Adoption Of Additive Manufacturing Research Articles

Detecting Multi-Scale Defects in Material Extrusion Additive Manufacturing of Fiber-Reinforced Thermoplastic Composites: A Review of Challenges and Advanced Non-Destructive Testing Techniques

Additive manufacturing (AM) defects present significant challenges in fiber-reinforced thermoplastic composites (FRTPCs), directly impacting both their structural and non-structural performance. In structures produced through material extrusion-based AM, specifically fused filament fabrication (FFF), the layer-by-layer deposition can introduce defects such as porosity (up to 10–15% in some cases), delamination, voids, fiber misalignment, and incomplete fusion between layers. These defects compromise mechanical properties, leading to reduction of up to 30% in tensile strength and, in some cases, up to 20% in fatigue life, severely diminishing the composite’s overall performance and structural integrity. Conventional non-destructive testing (NDT) techniques often struggle to detect such multi-scale defects efficiently, especially when resolution, penetration depth, or material heterogeneity pose challenges. This review critically examines manufacturing defects in FRTPCs, classifying FFF-induced defects based on morphology, location, and size. Advanced NDT techniques, such as micro-computed tomography (micro-CT), which is capable of detecting voids smaller than 10 µm, and structural health monitoring (SHM) systems integrated with self-sensing fibers, are discussed. The role of machine-learning (ML) algorithms in enhancing the sensitivity and reliability of NDT methods is also highlighted, showing that ML integration can improve defect detection by up to 25–30% compared to traditional NDT techniques. Finally, the potential of self-reporting FRTPCs, equipped with continuous fibers for real-time defect detection and in situ SHM, is investigated. By integrating ML-enhanced NDT with self-reporting FRTPCs, the accuracy and efficiency of defect detection can be significantly improved, fostering broader adoption of AM in aerospace applications by enabling the production of more reliable, defect-minimized FRTPC components.

Development of a fuzzy analytic hierarchy process model, sensitivity analysis, and addressing barriers to additive manufacturing implementation in small- and medium-sized enterprises (SMEs)

Additive manufacturing (AM) is gaining popularity worldwide due to its potential to revolutionize manufacturing processes, improve product customization, and reduce waste. Nevertheless, small- and medium-sized enterprises (SMEs) encounter major challenges when implementing AM technologies. The objective of this research is to utilize the fuzzy analytic hierarchy process (FAHP) to identify and prioritize the critical challenges that impede the implementation of AM for SMEs. The study employs a two-stage methodology. First, a thorough literature review and expert discussions identify 15 major barriers to AM adoption. Second, a systematic model is proposed to establish a ranking of these barriers, which is based on the FAHP. The results are validated using sensitivity analysis to ensure the robustness of the findings. Additionally, potential solutions to overcome these barriers are discussed. The research indicates that the two most significant barriers for SMEs are the high costs of equipment and the scarcity of skilled designers. The model that has been presented is a valuable resource for SME managers and practitioners, as it allows them to make strategic and informed decisions that facilitate the efficient implementation of AM. This research is groundbreaking in its comprehensive analysis of the prioritization and ranking of AM implementation barriers, with a particular emphasis on SMEs. It offers essential insights for future research and practical applications.

Machine learning predictions of spatter behavior in LPBF additive manufacturing

The adoption of additive manufacturing (AM) technologies, particularly Laser Powder Bed Fusion (LPBF), has been rapidly increasing in industries requiring high precision and complex geometries. Despite its advantages, LPBF faces challenges related to defects that affect material quality, with spatter formation being a significant concern. Spatters—tiny particles ejected during the printing process—can adversely affect the final product’s integrity by altering surface roughness and contributing to defects. This study introduces a comprehensive approach to predict the ejection velocity and direction of spatter particles using a suite of machine learning (ML) algorithms, including Random Forest, Gaussian Process Regression, Support Vector Machine, Regularized Linear Regressions, Gradient Boosting Trees, and Neural Networks. Our analysis reveals that the Neural Network model outperforms others, achieving prediction accuracies of 97.58% for spatter velocity and 88.22% for ejection direction, thus offering a substantial improvement in understanding and controlling spatter-related defects in LPBF processes. The practical implications of these predictions are profound, enabling manufacturers to adjust AM parameters in real time to minimize defects and enhance product quality. This study not only fills a gap in the current literature by providing a detailed comparative analysis of multiple ML algorithms for spatter ejection prediction but also paves the way for future research into real-time monitoring and control systems in AM.

