Additive Manufacturing Industry Research Articles

Sustainable additive manufacturing supply chains with a plithogenic stakeholder analysis: Waste reduction through digital transformation

The Additive Manufacturing (AM) industry is of paramount importance as means of personalized design capabilities by rapid prototyping, using exact amount of required materials avoiding waste, and applying Industry 4.0 technologies with digital transformation ability. The characteristics of AM are regarded as the prerequisites for sustainability in the manufacturing industry. However, there are limited papers discussing the stakeholders and sustainability objectives in a single comprehensive analysis. Hence, the purpose of this research is to identify the degree of each stakeholder to achieve the objective of sustainability in AM supply chain by proposing a novel Plithogenic Fuzzy MACTOR approach. Results reveal that the customers are in triggering position to shape entire AM supply chain as the demand generators. Workforce is a significant player in the AM supply chain by creating the design, manufacturing, marketing, and communicating to provide the collaborations in this business segment. Academia positions here a supportive role to enable the whole supply chain members in terms of delivering technological advancements, training people, and providing the workforce. The AM manufacturers and material / software / printer suppliers are the key players leading to the supply chain by producing the main business products. Thus, a practitioner can interpret its position in the AM supply chain and understand the requirements of sustainability points in detail. Besides, this study provides a theoretical contribution to the literature by extending the MACTOR analysis with Plithogenic sets via including a different uncertainty measure.

Sustainable Transformation: A Case Study of an Engineering-to-Order Additive Manufacturing Firm

Purpose: Addressing sustainability challenges in additive manufacturing (AM) is critical due to high energy consumption and waste. Optimising AM operations is vital for sustainability. However, the existing literature lacks practical examples for AM companies on how to enhance sustainability. To address this gap, a case study within an AM firm was conducted. Design approach: Using the analytical hierarchy process (AHP), sustainability key performance indicators (KPIs) were identified, aligned with the firm’s strategic objectives. Sustainable value stream mapping (Sus-VSM) analysed the production process for sustainability performance. A sustainability strategy map (SSM) was created based on the five perspectives of the 4th generation balanced scorecard (BSC) approach and the results from the case study. Findings: The research identified optimisation areas in the multi-jet-fusion (MJF) process, crafted tailored sustainability KPIs, and developed a SSM to seamlessly integrate sustainability initiatives into the company’s vision. Value: This contribution provides a structured methodology for selecting KPIs and offers a holistic approach to implementing Sus-VSM within the AM industry. The manuscript emphasises the need to incorporate a fifth perspective into the BSC and proposes a framework for developing a SSM. This study aims to guide AM companies toward a more environmentally responsible and socially conscious approach to manufacturing.

Graph neural network-based multiscale thermal modeling for heterogeneous materials with complex structures

This study presents a novel GNN-based model for multiscale thermal characterization of heterogeneous materials with complex microstructures. It incorporates relationships between composition, structure, and thermal transport across multiple scales, leveraging the structure of GNNs. Results demonstrate enhanced performance of the GNN-based model compared with existing methods. Specifically, the GNN model improved thermal conductivity prediction with a mean absolute error of 0.18 W/mK (15 % improvement over the Bayesian neural network-based model) and provided a 30x speedup in computation time. The model successfully connected atomic-scale interactions to macroscale properties, achieving less than 5 % error in predicting effective thermal conductivity from one scale to another. Furthermore, it demonstrated the ability to capture nonlinear thermal transport phenomena with 92 % accuracy. The model also improved the prediction of the transient thermal responses by 20 %, accurately capturing the time-varying behaviour of materials. This work also proved the model’s ability to handle new material systems with transfer learning, achieving 88 % accuracy. Finally, Bayesian neural networks integrated into the model provided uncertainty quantification, with 95 % of experimental measurements falling within the predicted bounds. This work demonstrated the ability of the framework to handle complex microstructures, nonlinear phenomena, and transient responses while maintaining a computationally efficient model that can enable real-time applications in design and optimization needed in modern materials innovation. It represents a powerful tool for data-driven multiscale mechanics that could accelerate materials research and development for the aerospace, electronics cooling, and additive manufacturing industries.

