Additive Manufacturing Industry Research Articles

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.

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.

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.

Investigation of melt pool dynamics and solidification microstructures of laser melted Ti-6Al-4V powder using X-ray synchrotron imaging

Titanium alloy (Ti-6Al-4V) is widely used in additive manufacturing (AM) industry. However, as laser powder-bed fusion (PBF-L) additive manufacturing (AM) advances towards reliable production of titanium parts, a thorough understanding of the process-structure-properties (PSP) relationships remain to be fully understood. A study of the laser melting was paired with high-speed X-ray synchrotron imaging at the 32-ID beamline of the Advanced Photon Source at Argonne National Laboratory. Simultaneous melting and imaging was carried out on a Ti-6Al-4V powder layer held in a custom device designed to mimic single-track scans of the PBF-L process at different laser power levels, powder size distributions, and cover gas environments (Ar and He) on top of AM Ti-6Al-4V base metal. It was found that the thickness of the powder layer significantly affected the melt behavior: too much powder led to the formation of molten droplets that wetted the surface of the titanium, yet did not contribute to a uniform melting profile. Residual gas pores in the atomized powder were also observed to contribute to the pores observed in the melt pool, with the porosity of the powder (defined as volume of pores divided by total material volume) constant with powder size distribution (i.e., larger particles contained more entrapped gas, which increased final part porosity). When varying Ar or He through the same gas flow meter settings and nozzle, the difference in flow rates likely contributed more to the resultant porosity of the solidified material than did the thermal conductivity of the gasses, with He being the greater contributor to porosity. The microstructure of the heat affected zone contained α′, α, and an increased β phase fraction relative to the base material. The crystallographic texture of the melt pool region adopted that of the base metal.

Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach

Aerosol jet printing (AJP) is a newly-developed technology for printed electronics in additive manufacturing (AM) industry. Compared to conventional printed electronics, AJP technology has the capability of fabricating high-resolution patterns (∼ 10 µm) with multiple materials and high degrees of freedom. However, insufficiently optimizing the printing process, including reliance on traditional trial-and-error methods and theoretical models, may lead to a decline in production quality, consequently limiting the widespread adoption of AJP technology. In this paper, an integrated machine learning approach is developed for autonomous optimization of process parameters and in-situ anomaly detection in AJP. In the developed approach, a coaxial camera is incorporated with a stepwise data-driven modeling approach for optimal operating window identification and process model development of AJP, thereby systematically optimizing the main process parameters prior to printing. Subsequently, a convolutional neural network (CNN) model is developed and integrated with an alignment camera for in-situ process monitoring and printing status evaluation, enabling the anomaly detection of the AJP process. Afterwards, leveraging process similarity through transfer learning allows rapid calibration of detected system drifts or variations in process stability, thereby enhancing the product consistency of AJP. Generally, by utilizing state-of-the-art machine learning techniques, the autonomous optimization of process parameters and anomaly detection in AJP achieve accuracies of 95.3% and 92.7%, respectively. This indicates the effectiveness and systematic nature of the developed approach compared to traditional trial-and-error methods and theoretical models. Furthermore, due to its data-driven characteristics, this approach has the potential to extend its applicability to other noncontact ink writing techniques, facilitating further research for process optimization in AM.

A novel feature engineering approach for predicting melt pool depth during LPBF by machine learning models

Melt pool geometry is a deterministic factor affecting the characteristics of metal Additive Manufacturing (AM) components. The wide array of physical and thermal phenomena involved during the formation of the AM melt pool, along with the great variety of alloy compositions and AM methods, coupled with the clear influence of multiple process parameters, make it difficult to predict the melt pool geometry under a given set of conditions. Therefore, using Artificial Intelligence (AI) approaches such as Machine Learning (ML) is necessary for accurate predictions. Using a physics-informed feature selection strategy along with the application of atomic features for the first time, this work aims to offer accurately trained models relying on existing high-fidelity data for most common alloys in AM academia and industry, i.e., 316 L stainless steel, Ti6Al4V, and AlSi10Mg. Multiple ML algorithms were trained, and the results revealed that the average R2 and RMSE obtained by the K-fold cross-validation (K = 5) were significantly enhanced when laser and material properties, inspired by the analytical models for AM melt pool geometry, were used as the model features. Removing the excess features and applying atomic features further enhanced the accuracy of the models. As a result, R2 for the XGBoost, CatBoost, and GPR models were 0.907, 0.889, and 0.882, respectively, while the hold-out cross-validation led to 0.978, 0.976, and 0.945, respectively. Furthermore, the results showed that the XGBoost model outperforms the Rosenthal equation. This approach provides a pathway to more accurately predict the properties of metal AM components.