An iterative price-based combinatorial double auction for additive manufacturing markets

The increasing adoption of additive manufacturing (AM) in the industrial sector is leading to an imbalance between supply and demand of additively manufactured subcomponents: companies demanding AM services require very specific products and AM suppliers differ widely in their capabilities. Some existing proposals aim to help match supply and demand by merely making customer–supplier allocations. Only a few recent works go beyond allocation issues and propose market mechanisms to also address pricing aspects. However, we observe that these mechanisms do not fully exploit the potential of additive manufacturing techniques. The aim of this paper is to design a market mechanism that considers the particularity of AM techniques, wherein suppliers can benefit from manufacturing multiple heterogeneous parts from multiple customers in the same build area to increase production throughput. This market mechanism has been implemented as an iterative combinatorial double auction that adapts to this feature of the AM market: customers will bid to get their orders produced and suppliers will submit asking quotes to win the production of combinations of those orders. The mechanism solves the allocation and pricing of AM orders while seeking the maximization of social welfare. The procedure is simulated in a theoretical environment to evaluate its performance and to identify the most appropriate conditions for its implementation in a real environment. Unlike other existing proposals for client-supplier allocation mechanisms in additive manufacturing, the proposed mechanism allows a single supplier to produce a combination of orders from different clients, leading to a pricing system that maximizes social welfare without participants disclosing sensitive information.

Thermal and energy efficiency in 3D-printed buildings: Review of geometric design, materials and printing processes

Applying 3D printing in construction is a promising, sustainable alternative to conventional methods. However, the thermal and energy performance of 3D-printed buildings remains underexplored, particularly regarding the interactions between geometric design, material selection, and printing parameters. This review addresses this gap by critically examining how the main steps of the printing process impact the thermal efficiency of 3D-printed buildings. Key findings indicate that material development needs to focus on mixes that balance thermal properties,printability,and structural integrity. The review demonstrates how optimising wall cross-sections and cavity shapes can reduce thermal conductivity and enhance energy efficiency. Innovative geometric designs, such as cellular and honeycomb structures, have shown improvements in thermal insulation. Optimising printing parameters, such as extrusion rate and nozzle travel speed, is crucial to minimising thermal bridges and reducing material anisotropy. Advanced numerical models, validated by experimental data and incorporating factors like inhomogeneous porosity, anisotropy, and surface roughness, are essential for accurately predicting the thermal behaviour of 3D-printed structures. The review underscores the need for large-scale, long-term performance studies of 3D-printed buildings under diverse climatic conditions. Additionally, developing standardised fabrication protocols and testing methods is essential for ensuring consistent quality and broader adoption. Addressing these research gaps is crucial to fully realising the potential of 3D printing in sustainable construction and accelerating the adoption of additive manufacturing in the construction sector.

Metal additive manufacturing adoption in SMEs: Technical attributes, challenges, and opportunities

Recent advancements in Metal Additive Manufacturing (MAM) are transforming manufacturing. Most research and market adoption of MAM have focused on Powder Bed Fusion (PBF), with less attention given to Directed Energy Deposition (DED), Binder Jetting (BJ), and Metal Material Extrusion (MEX), which are only now reaching industrial readiness. This increased availability of MAM processes provides SMEs with a wider range of options, opening up new opportunities that were previously inaccessible. However, despite recent technological improvements that broaden potential applications, the suitability of these processes for industrial use by SMEs is not yet well understood. SMEs currently face difficulties in adopting MAM due to complexities and costs. Moreover, existing literature often overlooks the distinct characteristics and needs of SMEs, making it challenging for them to identify the most suitable MAM processes. This study addresses this gap by using a fuzzy logic approach to evaluate the technical characteristics of PBF, DED, BJ, and MEX, focusing on their compatibility with SME requirements. Each process is ranked based on criteria including costs, complexity, energy consumption, mechanical quality, geometrical quality, speed, and market demand. This evaluation is refined through logarithmic normalization and scaling, resulting in a comprehensive scoring system from 1 to 5. Based on these findings, an SME-focused evaluation matrix is proposed to guide SMEs in selecting the most appropriate MAM process for their specific contexts. This matrix promotes informed and effective adoption strategies, supported by practical examples illustrating the application of each MAM process in SME environments.

