Current status of additive manufacturing for tissue engineering scaffold

Purpose The development of additive manufacturing (AM) techniques has significantly expanded the design space for engineering structures and facilitated the practical application of novel concepts, such as meta-materials with diverse microstructures. However, in load-bearing applications—particularly in aerospace and aeroengine fields—additively manufactured (AMed) metallic materials and structures still face limitations due to the presence of inherent defects. For meta-materials, the geometric accuracy of microstructural cells is also difficult to ensure using current metal AM technologies. These defects and geometric inaccuracies can markedly affect the mechanical properties of mechanical meta-materials. Consequently, a substantial body of research has focused on investigating the mechanical behavior and performance of AMed mechanical meta-materials. This paper presents a comprehensive review of recent advancements in metal AM technologies, with a particular focus on defect characterization methods and the evaluation of strength and fatigue properties in AMed mechanical meta-materials. Design/methodology/approach This paper provides a state-of-the-art review on the AM techniques for mechanical meta-materials, defects and defect characterization methods in AMed structures, and evaluation methods of strength and fatigue properties of mechanical meta-materials. Findings Metal AM techniques for mechanical meta-materials, like selective laser melting, wire-arc AM, etc, and recently developed technologies like, online inspection during the AM process, are reviewed. The defects in AMed meta-materials, along with the corresponding characterization methods, are systematically summarized. Additionally, this paper presents a comprehensive overview of evaluation approaches for the strength and fatigue properties of mechanical meta-materials, encompassing experimental testing, theoretical modeling, numerical simulation, and machine learning techniques. Future perspectives on manufacturing and the mechanical property study of mechanical meta-materials are also given. Originality/value A systematic summary of metal AM techniques, as well as defect detection and characterization methods in AMed mechanical meta-materials, is provided. Furthermore, the paper presents a comprehensive review of the mechanical properties of mechanical meta-materials, with a particular focus on strength and fatigue performance.

PurposeIn this era of Fourth Industrial Revolution, also known as Industry 4.0, additive manufacturing (AM) has been recognized as one of the nine technologies of Industry 4.0 that will revolutionize different sectors (such as manufacturing and industrial production). Therefore, this study aims to focus on “Additive Manufacturing Education” and the primary aim of this study is to investigate the impacts of AM technology at selected South African universities and develop a proposed framework for effective AM education using South African universities as the case study.Design/methodology/approachQuantitative research approach was used in this study, that is, a survey (questionnaire) was designed specifically to investigate the impacts of the existing AM technology/education and the facilities at the selected South African universities. The survey was distributed to several students (undergraduate and postgraduate) and the academic staffs within the selected universities. The questionnaire contained structured questions based on five factors/variables and followed by two open-ended questions. The data were collected and analyzed using statistical tools and were interpreted accordingly (i.e. both the closed and open-ended questions). The hypotheses were stated, tested and accepted. In conclusion, the framework for AM education at the universities was developed.FindingsBased on different literature reviewed on “framework for AM technology and education”, there is no specific framework that centers on AM education and this makes it difficult to find an existing framework for AM education to serve as a landscape to determine the new framework for AM education at the universities. Therefore, the results from this study made a significant contribution to the body of knowledge in AM, most especially in the area of education. The significant positive responses from the respondents have shown that the existing AM in-house facilities at the selected South African universities is promoting AM education and research activities. This study also shows that a number of students at the South African universities have access to AM/3D printing lab for design and research purposes. Furthermore, the findings show that the inclusion of AM education in the curriculum of both the science and engineering education is South Africa will bring very positive results. The introduction of a postgraduate degree in AM such as MSc or MEng in AM will greatly benefit the South African universities and different industries because it will increase the number of AM experts and professionals. Through literature review, this study was able to identify five factors (which includes sub-factors) that are suitable for the development of a framework for AM education, and this framework is expected to serve as base-line or building block for other universities globally to build/develop their AM journey.Research limitations/implicationsThe survey was distributed to 200 participants and 130 completed questionnaires were returned. The target audience for the survey was mainly university students (both undergraduate and postgraduate) and the academics who have access to AM machines or have used the AM/3D printing lab/facilities on their campuses for both academic and research purposes. Therefore, one of the limitations of the survey is the limited sample size; however, the sample size for this survey is considered suitable for this type of research and would allow generalization of the findings. Nevertheless, future research on this study should use larger sample size for purpose of results generalization. In addition, this study is limited to quantitative research methodology; future study should include qualitative research method. Irrespective of any existing or developed framework, there is always a need to further improve the existing framework, and therefore, the proposed framework for AM education in this study contained only five factors/variables and future should include some other factors (AM commercialization, AM continuous Improvement, etc.) to further enhance the framework.Practical implicationsThis study provides the readers and researchers within the STEM education, industry or engineering education/educators to see the importance of the inclusion of AM in the university curriculum for both undergraduate and postgraduate degrees. More so, this study serves as a roadmap for AM initiative at the universities and provides necessary factors to be considered when the universities are considering or embarking on AM education/research journey at their universities. It also serves as a guideline or platform for various investors or individual organization to see the need to invest in AM education.Originality/valueThe contribution of this study towards the existing body of knowledge in AM technology, specifically “AM education research” is in the form of proposed framework for AM education at the universities which would allow the government sectors/industry/department/bodies and key players in AM in South Africa and globally to see the need to invest significantly towards the advancement of AM technology, education and research activities at various universities.