Innovative measurement tool optimizes development

AbstractThe metalworking industry is evolving. Additive manufacturing methods – even in combination with machining treatments – are paving the way for more efficient processes and innovative designs. Reichenbacher Hamuel has recognized this potential. In very short order, the company has developed industrial additive manufacturing systems based on the laser powder bed fusion process. Here, measuring technology plays a decisive role.

Surface‐methacrylated microcrystalline cellulose bioresins with soybean oil for additive manufacturing via vat photopolymerization

AbstractThe additive manufacturing (AM) industry increasingly looks to differentiate itself by utilizing materials and processes that are green, clean, and sustainable. Biopolymers, bio‐sourced raw materials and light weighting of parts 3D printed with photopolymer resins each represent critical directions for the future of AM. Here, we report a series of bio‐based composite resins with soybean oil derivatives, up to 20% by weight of surface‐methacrylated micro‐crystalline cellulose (MCC) and 60% total bio‐based content for vat photopolymerization based additive manufacturing. The ultimate tensile strengths of the materials were found to increase up to 3X, the Young's moduli increased up to 10X, and the glass transition temperature increased by 11.3°C when compared to the neat resin without surface‐methacrylated MCC as a filler. Working curves and shrinkage factors were used to demonstrate how the surface‐methacrylated MCC causes changes in the dimensions of printed parts, to facilitate development of optimized print parameters based on the UV intensity of the 3D printer being used. These results will allow further development of commercial 3D printable resins with a high concentration of bio‐based fillers that print well and perform on par with conventional resins.

Optimal shape design of printing nozzles for extrusion-based additive manufacturing

The optimal design seeks the best possible solution(s) for a mechanical structure, device, or system, satisfying a series of requirements and leading to the best performance. In this work, optimized nozzle shapes have been designed for a wide range of polymer melts to be used in extrusion-based additive manufacturing, which aims to minimize pressure drop and allow greater flow control at large extrusion velocities. This is achieved with a twofold approach, combining a global optimization algorithm with computational fluid dynamics for optimizing a contraction geometry for viscoelastic fluids and validating these geometries experimentally. In the optimization process, variable coordinates for the nozzle’s contraction section are defined, the objective function is selected, and the optimization algorithm is guided within manufacturing constraints. Comparisons of flow-type and streamline plots reveal that the nozzle shape significantly influences flow patterns. Depending on the rheological properties, the optimized solution either promotes shear or extensional flow, enhancing the material flow rate. Finally, experimental validation of the nozzle performance assessed the actual printing flow, the extrusion force and the overall print control. It is shown that optimizing the nozzle can significantly reduce backflow-related pressure drop, positively impacting total pressure drop (up to 41 %) and reducing backflow effects. This work has real-world implications for the additive manufacturing industry, offering opportunities for increased printing speeds, enhanced productivity.

Design of Experiments to Compare the Mechanical Properties of Polylactic Acid Using Material Extrusion Three-Dimensional-Printing Thermal Parameters Based on a Cyber-Physical Production System.

The material extrusion 3D printing process known as fused deposition modeling (FDM) has recently gained relevance in the additive manufacturing industry for large-scale part production. However, improving the real-time monitoring of the process in terms of its mechanical properties remains important to extend the lifespan of numerous critical applications. To enhance the monitoring of mechanical properties during printing, it is necessary to understand the relationship between temperature profiles and ultimate tensile strength (UTS). This study uses a cyber-physical production system (CPPS) to analyze the impact of four key thermal parameters on the tensile properties of polylactic acid (PLA). Layer thickness, printing speed, and extrusion temperature are the most influential factors, while bed temperature has less impact. The Taguchi L-9 array and the full factorial design of experiments were implemented along with the deposited line's local fused temperature profile analysis. Furthermore, correlations between temperature profiles with the bonding strength during layer adhesion and part solidification can be stated. The results showed that layer thickness is the most important factor, followed by printing speed and extrusion temperature, with very close influence between each other. The lowest impact is attributed to bed temperature. In the experiments, the UTS values varied from 46.38 MPa to 56.19 MPa. This represents an increase in the UTS of around 17% from the same material and printing design conditions but different temperature profiles. Additionally, it was possible to observe that the influence of the parameter variations was not linear in terms of the UTS value or temperature profiles. For example, the increase in the UTS at the 0.6 mm layer thickness was around four times greater than the increase at 0.4 mm. Finally, even when it was found that an increase in the layer temperature led to an increase in the value of the UTS, for some of the parameters, it could be observed that it was not the main factor that caused the UTS to increase. From the monitoring conditions analyzed, it was concluded that the material requires an optimal thermal transition between deposition, adhesion, and layer solidification in order to result in part components with good mechanical properties. A tracking or monitoring system, such as the one designed, can serve as a potential tool for reducing the anisotropy in part production in 3D printing systems.