Evaluation of the layering adhesion between PLA and TPE materials

Abstract The additive manufacturing industry has witnessed rapid growth and has applications across various sectors such as aerospace, automotive, healthcare, and consumer goods. Both Thermoplastic Elastomer (TPE) and Polylactic Acid (PLA) hold significant relevance in these industries because of their unique properties and environmental sustainability, thus driving the demand for research aimed at improving their compatibility and performance in multi-material printing processes. This study delves into the interlayer adhesion aspects of multi-material filament fabrication, particularly focusing on the incorporation of TPE and PLA. TPEs offer elasticity and flexibility, while PLA provides rigidity and biodegradability, making them an intriguing combination for a wide range of applications. Experimental investigations of mechanical properties, specifically tensile, compression, and Charpy impact tests, are conducted to evaluate the interlayer adhesion between TPE and PLA. This study aims to contribute to the understanding of interlayer adhesion in multi-material filament fabrication, focusing on the integration of TPE and PLA. Through experimental analysis and exploration of industrial applications, we seek to shed light on the potential of these materials in additive manufacturing and outline future directions for research and development in this field.

A new model framework of laser powder bed fusion by integrating a powder-scale model with a thermodynamic database

Laser powder bed fusion (LPBF) has been increasingly practised in powder-based additive manufacturing industries, yet the fundamentals including multiphase flow and phase change were not well understood. This work develops a powder-scale computational fluid dynamics-discrete element method-volume of fluid (CFD-DEM-VOF) model integrated with a temperature-dependent thermodynamic database to simulate the multiphase flow with phase change in the LPBF process. The integrated model is validated by comparing prediction results with the experimental measurements. Several LPBF cases with different laser powers, track numbers, and hatch distances are numerically studied. The simulation results demonstrate that the model can capture the key phenomena and key features in the LPBF process observed experimentally, including temperature distribution and morphology of molten tracks. The results also show the random configuration of the powders leads to the asymmetrical distribution of the temperature in the molten pool. The laser beam with a lower power density causes pore defects in the transition zone of the laser track because of partially melted particles, causing a rough molten surface. Incorporating an overlap between two molten tracks is crucial for enhancing the surface quality and mitigating issues such as discontinuity and particle adherence along the track edge. This model provides a cost-effective solution for comprehending and optimizing multiphase flow and phase change phenomena within the LPBF process in additive manufacturing.

Genetic Algorithm-Based Data-Driven Process Selection System for Additive Manufacturing in Industry 4.0.

Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications. However, additive manufacturing, as an upcoming field, lacks automated and specific design rules for different AM processes. Moreover, the selection of specific AM processes for different geometries requires expert knowledge, which is difficult to replicate. An automated and data-driven system is needed that can capture the AM expert knowledge base and apply it to 3D-printed parts to avoid manufacturability issues. This research aims to develop a data-driven system for AM process selection within the design for additive manufacturing (DFAM) framework for Industry 4.0. A Genetic and Evolutionary Feature Weighting technique was optimized using 3D CAD data as an input to identify the optimal AM technique based on several requirements and constraints. A two-stage model was developed wherein the stage 1 model displayed average accuracies of 70% and the stage 2 model showed higher average accuracies of up to 97.33% based on quantitative feature labeling and augmentation of the datasets. The steady-state genetic algorithm (SSGA) was determined to be the most effective algorithm after benchmarking against estimation of distribution algorithm (EDA) and particle swarm optimization (PSO) algorithms, respectively. The output of this system leads to the identification of optimal AM processes for manufacturing 3D objects. This paper presents an automated design for an additive manufacturing system that is accurate and can be extended to other 3D-printing processes.

Selective laser sintering at the Point-of-Care 3D printing laboratory in hospitals for cranio-maxillo-facial surgery: A further step into industrial additive manufacturing made available to clinicians

Additive manufacturing has developed rapidly in recent years and has many useful applications in the clinical field. In particular, cranio-maxillo-facial (CMF) surgery requires high precision, which can be obtained with 3D printed patient-specific surgical guides and anatomical models. Among the many different printing options, selective laser sintering (SLS) seems to be rarely used in point-of-care applications, considering its apparent characteristics.This article examines the advantages and disadvantages of SLS printers for CMF point-of-care (PoC) by reviewing the literature and comparing in-house printed SLS and stereolithography (SLA) prints.The investigation showed that the easily sterilizable and robust materials processed by SLS printing are well suited for CMF surgical guides and have clear advantages over SLA parts.Some barriers to the use of SLS printers in PoC are likely to be the slightly higher complexity and cost.However, these will decrease as 3D printing technology advances and surgeon acceptance increases, making SLS a practical PoC tool.