Design, calibration and performance evaluation of a small-scale 3D printer for accelerating research in additive manufacturing in construction

3D printing in construction presents numerous advantages, such as geometric flexibility, potential cost and time savings, the incorporation of recycled and sustainable materials, and reduced waste, thereby reducing the construction sector's environmental impact. Despite these advantages, the widespread adoption of AM in construction faces hurdles, primarily due to the prohibitive costs of large-scale concrete printers — typically ranging from $180,000 to over $1 million — and technological constraints that impede research and development efforts within the construction sector. To address these challenges, our study focuses on designing, developing, calibrating and evaluating an affordable lab-scale 3D printer specifically tailored for cement-based materials, aiming to lower the entry barrier for AM research in construction. This paper presents a proof-of-concept for a simple, yet functional printing technology that meet the requirements for research studies. The study details the development process, from the conceptual design to the calibration of printing parameters. The development process included the assessment of preliminary extrusion system designs integrated with the motion systems of a fused deposition modeling 3D printer. Subsequently, material studies were carried out to determine optimal material mix compositions and ratios. A comprehensive calibration of printing parameters using statistical analysis was proposed to ensure consistent and quality printing. The printability and applicability of the proposed small-scale 3D printer were assessed by printing samples and testing their thermal properties. Cost analysis showed that the proposed 3D printer, costing $273, offers benefits compared to existing market alternatives. The study illustrates the potential of small-scale 3D printers to facilitate construction research and practices, thereby promoting the development of sustainable construction methods.

Justification Framework for Adoption of Additive Manufacturing to Automotive Supply Chain: AHP Approach

Additive Manufacturing holds significant potential in influencing automotive sector and its supply chain however its effectiveness depends on variety of factors. This study aims to propose an analytic hierarchy process model to justify AM implementation to automotive sector for enhanced sustainability and resilience to its supply chain. This paper outlines benefits of AM over traditional manufacturing for automotive supply chain which leads to development of more sustainable and resilient supply chain. The AHP is used in this study to justify the advantages of the AM over Traditional Manufacturing for automotive supply chain. Major benefits of AM adoption have been identified through recent review of the literature on this topic and expert perspectives. AHP is used to calculate and compare the global desirability index of AMSC and TSC. Comparing AM-based supply chains to traditional manufacturing, it is found that the former have a higher global desirability index.

Predicting 3D printed plastic part properties: A deep learning approach with thermographic and vibration data fusion

Additive Manufacturing (AM) holds transformative potential for the manufacturing industry, yet its widespread adoption is hindered by inconsistent product properties. This study addresses this challenge by pioneering a novel predictive method to assess the impact of in-process sensing on part property prediction in Fused Deposition Modeling (FDM), serving as a representative case study. Focused on five critical mechanical properties, including surface roughness, tensile strength, elongation at break, micro-hardness, and warpage, we systematically explore various sensor combinations by integrating Inertial Measurement Unit (IMU) sensors and a thermal camera with machine setting parameters. Utilizing deep learning models, specifically hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) architectures, we encode time-series sensor data into signal images and evaluate performance across different sensor configurations. Our best-performing model achieves an impressive 99% correlation in predicting tensile strength, albeit with less robust performance in predicting warpage and micro-hardness, suggesting additional influencing factors. Furthermore, the employed signal imaging technique outperforms a handcrafted feature selection alternative. These findings carry significant implications for advancing the broader adoption of AM in critical applications, addressing challenges associated with inconsistent product properties and unlocking its full potential.

Additive manufacturing in the medical sector: from an empirical investigation of challenges and opportunities toward the design of an ecosystem model

PurposeThis works provides a thorough understanding of the challenges and opportunities associated with Additive Manufacturing (AM) adoption in the medical sector. Through this analysis, we aim to better understand when to adopt AM, how to do so, and how such adoption might change in the future.Design/methodology/approachThis research first conducted a systematic literature review (SLR) to identify AM challenges and opportunities in the medical sector, which were then validated through a Delphi study. The 18 Delphi study participants were also asked to suggest countermeasures for the challenges and help identify future AM adoption scenarios. Finally, these findings were analyzed according to the ecosystem pie model to design an ecosystem model for AM in the medical sector.FindingsAmong the 13 challenges and 13 opportunities identified, the lack of a skilled workforce and the responsiveness achievable via AM were by far the most relevant challenge and opportunity. Moreover, the participants identified countermeasures for 10 challenges, as well as three future AM adoption scenarios. Finally, leveraging these findings, an ecosystem model was developed.Originality/valueThis work contributes to the limited understanding of the AM challenges and opportunities in the medical sector. It helps medical practitioners to better understand the challenges and opportunities associated with AM and AM manufacturers to better identify where to focus their R&D efforts and how this would impact future AM adoption levels. Furthermore, this work extends current theory supporting the design of an ecosystem model for AM in the medical sector following the ecosystem pie model.