Multi-layer additive manufacturing on nickel-based superalloy by optimization of pulsed mode process parameters of single-layer bead geometry

Recent advancements in the wire arc additive manufacturing (WAAM) process for nickel-based superalloys have had a substantial impact on metal additive manufacturing industries such as aerospace, energy, and construction. The input process parameters influence the WAAM part's dimensional accuracy, process stability, and other critical impacts. The quality of each weld layers in WAAM can be controlled by various process parameters. In this study, a single-layer bead was deposited from Hastelloy C-22 using the pulsed mode gas tungsten arc welding (PC-GTAW) process. The chosen input process parameters include pulse frequency (X1), peak current (X2), and pulse on time (X3). The design of experiments (DoE) statistical quality tool was used to optimize the parameters, and the response surface m`ethodology (RSM) technique was used to develop the design matrix. The experiments were conducted based on central composite design (CCD), and their resulting responses bead width (Y1), height (Y2), and depth of penetration (Y3) were evaluated using optical microscopy. The results of the geometrical analysis have revealed a significant correlation between the characteristics of the layer and the parameters of the process. The analysis of variance (ANOVA) method was employed to verify the validity of the actual and predicted models. The model was experimentally validated using the established optimal process parameters, and the mechanical and metallurgical performance was evaluated. The microstructure of the single-layer exhibits equiaxed, cellular, and columnar structures at various regions. The pulse mode process was found to effectively reduce elemental segregation, improve the microstructure, and enhance the mechanical properties of the weld layer. The multi-layer experiments were carried out using the optimal process parameters of a single layer. According to metallographic investigations, the multi-layer that has been deposited exhibits a lack of cracks and porosity. This study provides new insights into the stabilized properties necessary to achieve optimal process parameters and metallurgical characteristics in the robust wire arc additive manufacturing of Hastelloy C-22.

Antibacterial and biodegradable food packaging film from bacterial cellulose

Antimicrobial film is becoming the - revolutionary active packaging materials for preserving food quality and extending its shelf life. Consequently, attention towards antimicrobial film is recently on the rise from food scientists for developing eco-friendly, biodegradable, and edible food packaging products over to plastic materials. Bacterial cellulose (BC), commonly known as cellulose generated from bacteria, is gaining popularity in biomedical, additive manufacturing, environmental, and food industries. As such, BC does not have antimicrobial and antioxidative properties. Therefore, researchers are fortifying BC by adding various food additive materials and ingredients to improve shelf-life, antimicrobial and antioxidant properties. Our review paper, summarized the quality parameters of BC, including tensile strength, shelf life, mechanical properties, structural properties, adhesive properties, color, surface hydrophobicity, biodegradability, and thermal stability by adding various additives. This review provides benefits for the food industry to select and produce fortified BC with enhanced shelf-life and other functional qualities. Furthermore, this report will help food scientists to develop novel food packaging materials with improved quality and eco-friendly.

Mechanical metamaterials: properties and classification

The work presents and describes distinctive properties and classification of most-known metamaterial. The principle of physical preconditions has been described together with their mechanical behaviour in the implementation of vision applications in the additive manufacturing industry. In addition, this paper presents an overview of the recent developments in the area of mechanical metamaterials.

Critical success factors of additive manufacturing for higher sustainable competitive advantage in supply chains

The manufacturing economy of a nation has to be competent and sustainable to develop technology, generate employment opportunities, serve customers with innovative products at competitive prices, provide best quality and services, and participate in global trade to boost the world economy. Adoption of new technology is important for firms to remain successful and achieve sustainable competitive advantage in the production and distribution of goods/services. The purpose of this study is to explore the critical success factors (CSFs) affecting the adoption of Additive Manufacturing (AM) in mainstream manufacturing in enhancing supply chain competitiveness and sustainability. As a methodological contribution, this research uses a mixed methods' approach. An Exploratory Factor Analysis (EFA) derives five CSFs: 'customized production with speed'; 'sustainable production'; 'flexible manufacturing system (FMS) for complex products'; 'standardization in AM industry'; and 'digital skills & infrastructure’. The Analytical Hierarchic Process (AHP) shows the weights of factors. TOPSIS (Technique for order of preference by similarity to ideal solution) ranks the four manufacturing enterprises based on EFA factors. Decision Making Trial and Evaluation Laboratory (DEMATEL) establishes the cause-effect relationship among factors. This study finds that 'customized production with speed’ is the most influential causal factor and ‘sustainable production’ the main effect factor. AM adoption helps industrial sectors in advancing with new technologies, advanced materials, and organizational structures so as to enhance the competitiveness and sustainability of manufacturing supply chains. It has a great potential to reconfigure supply chain (SC) structures in such a way that sustainable competitive advantage is achieved with less lead time and negligible adverse impact on the environment.