Examining the moderating role of organizational emotional culture on the relationship between leader emotional intelligence and employee job performance in South Africa’s additive manufacturing industry

South Africa’s additive manufacturing research has mostly concentrated on engineering and technical aspects, with a lack of studies on management and organizational dynamics. To fill this gap, we conducted research on the direct and moderating links between leader emotional intelligence, organizational emotional culture, and employee job performance in South African additive manufacturing enterprises. We used a deductive research approach, a quantitative research method, and a cross-sectional explanatory correlational study design. Our study used simple random sampling to select 148 employees of additive manufacturing firms in South Africa. Questionnaires served as the data collection instruments, and online data gathering was done using Google Forms. The Statistical Package for Social Sciences (SPSS) version 26 software was used for data coding, data entry, and descriptive statistics. Smart PLS 4 was used to conduct partial Least Squares (PLS) Structural Equation Modelling. Findings of the study demonstrate that a significant positive relationship exists between leader emotional intelligence and employee job performance. A significant positive relationship also exists between leader emotional intelligence and organizational emotional climate. A significant positive relationship was found to exist between organizational emotional culture and employee job performance. The finding also showed that organizational emotional culture moderates the relationship between leader emotional intelligence and employee job performance. The findings suggest that developing emotional intelligence among leaders in South African additive manufacturing enterprises could enhance both staff productivity and the organizational emotional environment. Our findings highlight the importance of prioritizing leader emotional intelligence training and development for additive manufacturing enterprises in creating a good emotional environment conducive to enhanced staff job performance.

Enhancing mechanical performance in SLS-printed PA12-slate composites through amino-silane treatment of mineral waste

The SLS additive manufacturing industry enables the development of products for diverse applications with distinct properties due to its excellent surface finish and ability to create varied part geometries, but it consumes high-performance materials with high acquisition costs. An extensive quarrying of stone leads to the accumulation of mineral residues, posing environmental hazards by contaminating soil and water when disposed of in landfills. The primary objective of the study was to incorporate mineral waste into the SLS technique and investigate the influence of its addition, along with a silane-based chemical treatment, on the mechanical performance of polymer-mineral composites (PA12-slate). Additionally, the feasibility of producing a highly loaded printed prototype, employing 50 wt% of mineral waste, was examined. Samples of PA12, PA12 blended with 50 wt% slate waste, and slate waste treated with silane underwent fabrication via selective laser sintering (SLS) and subsequent mechanical characterization, including tensile, flexural, and compressive tests. Additionally, the samples underwent accelerated aging using a QUV weathering tester, followed by mechanical characterization. The geometric accuracy, stability, and processing feasibility of these formulations were evaluated through SLS-printed composite prototypes utilizing PA12_50Sla_Si. It was found that the addition of 50% of slate to the PA12 presented mechanical properties decreasing compared to the printed PA12 only. However, an increase was verified when using silane-induced mineral bonding. The incorporation of mineral agents and silane enhanced the resistance of PA12 to aging. However, after aging, both tensile and flexural strength decreased across all printed samples. Nonetheless, this study showcased the feasibility of producing complex PA12-slate waste specimens containing up to 50 wt% of mineral waste using the SLS printing technique. Therefore, SLS presents itself as a viable means of adding value to this mineral waste.