Proceedings of the Stated Meeting held Wednesday, October 15, 1902

State-of-the-art Laser Additive Manufacturing for Hot-work Tool Steels

Additive Manufacturing Techniques in Manufacturing -An Overview

This paper describes the use of Additive Manufacturing (AM) technologies in permanent magnet synchronous machines (PMSM) for electric aircraft. Due to economic aspects and government restrictions, conventional manufacturing technologies like punching reach their limits when aiming for fulfilling future requirements. In the past years, AM has become a more and more relevant technology. In this paper, we present an investigation on how AM can be used in order to improve electric machines for electric flight propulsion. More precisely, the aim is to obtain an increasing torque and power density. Nevertheless, giving a brief overview of the feasibility of AM in electric aircraft is focused more than discussing technical issues in detail in this paper. The research starts with an extensive analysis on the current state of research of AM on electric machines and an evaluation of the degree of maturity. Moreover, geometric approaches for the rotor and the winding, which can only be built by AM, are pointed out and will be evaluated. Therefore, the winding and the rotor are modified. This results in lower ohmic loss density and lower eddy current loss for the winding as well as in higher torque density due to the decreased rotor leakage flux. So finally, the power density is increased which is crucial for flight propulsion. In summary, the huge potential of AM in all-electric aircraft is presented and performance improvements are quantified in this paper.

Advancements in understanding the microstructure and properties of additive manufacturing Ti-6Al-4V alloy: A comprehensive review

The unique design freedom offered by additive manufacturing (AM) technologies enables engineers to develop more innovative products with relatively lower costs within a shorter period of processing time in comparison with conventional manufacturing methods. On the other hand, the unique capabilities of AM have created a platform for researchers to combine several engineering methods with the new manufacturing technique to grow industrial applications as well as resolve the existing issues with AM processes. Understanding the research values that AM offers academic environments, this paper performs a systematic survey on AM-related research topics in the fields of mechanical engineering and materials science that have attracted much attention from research teams over the last few years. These topics, namely process modelling in AM, innovative research in AM, generative design by AM, material characterisation in AM processes, and finally, design for additive manufacturing (DfAM), are notably investigated through this study.

Additive manufacturing (AM) technologies continuously evolve in materials and operational processes. However, challenges related to energy consumption, material reuse efficiency, and the integration of Circular Economy (CE) principles may impede the overall sustainability of AM processes. As many industrial sectors adopt AM technologies, there is a growing need for innovative tools, methods, and frameworks that guide companies toward more sustainable and circular production practices. This shift involves improving resource efficiency and fostering new Circular Business Models that emphasize material recovery, product lifecycle extension, and waste minimization. This study assesses current and future trends in the development of sustainable AM technologies for processing metal feedstock, based on a comprehensive patent analysis covering the period from 2004 to 2024. We specifically focus on the sustainability impacts of Electron and Laser Beam Powder Bed Fusion (PBF-EB, PBF-LB) and Direct Energy Deposition (DED), given their widespread industrial applications. Patents were categorized into three groups: materials, processes, and components. Furthermore, AM technologies were analyzed according to their role within the production cycle: materials preparation, pre-processing, manufacturing, and post-processing. This categorization allows for a detailed understanding of how innovations in AM contribute to more sustainable production and consumption practices by improving energy efficiency, material usage, circularity, and overall environmental impact.

Purpose Because of the significant differences in the features and requirements of specific products and the capabilities of various additive manufacturing (AM) solutions, selecting the most appropriate AM technology can be challenging. This study aims to propose a method to solve the complex process selection in 3D printing applications, especially by creating a new multicriteria decision-making tool that takes the direct certainty of each comparison to reflect the decision-maker’s desire effectively. Design/methodology/approach The methodology proposed includes five steps: defining the AM technology selection decision criteria and constraints, extracting available AM parameters from the database, evaluating the selected AM technology parameters based on the proposed decision-making methodology, improving the accuracy of the decision by adopting newly proposed weighting scheme and selecting optimal AM technologies by integrating information gathered from the whole decision-making process. Findings To demonstrate the feasibility and reliability of the proposed methodology, this case study describes a detailed industrial application in rapid investment casting that applies the weightings to a tailored AM technologies and materials database to determine the most suitable AM process. The results showed that the proposed methodology could solve complicated AM process selection problems at both the design and manufacturing stages. Originality/value This research proposes a unique multicriteria decision-making solution, which employs an exclusive weightings calculation algorithm that converts the decision-maker's subjective priority of the involved criteria into comparable values. The proposed framework can reduce decision-maker's comparison duty and potentially reduce errors in the pairwise comparisons used in other decision-making methodologies.