Localized keyhole pore prediction during laser powder bed fusion via multimodal process monitoring and X-ray radiography

Systematic fault detection and control during laser powder bed fusion (L-PBF) has been a long-standing objective for system manufacturers and researchers in the additive manufacturing (AM) industry. This manuscript investigates a data fusion approach for detection of keyhole porosity formation during laser irradiation of Ti-6Al-4V substrates by concurrent recording of thermally induced optical emission measured using both off-axis and coaxial photodiode sensors, and acoustic emission. Subsurface defect formation was monitored via high-speed synchrotron X-ray imaging at 20,000 frames per second, enabling temporal registration of keyhole pore formation events to the monitoring signals at a resolution of 50 µs. We developed data fusion machine learning (ML) models for localized prediction of keyhole pore formation at various time scales ranging from 0.5 ms to 2 ms. The signal segments were featurized using two independent approaches: (1) power spectral density (PSD) and (2) highly comparative time series analysis (HCTSA) framework. The extracted features from different sensor modalities were fused together to construct a multimodal feature space and sequential feature selection was used to determine the most informative features for training the ML models. The predictive performance was evaluated for three classifying algorithms: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Gaussian Naive Bayes (GNB). As a result, pore formation events were predicted with up to 0.95 F1-score, 1.0 recall and 0.94 accuracy. The most heavily weighted features indicate that model performance is chiefly governed by the acoustic monitoring signal, with a secondary contribution from the optical emission sensors.

Policy-driven responsibility for innovations and organisational learning: an ethnographic study in additive manufacturing product innovations

PurposePolicy-oriented responsibility initiatives are institutional attempts to make innovations more responsible. One such initiative is offered by the European Commission’s responsible research and innovation (RRI) keys (public engagement, gender equality, science education, open access and ethics). This study is conducted in the context of an EU Horizon 2020 project and focuses on the introduction of RRI keys to innovation projects of the additive manufacturing (AM) industry. This study aims to understand how these RRI keys are perceived and adopted by industry project partners.Design/methodology/approachThe authors use an ethnographic study based on “participant observation”, supported by interviews and workshops with AM industry experts. In particular, the analysis covers two specific innovation use cases – one in the medical field, and second in the automotive field, in the context of the EU project. The analysis, based on ethnographic data, is inductive and interpretative.FindingsThe authors take a critical approach towards the implementation of RRI policy keys as measurable indicators, and argue that they are not easy tools to implement. The authors portray how RRI keys were understood and welcomed by industrial organisations, and how their implementation raised controversies. The authors also found that RRI keys are difficult to understand. They are not easy to measure and report, and this contrasts with earlier proposals of how RRI keys should be governed or implemented. The governance, meaning the dialogue between stakeholders both internal and external to the organisation, was time-consuming and required constant organisational learning.Originality/valueDue to the insightful ethnographic methodology, the authors could well underline the faults and difficulties of the application of policy-oriented responsibility in innovation. The findings illustrate the difficulty of implementing RRI in an industry that mainly operates business-to-business. This can help future policymakers to find more successful ways of pushing industry and innovators to be more responsible. It can also suggest better ways of reaching higher organisational learning for the purpose of more responsible innovations.

Business models for logistics service providers in industrial additive manufacturing supply chains

PurposeThis study identifies and characterizes configurations of generic business models for logistics service providers (LSPs) in the context of industrial additive manufacturing (AM). A literature-based framework of the AM service supply chain (SC) is developed to embed the generic configurations in their SC context.Design/methodology/approachFollowing an exploratory research design, 17 interviews were conducted with LSPs, LSPs' potential partners and customers for industrial AM services.FindingsSix generic configurations are identified, the LSP as a Manufacturer, Landlord, Logistician, Connector, Agent and Consultant. The authors outline how these configurations differ in the required locations, partners and targeted customer segments.Practical implicationsThe current discussion of reshoring and shorter, decentralized AM SCs confronts LSPs with novel challenges. This study offers guidance for managers of LSPs for designing business models for industrial AM and raises awareness for LSPs' resource and SC implications.Originality/valueThis study contributes to the scarce literature on AM business models for LSPs with in-depth empirical insights. Based on the six identified configurations, this study sets the ground for theorizing about the business models, in particular, the value creation, value proposition and mechanisms for value capture of the business models. In addition, this study suggests how the generic configurations fit the features of specific types of LSPs.