Additive Manufacturing Service Research Articles

Additive manufacturing service bureau selection: A Bayesian network integrated framework

Additive manufacturing service bureaus (AMSBs) are crucial for enabling manufacturing organizations to leverage the benefits of additive manufacturing (AM) technology, such as on-demand manufacturing, production speed, etc., all while eliminating the expense of maintaining inventories. Consequently, many organizations favor AMSBs for expertise, cost efficiency, and access to diverse equipment, materials, and post-processing, reducing the necessity for substantial in-house investments. While researchers have explored evolving business models and the types of AM services offered by AMSBs to some extent, there is a noticeable research gap in selecting the most compatible AMSB for specific customer requirements, which this research would like to address. Initially, this research identifies various types of services offered by AMSBs, classifying them into eight groups: generative, evaluative, explorative, facilitative, constructive, decisive, selective, and assistive. Then, a knowledge-based expert system is introduced to select a suitable type of AM service. Further, 101 AMSB selection criteria are identified and grouped into criteria and sub-criteria by incorporating insights from literature and experts. Then, 26 pertinent criteria were shortlisted through Delphi. Neutrosophic best-worst method is then utilized to quantify criteria weights. Finally, a Bayesian network is used to calculate the selection probability of each AMSB, identifying the AMSB with the highest probability as the most compatible. The robustness of this framework is validated through sensitivity analysis. The practical effectiveness of the framework was demonstrated through a case study involving Ferro Oil-Tech India Private Limited. The analysis of the results provided valuable managerial insights and suggested ways to enhance the business competitiveness of the organization.

The state of the additive manufacturing ontology landscape for enabling rapid response in times of crisis

This paper examines the current landscape of ontologies for describing additive manufacturing (AM) technologies and services. Through an extensive search, using both a bottom-up and top-down approach, four accessible AM ontologies were identified. These ontologies were then evaluated according to generally accepted principles and guidelines. It was found, that two of the four ontologies could deal as a starting point for a semantic core of a B2B AM services matchmaking platform. To remedy the existing problems of impracticability, underdevelopment, and lacking linkage to other ontologies, we see a gap in regathering of AM data in a FAIR and sustainable way, and deploying a re-engineered version of a candidate ontology.

Improving Additive Manufacturing production planning: A sub-second pixel-based packing algorithm

Additive Manufacturing (AM), the technology of rapid prototyping directly from 3D digital models, has made a significant impact on both academia and industry. When facing the growing demand of AM services, AM production planning (AMPP) plays a vital role in reducing makespan and costs for AM service companies. This research focuses on the AMPP problem under the unrelated machine environment and two-dimensional irregular packing constraints such that the makespan can be minimized. Since there is no promising sub-second algorithm for solving the 2D irregular packing sub-problems in current literature, the efficiency of checking the packing feasibility of batches is the bottleneck of algorithms of AMPP. Therefore, we propose an efficient pixel-based AM packing algorithm (PAMPA) which can tackle irregular packing sub-problems that allow hole filling and free rotation. In PAMPA, parts are placed one by one in a Bottom-Left (BL) way and the rotation angle is determined by a novel angle selection heuristic. A new codification method and contour-guided method are proposed to accelerate separating overlap. The placement sequence is optimized by a biased random-key genetic algorithm (BRKGA). Furthermore, by adopting PAMPA, we introduce a variable neighbourhood search (VNS) framework to solve the AMPP. Finally, new instances are generated to conduct experiments. Based on the new instances and instances from (ESICUP), the efficiency of PAMPA is analysed and verified. The proposed VNS framework can reduce the makespan by 8 h on average compared with the initial solution, which illustrates that our PAMPA is beneficial for AMPP. Some interesting insights are also revealed and discussed based on the case study.

Additive manufacturing–enabled innovation in small- and medium-sized enterprises: the role of readiness in make-or-buy decisions

PurposeThe digital transition process is an important strategic initiative for manufacturing companies to ensure continued competitiveness. The purpose is to investigate the relationship between firms' additive manufacturing (AM) readiness and product and process innovation and how this process is mediated by firms' make-or-buy decisions regarding performing AM processes internally or buying AM services from external partners.Design/methodology/approachThe paper is based on a questionnaire survey including full answers from 157 small- and medium-sized manufacturing companies.FindingsResults show a positive relationship between AM readiness and both product and process innovation. Results also reveal that firms with higher readiness invest more in in-house AM, which in turn promotes innovation. There was no significant association between AM readiness and the use of external AM services. Nonetheless, buying external AM services is still associated positively with innovation.Research limitations/implicationsData in the questionnaire survey are provided by single respondents from each company and are only based on Danish respondents.Practical implicationsThe results indicate that firms' product and process innovation benefits from higher AM readiness derive from increased investment in in-house AM rather than from increased use of external AM services. This also signifies that firms with lower levels of AM readiness buy external AM services and derive the innovation benefits hereof.Originality/valueThe paper delivers new, empirically found knowledge about how small- and medium-sized manufacturing can improve innovation by both making and buying AM services.