Introduction The challenge in tissue engineering lies in replicating the intricate structure of the native extracellular matrix. Recent advancements in AM, notably 3D printing, offer unprecedented capabilities to tailor scaffolds precisely, controlling properties like structure and bioactivity. CAD tools complement this by facilitating design using patient-specific data. Area’s Covered This review introduces additive manufacturing (AM) and computer-aided design (CAD) as pivotal tools in advancing tissue engineering, particularly cartilage regeneration. This article explores various materials utilized in AM, focusing on polymers and hydrogels for their advantageous properties in tissue engineering applications. Integrating bioactive molecules, including growth factors, into scaffolds to promote tissue regeneration is discussed alongside strategies involving different cell sources, such as stem cells, to enhance tissue development within scaffold matrices. Expert opinion Applications of AM and CAD in addressing specific challenges like osteochondral defects and osteoarthritis in cartilage tissue engineering are highlighted. This review consolidates current research findings, offering expert insights into the evolving landscape of AM and CAD technologies in advancing tissue engineering, particularly in cartilage regeneration.

In recent years, there has been widespread adoption of machine learning (ML) technologies to unravel intricate relationships among diverse parameters in various additive manufacturing (AM) techniques. These ML models excel at recognizing complex patterns from extensive, well-curated datasets, thereby unveiling latent knowledge crucial for informed decision-making during the AM process. The collaborative synergy between ML and AM holds the potential to revolutionize the design and production of AM-printed parts. This review delves into the challenges and opportunities emerging at the intersection of these two dynamic fields. It provides a comprehensive analysis of the publication landscape for ML-related research in the field of AM, explores common ML applications in AM research (such as quality control, process optimization, design optimization, microstructure analysis, and material formulation), and concludes by presenting an outlook that underscores the utilization of advanced ML models, the development of emerging sensors, and ML applications in emerging AM-related fields. Notably, ML has garnered increased attention in AM due to its superior performance across various AM-related applications. It is envisioned that the integration of ML into AM processes will significantly enhance 3D printing capabilities across diverse AM-related research areas.

Sinter-based metal additive manufacturing (AM) processes that decouple the geometric shaping and consolidation of AM have several advantages over melt-based AM technologies (one-step consolidation and shaping). The sinter-based AM technologies, led by binder jetting, has the potential to significantly increase the productivity and attain more isotropic microstructures in the final product. Sinter-based AM includes the ability to cover a wide spectrum of productivity, including prototyping, low volume serial production, and high-volume mass production. Recently, several non-traditional sinter-based metal AM processes (relatively less common) have emerged. This paper will review some of these emerging and lesser-known sinter-based metal AM technologies along with a few processes based on modifications of binder jet and material extrusion techniques that have gained traction.

Additive manufacturing (AM) processes have revolutionized manufacturing industries by enabling the production of complex geometries with reduced material waste and lead times. However, ensuring the quality of AM parts remains a significant challenge due to the complexity of the process and inherent variability in material properties. This review investigates the use of artificial intelligence (AI) to enhance quality assurance in AM processes, focusing on specific machine learning techniques such as convolutional neural networks for defect detection, support vector machines for classification of material properties, and reinforcement learning for real-time process optimization. The AI-driven methodologies are applied to predict defects, optimize process parameters, and monitor real-time production quality, utilizing large datasets generated from sensors and in-situ monitoring systems. The study demonstrates significant improvements in the accuracy of defect detection, the reliability of material property classification, and the efficiency of process optimization. In addition, it addresses challenges such as data pre-processing, model interpretability, and integration with existing AM systems. The findings highlight the potential of AI to transform quality assurance in AM and outline future research directions for further integration and enhancement of AI techniques in AM.

PurposeMaterial extrusion (MEX) is a class of additive manufacturing (AM) process based on MEX principle. In the viewpoint of Industry 4.0 and sustainable manufacturing, AM technologies are gaining importance than conventional manufacturing route (subtractive manufacturing). Because of the ease of use and lesser operation skills, MEX had wide popularity in industry for product and prototype development. This study aims to analyze energy consumption of MEX-based AM process and its influencing factors.Design/methodology/approachA group of factors were identified pertaining to MEX-based AM process. In this viewpoint, this study presents the configuration of a structural model using interpretive structural modeling (ISM) to depict dominant factors in MEX-based AM process. A total of 18 influencing factors are identified and ranked using ISM methodology for MEX process. The Impact Matrix Cross-reference Multiplication Applied to a Classification analysis was done to categorize influencing factors into four groups for MEX-based AM process.FindingsThe derivation of structural model would enable AM practitioners to systematically analyze the factors and to derive key factors which enable comprehensive energy modeling and energy assessment studies. Also, it facilitates the development of energy efficient AM system.Originality/valueThe development of structural model for analysis of factors influencing energy consumption of MEX-based AM is the original contribution of the authors.