Towards a practice-based framework for supply chain resilience in the context of additive manufacturing technology adoption

ABSTRACT Additive manufacturing (AM), also known as 3D printing, is widely believed to enhance supply chain resilience (SCR). However, there is a lack of empirical frameworks to provide directions for practitioners and scholars in this regard. Motivated by the dynamic capabilities view, this exploratory survey research aims to overcome this gap. To this end, empirical data are collected from a heterogeneous sample of experts involved in different industries at the forefront of AM. These data are used to explore pathways through which AM adoption leads to enhancing SCR via different resilience practices. More specifically, the collected data are analyzed to explain how AM adoption affects different resilience practices and how these practices in turn affect SCR. Based on these findings, a preliminary practice-based framework is developed that can support practitioners in deploying AM-enabled resilience practices aimed at generating the supply chain (SC) capabilities necessary for dealing with SC vulnerabilities and therefore enhancing SCR. Moreover, relevant propositions are put forward that reflect these findings and open up avenues for future research.

A knowledge-driven, integrated design support tool for additive manufacturing

AbstractIncreasing adoption of additive manufacturing (AM) makes software support for design for additive manufacturing (DfAM) more relevant. This paper presents a novel, knowledge-driven design support tool for AM that leverages a central knowledge base to provide extensible and powerful DfAM support early in the development process. The approach was implemented using Python for the knowledge base and as a plugin for Siemens NX. It offers automated design checks, optimizations, and further information through an integrated Wiki. Evaluation confirms the feasibility and benefits of the approach.

A Methodology for the Rapid Qualification of Additively Manufactured Materials Based on Pore Defect Structures

Additive manufacturing (AM) can create net or near-net-shaped components while simultaneously building the material microstructure, therefore closely coupling forming the material and shaping the part in contrast to traditional manufacturing with distinction between the two processes. While there are well-heralded benefits to AM, the widespread adoption of AM in fatigue-limited applications is hindered by defects such as porosity resulting from off-nominal process conditions. The vast number of AM process parameters and conditions make it challenging to capture variability in porosity that drives fatigue design allowables during qualification. Furthermore, geometric features such as overhangs and thin walls influence local heat conductivity and thereby impact local defects and microstructure. Consequently, qualifying AM material within parts in terms of material properties is not always a straightforward task. This article presents an approach for rapid qualification of AM fatigue-limited parts and includes three main aspects: (1) seeding pore defects of specific size, distribution, and morphology into AM specimens, (2) combining non-destructive and destructive techniques for material characterization and mechanical fatigue testing, and (3) conducting microstructure-based simulations of fatigue behavior resulting from specific pore defect and microstructure combinations. The proposed approach enables simulated data to be generated to validate and/or augment experimental fatigue data sets with the intent to reduce the number of tests needed and promote a more rapid route to AM material qualification. Additionally, this work suggests a closer coupling between material qualification and part certification for determining material properties at distinct regions within an AM part.

Oral and maxillofacial surgeons’ views on the adoption of additive manufacturing: findings from a nationwide survey