Unpacking Additive Manufacturing Challenges and Opportunities in Moving towards Sustainability: An Exploratory Study

The global market for Additive Manufacturing (AM) is expected to grow, which may increase the prominence of sustainability aspects in the manufacturing process. A growing number of AM academics and practitioners have started to pay attention to the environmental and societal impacts of AM instead of only focusing on its economic aspect. Yet, AM is still not widely adopted, and the research on AM sustainability is still at the nascent stage. This paper aims to better understand AM’s sustainable adoption and seeks to address three questions: what the sustainability implications of AM are; what challenges may prevent the broad adoption of AM; and what opportunities can enable AM sustainability. The research adopts a multiple case study method to investigate six AM companies that play different roles in the AM ecosystem, including AM design, AM machine, AM material, AM service, AM education, and AM consulting. The results from these studies reveal that AM has the potential to reduce environmental and social impacts; however, it might also cause negative consequences and lead to some rebound effects. We identified 43 categories (synthesized from 199 examples) of key challenges for AM adoption and proposed 55 key solutions in moving AM towards sustainability. It is evident that AM acts as a promising digital technology for manufacturing and has the potential to pave the way for a new era of sustainable manufacturing.

Additive Manufacturing Service Provider Selection Using a Neutrosophic Best Worst Method

Additive manufacturing (AM) is poised to disrupt the manufacturing industry due to its distinctive (layer-by-layer) approach to part production, as AM comprises a range of fabrication techniques that facilitate the production of customized and highly complex parts. For a particular part selecting the most suitable AM machine and material is an indispensable decision due to several AM machines, their process and material constraints like size, accuracy, mechanical properties etc. This paper demonstrates the multi-criteria framework using Delphi and neutrosophic best-worst method to determine the best suitable AM machine and feasible material from the spool of databases. The Delphi method is employed to determine and validate essential criteria for a selected part, which results in shortlisting the 9 most relevant criteria. Further, the neutrosophic best-worst method is utilized to compute the criteria weights. A rating and normalization approach is deployed to calculate each AM machine and compatible material aggregate score to determine the optimal AM machine, material and ultimately, the AM service provider.

A resource sharing approach for PSS-enabled additive manufacturing platform

Distributed additive manufacturing (AM) has emerged as an innovative service-oriented paradigm for decentralized production of personalized products. In this paper, a resource sharing problem for product service system (PSS)- and cloud-enabled additive manufacturing platforms is addressed. A PSS-enabled business model is proposed to share resources. A deep reinforcement learning approach for resource sharing is then presented to have excellent potential based on a real-life case company that provides AM services for dental clinics and hospitals through producing 3D printed dental crowns and bridges, teeth and brackets. This case company is a national high-tech enterprise providing a 3D professional printing solutions in Nanjing, China. Using the datasets from the case company, the validation results show the feasibility and practicality of the proposed approach. • A PSS-enabled business model is proposed to facilitate resource sharing. • A deep reinforcement learning-based model is developed to share resources. • A real-life case is used to verify the practicability of the proposed method. • The proposed model is effective and applicable to the large-scale implementation.

How additive manufacturing drives business model change: The perspective of logistics service providers

Additive manufacturing (AM) is expected to facilitate local manufacturing in shorter, less complex supply chains and, thus, impact the demand for traditional logistics services. With increasing dissemination, AM confronts logistics service providers (LSPs) with the question of how they should adapt their business model to the threats and opportunities that come with the emerging digital technologies. We structure the AM activities of LSPs and develop a deep understanding of their resulting business model dynamics. For this exploratory purpose, this study develops a taxonomy and performs a cluster analysis to present six clusters of how LSPs approach AM today. The six profiles include LSPs that reactively monitor AM or, in contrast, proactively leverage AM for their internal operations and the development of new services for their external customers. Among them, four profiles entail fundamental changes to the traditional business models of LSPs. We find that these LSPs oftentimes continue to rely on their traditional “analog” service strengths to offer integrated service bundles of AM and logistics solutions. They bridge their lack of specific resources by strategic alliances with AM experts. Only a few LSPs have started severing ties to their traditional businesses to develop digitally dominated, platform-based AM services that require different resources. Overall, the comprehensive picture of AM activities enables us to contribute to the knowledge of how LSPs navigate in the digital age and to the nexus of business model dynamics and emerging technologies. We propose a set of propositions and support practitioners in analyzing and designing AM activities.