Subcutaneous coding via laser directed energy deposition (DED-LB) for unalterable component identification

Part identification has become a priority in manufacturing, especially when it comes to the additive manufacturing (AM) industry, where the counterfeit printing of almost any digitalized component is possible. As a means of avoiding forgery, the development of non-detectable labels, or digital passports is necessary. In the present work, a novel methodology for printing subcutaneous coding on aeronautical parts is proposed thanks to the multimaterial capabilities of the laser based directed energy deposition (DED-LB). The coding is based on embedding a dot pattern of a high-density alloy on a small area of a lower density component, which once covered and finished generates a pattern only visible by X-ray imagining. The viability of the proposed methodology is proven by embedding WC particles on a Ti6Al4V substrate. Firstly, the most relevant process parameters are optimized to ensure a sharp and a readable code, and their geometry is analysed by means of industrial Computed Tomography (CT). Also, the metallurgical quality and chemical composition of the generated dots is evaluated by Scanning Electron Microscope (SEM). Finally, a demonstrator part is fabricated with a hidden code. The code readability and the mechanical properties are tested to ensure the feasibility of the developed methodology.

Sensitivity of mechanical properties to processing defects: Is tensile testing an appropriate metric for laser beam metal powder bed fusion machine qualification?

The Laser-based Powder Bed Fusion of Metals (PBF-LB/M) process has shown potential in the manufacturing of critical flight components, yet also has faced difficulties in achieving reproducibility. One potential reason for this is that tensile testing, which is one of the methods traditionally used to ascertain quality in the additive manufacturing (AM) industry, is insufficient for capturing underlying flaws in an additively manufactured component. To quantify the ability of tensile testing to detect underlying flaws in a component, a generalized full factorial experiment was performed where the tensile properties of UNS N07718 were measured across several types of build conditions. Combinations of differently sized bars and surface conditions were investigated. Defects were induced by amplifying the polygon delay parameter, hence prolonging the dwell time of the laser in the contours. The authors found that those tensile bars that experienced increased polygon delay had ∼10x more pores than those built with nominal parameter values while still maintaining an average porosity of <0.25 % (i.e., a total part porosity that is acceptable by NASA-STD-6030). Regardless of the specimen geometry and surface condition, differences between parts which had undergone polygon delay and those that had not were either statistically insignificant or were too small to be relevant, indicating that tensile testing may not be an effective metric for identifying the quality of a PBF-LB/M process.

Comprehensive Evaluation of Printing Process Parameters and Tensile Properties of Coconut Polypropylene Filament Composites in Additive Manufacturing

The printing process parameters are important in producing coconut polypropylene (CcPP) products by filament composite in the additive manufacturing industry. Fused deposition modeling techniques in 3D printing applications have considered multiple parameters to meet good finishing parts. This study uses comprehensive measurements to identify the best printing parameter for evaluating the composite properties. Complete deposition, unwrapping, good finishing, and adequate heat are the qualitative printing process parameters used to finalize the optimum nozzle and bed temperature of the CcPP filament composite. The range between 50⁰C to 80⁰C and 225⁰C to 245⁰C for bed and nozzle temperatures were used to achieve a well surface and successful production. After multiple trials of printing the CcPP filament composite, the optimal bed and nozzle temperatures were found to be 80°C and 230°C, respectively. Two types of infill density were used to analyze the effect on the tensile properties of CcPP filament composite. The result showed that 50% infill density was higher for both 1% and 5% fiber loading than 25% infill density for tensile strength with 15.65 and 22.87 MPa compared to 12.93 and 16.59 MPa. The same pattern as the score of Young’s moduli of 50% infill density was higher than 25% infill density for 1% and 5% fiber loadings with 479.52 and 641.23 MPa compared to 400.17 and 493 MPa. Qualitative and quantitative measurements of printing process parameters and properties help reduce the time and cost and benefit 3D printer users in the industry.

BRIDGING DATA GAPS: A FEDERATED LEARNING APPROACH TO HEAT EMISSION PREDICTION IN LASER POWDER BED FUSION

Abstract Deep learning has impacted defect prediction in Additive Manufacturing (AM), which is important to ensure process stability and part quality. However, its success depends on extensive training, requiring large, homogenous datasets–remaining a challenge for the AM industry, particularly for small- and medium-sized enterprises (SMEs). The unique and varied characteristics of AM parts, along with the limited resources of SMEs, hampers data collection, posing difficulties in the independent training of deep learning models. Addressing these concerns requires enabling knowledge sharing from the similarities in the physics of the AM process and defect formation mechanisms while carefully handling privacy concerns. Federated learning (FL) offers a solution to allow collaborative model training across multiple entities without sharing local data. This paper introduces an FL framework to predict section-wise heat emission during Laser Powder Bed Fusion (LPBF), a vital process signature. It incorporates a customized Long Short-Term Memory (LSTM) model for each client, capturing the dynamic AM process's time series properties without sharing sensitive information. Three advanced FL algorithms are integrated–FedAvg, FedProx, and FedAvgM–to aggregate model weights rather than raw datasets. Experiments demonstrate that the FL framework ensures convergence and maintains prediction performance comparable to individually trained models. This work demonstrates the potential of FL-enabled AM modeling and prediction where SMEs can improve their product quality without compromising data privacy.