ObjectivesHospitals in many European countries have implemented Additive Manufacturing (AM) technology for multiple Oral and Maxillofacial Surgery (OMFS) applications. Although the technology is widely implemented, surgeons also play a crucial role in whether a hospital will adopt the technology for surgical procedures. The study has two objectives: (1) to investigate how hospital type (university or non-university hospital) influences surgeons' views on AM, and (2) to explore how previous experience with AM (AM experience or not) influences surgeons' views on AM.Materials and methodsAn online questionnaire to capture surgeons’ views was designed, consisting of 11 Likert scale questions formulated according to the Consolidated Framework for Implementation Research (CFIR). The questionnaire was sent to OMF surgeons through the channel provided by the Association of Oral and Maxillofacial Surgery in Sweden. Data were analyzed using the Mann–Whitney U test to identify significant differences among OMF surgeons in terms of organizational form (i.e., university hospital or non-university hospital) and experience of AM (i.e., AM experience or no-experience).ResultsIn total, 31 OMF surgeons responded to the survey. Views of surgeons from universities and non-universities, as well as between surgeons with experience and no-experience, did not show significant differences in the 11 questions captured across five CFIR domains. However, the “individual characteristics” domain in CFIR, consisting of three questions, did show significant differences between surgeons’ experience with AM and no-experience (P-values: P = 0.01, P = 0.01, and P = 0.04).ConclusionsSurgeons, whether affiliated with university hospitals or non-university hospitals and regardless of their prior experience with AM, generally exhibit a favorable attitude towards AM. However, there were significant differences in terms of individual characteristics between those who had prior experience with AM and those who did not.Clinical relevanceThis investigation facilitates the implementation of AM in OMFS by reporting on the views of OMF surgeons on AM.

Understanding the Adoption of Additive Manufacturing in Construction: A Sociological Perspective through a Revised TAM Model

Understanding the Adoption of Additive Manufacturing in Construction: A Sociological Perspective through a Revised TAM Model

Framework for Adoption of Metal Additive Manufacturing in the Oil and Gas Industry in Africa

The oil and gas industry in Africa is under mounting pressure to elevate operational efficiency and curb costs, even as supply chain challenges stemming from the pandemic persist. Additive manufacturing (AM) emerges as a unique solution with the potential to free segments of the industry from conventional supply chains. This study introduces a framework tailored to the distinctive challenges and opportunities of the African oil and gas sector, aiming to facilitate the effective adoption of metal AM. Considering constraints such as supply chain limitations, unstable electricity supply, remote locations, and limited manufacturing facilities, this framework intends to guide stakeholders in leveraging the transformative potential of AM technology while overcoming potential barriers. Leveraging a literature review, expert insights, and real-world scenarios, the study assesses the current state of metal AM in the oil and gas sector. The framework's applicability is demonstrated through a case study of the Nigerian National Petroleum Company (NNPC) Limited, while acknowledging the specific African context. It uncovers the existing state of metal AM in Africa and identifies various facilities along with their constraints. Encompassing domains such as prototyping, customized tooling, complex component fabrication, and spare part production, the framework addresses challenges like cultural shifts, quality control, and facility sustainability. It offers key stages encompassing technology assessment, workforce training, and strategic planning to drive the transition toward additive manufacturing. The adoption of metal AM in Africa's oil and gas sector holds the potential to significantly enhance operational efficiency and competitiveness, yet it necessitates overcoming multifaceted challenges. The proposed framework presents a strategic roadmap for harnessing this technology's transformative potential. By embracing innovation and surmounting region-specific obstacles, Africa's oil and gas industry can attain enhanced efficiency, cost-effectiveness, and adaptability

Unveiling the Environmental and Economic Implications of Additive Manufacturing on Inbound Transportation

This studyaims to investigate the impact of additive manufacturing (AM) on the sustainability of inbound transportation. By combining insights from existing literature and interviews with professionals in the additive manufacturing field, we present a comprehensive analysis of this subject. Drawing upon both literature review and interview perspectives, it is evident that AMsignificantly influences the sustainabledimension of transportation. A key attribute of additive manufacturing is its inherent efficiency, resulting in minimized waste generation during production. This stands in contrast to traditional manufacturing methods, which involve material reduction to achieve desired shapes. The streamlined process of additive manufacturing requires fewer raw materials, thereby reducing environmental impact. The transportability of filament powder in bulk form further contributes to these sustainability gains, reducing material volume and transportation frequency. Consequently, emissions associated with additive manufacturing-related transportation are lowered. Recycling of materials is also structured within a broader framework, deviating from the conventional model of product returns to manufacturers.Despite its potential, our empirical findings suggest that the adoption of additive manufacturing doesn't consistently lead to cost reduction compared to conventional methods. The applicability of additive manufacturing remains limited to specific product ranges. Nevertheless, cost savings can occur in the realm of inbound transportation due to factors such as reduced transportation frequency and the transport-friendly nature of raw material containers. Furthermore, the efficiency of raw material usage in additive manufacturing contributes to economic benefits. Unlike conventional manufacturing, additive processes don't require extensive raw material inventories.Economic considerations extend to the adoption of distributed manufacturing systems. While centralized systems minimize raw material transportation, they may increase transportation distance for the final products. Conversely, distributed systems increase raw material travel but decrease product transportation distance. The study underscores that while distributed manufacturing offers advantages, it impacts transportation costs.In essence, this study sheds light on the complex interplay between additive manufacturing and inbound transportation sustainability. It underscores how additive manufacturing's inherent efficiencies and material characteristics have ramifications for environmental and economic dimensions. Through a blend of theoretical insights and empirical evidence, this research contributes to the broader discourse on sustainable manufacturing practices.

