, a discussion about the applicability of the proposed model to other AM technologies show whether and to what extent the proposed models can be applied for modelling other AM technologies.

Melt‐Versus Solution‐Extrusion Additive Manufacturing of Poly(methyl methacrylate) Medical Implant Prototypes

AbstractThe development of tailored additive manufacturing (AM) approaches is resulting in novel strategies for engineering the macro/microstructural details of potential medical devices made of biocompatible polymers. The aim of this work is the employment of a multifunctional AM machine for fabricating poly(methyl methacrylate) (PMMA) constructs with a predefined shape and porous architecture, by either melt‐ or solution‐extrusion AM. In particular, PMMA porous scaffolds are manufactured by employing fused filament fabrication (FFF) or computer‐aided wet‐spinning (CAWS). The comparative characterization of the two developed PMMA implant prototypes demonstrates significant differences in terms of porous structure, polymer surface topography, material composition, glass transition temperature, and mechanical properties. As a consequence, FFF‐printed PMMA samples support a faster in vitro proliferation of a murine fibroblast cell line in comparison to PMMA scaffolds by CAWS. The results achieved encourage further research on the developed processing protocols, aimed at the development of tailored PMMA implants for personalized surgery applications.