Build Orientation Effect on Bending Fatigue Performance and Impact Toughness of Laser Powder Bed Fusion Manufactured Ti6Al4V Without Heat Treatment

Titanium alloys are highly valued in various industries due to their exceptional qualities. This study examines how the build orientation affects the mechanical and fatigue properties of Laser Powder Bed Fusion (PBF-LB) produced Ti6Al4V, without heat treatment. The research shows mechanical properties vary based on build orientation with vertically oriented specimens exhibiting the highest yield and tensile strengths, while vertical orientation excels in ductility, measured through elongation at break. Impact toughness sees variations with horizontal orientation performing the best. However, build orientation has minimal influence on flexural bending fatigue performance. Both diagonal and vertical orientations show similar fatigue limits at around 40 MPa. Dry electropolishing proves to be an effective technique, significantly enhancing fatigue performance with limits stabilizing at about 150 MPa. This study underscores the importance of considering build orientation in PBF-LB manufacturing for specific mechanical and impact properties and the potential of dry electropolishing in improving the fatigue performance of Ti6Al4V components. These findings offer valuable insights for the additive manufacturing industry, aiding in the optimization of Ti6Al4V component production.

A mechanism-based optimization strategy with adaptive laser power for laser powder bed fusion

The presence of keyhole-induced porosity significantly limits the application of laser powder bed fusion (L-PBF) in the additive manufacturing industry. Typically, a trial-and-error approach is used to select the best printing strategies for material type, powder bed, and laser beam to optimize the manufacturing process. This paper proposes an optimization strategy that uses adaptive laser power based on key understanding of the physical mechanisms of keyhole fluctuations. We identify and analyze five stages of the keyhole pore formation process, including the "J"-like keyhole formation, keyhole closure, keyhole pore collapse, keyhole pore splitting, and keyhole pore motion. Our simulation results suggest that reducing the laser power at the onset of "J"-like keyhole formation is an appropriate optimization approach. We propose adaptive indices to quantify keyhole fluctuations and predict the onset of "J"-like keyhole formation, which facilitates the use of adaptive laser power for optimization. Two parametric studies are conducted to investigate the impact of the optimization criterion and laser power adjustment on keyhole instability. The results demonstrates that our optimization strategy can effectively stabilize the keyhole and reduce the occurrence of keyhole porosity.

Laser powder bed fusion of bio-inspired metamaterials for energy absorption applications: A review

A significant amount of research has been done in the last few decades to reduce the risk of injury for occupants and the structures that are subjected to impact loading. Metamaterials have been proven to be useful in energy-absorbing structures to improve a structure's crashworthiness performance by reducing the negative impacts during collision. The metal additive manufacturing industry, especially Laser Powder Bed Fusion (LPBF), has made it easier to produce complex metamaterials with remarkable mechanical characteristics like lightweight, high specific strength, and effective energy absorption. This review paper investigates the transformative potential of bio-inspired metamaterial designs, which are additively manufactured using LPBF machines, for use in protective energy-absorbing structures. First, biomimicry in engineering is briefly discussed. The review focuses on the energy absorption performance of different designs, like thin-walled structures and different bio-inspired metamaterials. It discusses the effects of base metal, process conditions, and manufacturing defects. Optimization methods to enhance the design and crashworthiness of these bio-inspired energy absorbers are investigated. Various characterization methodologies including experimental techniques and numerical simulations, are highlighted, with a particular emphasis on integrating manufacturing defects into simulations. Finally, possible applications and future trends in aerospace, automotive, construction, and medical applications are reviewed. Despite decades of research into energy absorbers, there remains a lack of a comprehensive review on the use of additively manufactured metamaterials as energy absorbers, particularly those inspired by nature. This review paper addresses this gap by examining recent studies in the field, assessing the effectiveness of various bio-inspired metamaterial designs, their crashworthiness, and the associated characterization methods under different loading scenarios.