Advances in Additive Manufacturing Techniques for Electrochemical Energy Storage

AbstractThe increasing adoption of additive manufacturing (AM), also known as 3D printing, is revolutionizing the production of wearable electronics and energy storage devices (ESD) such as batteries, supercapacitors, and fuel cells. This surge can be attributed to its outstanding process versatility, precise control over geometrical aspects, and potential to reduce costs and material waste. In this comprehensive review, major AM processes like inkjet printing, direct ink writing, fused deposition modeling, and selective laser sintering/melting along with possible configurations and architectures, are elaborately discussed for each bespoke ESD. The application of 3D‐printed energy storage devices in wearable electronics, Internet of Things (IoT)‐based devices, and electric vehicles are also mentioned in the review. The role of AM in facilitating the production of solid‐state batteries has also revolutionized the electric vehicle (EV) industry. Recent progress in the field of AM of energy systems with solid electrolytes and potential future directions such as 4D printing to incorporate stimuli‐responsive behavior in 3D‐printed materials, biomimetic design optimization, and additive manufacturing of ESD in a micro‐gravity environment have also been highlighted. Extensive research and continuous progress in this field are expected to enhance the longetivity, industrial scalability, and electrochemical performance of 3D‐printed energy storage devices in future.

Layout and geometry optimization design for 3D printing of self-supporting structures

As the demand for high-performance structures in various scenarios continues to rise, the complexity of engineering structures also increases, necessitating the development of advanced design methods and the additive manufacturing (AM) of sophisticated structures. While the layout optimization method based on the ground structure technique can produce optimized designs, the gravity-induced overhang effect during the printing process often requires additional support materials. The use of additional support materials during the printing process can result in higher material costs or the need for post-processing to remove the support structures, significantly hindering the adoption of AM in practice. This paper presents an optimization framework to obtain self-support optimization designs and provides a practical validation for the effectiveness of the proposed framework. Firstly, a self-support point-line structure is obtained via the layout and geometry optimization, considering overhang constraints. Secondly, the point-line structure is transformed into a physical model with nodal expansion considered (i.e., taking into account the overlapping of members at nodes). Finally, the physical models are sliced and printed using both a plastic Fused Deposition Modeling (FDM) printer and a metal Wire Arc Additive Manufacturing (WAAM) printer. The results confirm that the proposed process is effective for both plastic and metal printing, demonstrating its exceptional versatility.

Mechanical and Tribological Performance of Carbon Fiber-Reinforced PETG for FFF Applications

With the increasing adoption of Additive Manufacturing in the industry, driven by its efficiency, productivity, and project profitability, materials have undergone significant evolution to enhance process performance and part properties. One of the processes employed to enhance these properties involves the incorporation of various types of reinforcements. This aims to ensure that the material acquires a proportion of the properties of the added reinforcement. Consequently, the options for material selection expand depending on the application. Hence, there is a need to understand how specific reinforcements modify the properties of these materials. For this reason, this study investigates the modification of mechanical properties in a PETG matrix through the incorporation of short carbon fiber (CF) reinforcements, driven by their industrial relevance. To achieve this, the Fused Filament Fabrication (FFF) process will be utilized to produce a series of standardized specimens made of both PETG and CF-reinforced PETG, with variations in layer height and extrusion temperature. Subsequently, these specimens will undergo mechanical evaluation in tension and compression, following the relevant standards for each case. Finally, distinctions between both materials will be analyzed, based on the data obtained from tensile and compression tests. The incorporation of carbon fiber reinforcement shows a detrimental effect, leading to a decrease in the material’s stress (39.23 N/mm2 vs. 48.41 N/mm2 for the conventional material). As expected, due to the nature of the reinforcement (short fibers), the deformation of the material also decreases (2.13% compared to 2.9%).