Flexible laser plastic welding as an addition to AM

AbstractIndividualization, shorter product cycles and prototyping are leading to increased use of additive manufacturing in industry. A reliable and versatile process like laser plastic welding is needed to join parts with other additively manufactured or injection molded parts.

Application of Additive Manufacturing in the Automobile Industry: A Mini Review

The automobile industry is recognized as one of the most influential sectors shaping global economies, societies, and individual lifestyles. Therefore, fierce competition among different companies is continuously undergoing, and special attention is focused on innovations to improve competitiveness. In the past several years, additive manufacturing (AM) has emerged as an innovative technology in applications in the automobile industry with significant advantages over traditional techniques. As a result, increasing efforts have been paid to combining AM technology with the development of the automobile industry. Currently, many automobile players are optimizing their industrial layout by incorporating innovative AM techniques, and meanwhile, a lot of research progress has been achieved in order to meet the market demand. This article aims at presenting a timely review to conclude the recent advances in the application of AM techniques in the automobile industry, focusing on the available AM techniques, printable materials, and industry applications, based on which the advantages and disadvantages of each technique and material system are discussed in order to reveal the current application situation. The current research gaps and challenges are also outlined to indicate future research opportunities. Hopefully, this work can be useful to related researchers as well as game players in the industry of this field.

Additive Manufacturing in Industry 4.0 and its Implications for Sustainability in Production Processes

Objective: This research aimed to analyze the implications of the use of additive manufacturing for sustainability in production processes. Methodology: The research was characterized as exploratory qualitative approach, which was carried out with 15 professionals from a Brazilian industry. Data collection was conducted through in-depth interviews, and analysis of the speech revealed insights into the environmental implications of additive manufacturing. Results and discussion: As a result, it was evidenced that despite the benefits offered by additive manufacturing for sustainability, such as reducing material waste and personalized production, there are significant challenges to be faced. Concern about the recyclability of materials used in 3D printing has been identified as a critical point, underlining the pressing need to develop more efficient and sustainable recycling processes to deal with the waste generated. In addition, mentioning the importance of considering the full life cycle of products emphasizes the complexity of environmental issues associated with additive manufacturing, requiring holistic approaches that consider environmental impacts at all stages of the process. Conclusion: Therefore, the need for an integrated approach to sustainability in additive manufacturing is highlighted, which not only maximizes the environmental benefits of the technology, but also proactively deals with emerging challenges, thus ensuring a positive impact on the environment.

Explainable machine learning for the regulatory environment: A case study in micro-droplet printing

Micro-droplet printing remains an invaluable tool for the additive manufacturing industry due to its versatility, efficiency and cost-effectiveness. The design and deployment of a disposable micro-droplet dispenser seeks to facilitate a quick and easy way to alternate between different printing materials (such as ink, metals, bio-materials, pharmaceuticals, etc.) without the need for thorough cleaning or separate delivery systems. While this works well in principle, in practice achieving consistent performance between dispensers can prove challenging. This paper aims to tackle this issue through the use of Machine Learning (ML) in order to (1) build a model that predicts dispenser performance from a set of physical features, (2) explain the model’s predictions by relating features to outcome, and (3) suggest criteria that characterize optimal performance. A micro-droplet dispenser was used in conjunction with a drop-watcher for in-situ monitoring of droplet formation. Using this setup, 300 micro-droplet dispensers were evaluated for printing performance while various physical measurements of the dispensers were taken separately using a high magnification digital microscope. Logistic Regression, Random Forest, Support Vector Machine and Gradient Boosting Machine algorithms were used to train and test classification models based on dispenser performance. AUC scores in the range of 0.75–0.85 were observed for the various algorithms while Partial Dependence Plots were utilized to investigate how the physical attributes of the dispenser impacted performance. Importantly, results were presented and interpreted in the context of providing an insight to transparency for ML in the regulatory environment.