Assessing industrial barriers of additively manufactured digital spare part implementation in the machine-building industry: a cross-organizational focus group interview study

PurposeAlthough additive manufacturing (AM) has been demonstrated to have significant potential in improving spare part delivery operations and has been adopted to a degree in the aviation and automotive industries, its use in spare part production is still limited in other fields due to a variety of implementation barriers. The purpose of this article is to assess the significance of previously reported barriers in the context of the machine-building industry.Design/methodology/approachAdoption barriers are identified from the literature and formulated as hypotheses, which are verified with a set of focus group interviews consisting of original equipment manufacturers (OEMs), AM service providers and quality inspection and insurance institutions. The results of the interviews are reported qualitatively, and the transcripts of the interviews are subjected to quantitative content analysis.FindingsThe article identifies distrust in quality, insufficient material and design knowledge among stakeholders and poor availability of design documentation on spare parts as the key barriers of adopting AM in the production of spare parts. The three key barriers are interconnected and training engineers to be proficient in design and material issues as well as producing high-quality design documentation will yield the highest increase in AM implementation in spare parts.Originality/valueThe article offers a unique approach as it investigates the subjective views of a cross-organizational group of industrial actors involved in the machine-building industry. The article contributes to the theory of digital spare parts by verifying and rejecting presented barriers of AM implementation and how they are interconnected.

Additive Manufacturing for Localized Medical Parts Production: A Case Study.

Centralized supply chains (SCs) are prone to disruption, which makes them a risky choice for medical equipment production. Additive manufacturing (AM) allows for production localization and improvements in SC resilience. However, the comparative competitiveness of a localized SC from the time and cost perspective is still unclear. In this study, we investigate the competitiveness of localized medical part AM SCs against centralized ones by analyzing the responsiveness and cost of each SC. We utilize a real-world case study in which an AM service provider supplies medical parts to university medical centers in the Netherlands to construct six scenarios. We also develop a thorough empirical cost formulation for both central and local AM of patient-specific medical parts. The results of scenario analysis show that when utilizing the currently available AM technology, localized SC configurations significantly reduce the delivery time from about 54 to 27h, but at a 4.3-fold higher cost. Hence, we illustrate that the cost difference between the localized and centralized scenarios can be reduced when state-of-the-art AM machines are utilized, demand volumes increase, and the distances between the SC network nodes expand. Moreover, our scenario analysis confirms that the cost of the measures taken to prevent dust dispersion associated with powder-bed fusion AM has a major impact on the total cost of localized AM SCs for medical parts. The results of this study contribute to the understanding of the relevant factors in deciding whether central or localized SC configurations can be used in the AM production of medical parts. Furthermore, this study provides managerial insights for decision-makers at governments and hospitals as well as AM service providers and AM equipment manufacturers.

Simulation of utilization for LPBF manufacturing systems

Due to resolving major technological challenges Additive Manufacturing (AM) is on the brink of industrialization. In order to operate capital-intensive AM equipment in an economically viable manner, service providers must configure their production environment in a way which enables high capacity utilization and short throughput times while minimizing work in process. The interrelation of those three mentioned production-related key performance indicators, also known as the scheduling dilemma, must be addressed with due consideration of the technology’s characteristics. Within the framework of this paper the authors describe the impact of a service provider’s facility configuration regarding machine pool, operator availability and distribution of work content on the production system’s utilization. The evaluations rely on a simulation model developed in Matlab®, which allows for modification and execution of production schedules within AM facilities of different configurations. The validation of the proposed model is based on empirical data gathered on the shopfloor of GKN Additive, a global AM service provider.

ENVIRONMENTAL BENEFITS AND WEAKNESSES OF ADDITIVE MANUFACTURING PROCESSES IN THE BUSINESS SECTOR. KEY PLAYERS INFLUENCING THE ENVIRONMENTAL IMPACT OF DIGITAL MANUFACTURING TECHNOLOGIES

Industry 4.0 include additive manufacturing (AF) technologies. They offer the possibility of revolutionising a wide variety of production processes but potential environmental impacts caused have not yet been studied in depth. Academic literature, mainly based on laboratory studies, is limited and focuses on certain environmental aspects, considered relevant, such as energy consumption. The present research has approached the environmental implications of AF from a business perspective in an exploratory way, for which a multiple case study of AF equipment and service companies has been carried out. A drastic reduction of noise from AF operations has been observed with respect to traditional subtractive techniques and a better use of the material, a more flexible and personalized production, which allows reducing production costs and saving time in the design and manufacturing processes of products. However, depending on the raw material, especially important impacts are generated in the obtaining of such materials (metallic and/or plastic), during the manufacturing process by the relevant release of volatile organic compounds or at the end of life of products with completely new plastic compositions. These are created by means of the material injection technique and they compromise the recycling of the material. In addition, the average life span of these plastic products is shorter, which means more product replacement. Finally, it should be stressed that neither the companies participating in the study nor the experts consulted consider the increase in energy consumption to be a relevant environmental aspect. This varies depending on the FA technology used and the intensity of use of the manufacturing systems, which are always oriented towards short series.

Sustainable part consolidation model for customized products in closed-loop supply chain with additive manufacturing hub

Abstract In recent years, additive manufacturing (AM) has gained considerable interest because of its capacity to facilitate the fabrication of products of various shapes better than conventional manufacturing (CM). Moreover, it can respond to the growing demand for customized products and can reduce the impact on the environment by minimizing production waste. To maximize these benefits and overcome entry barriers, researchers have conducted part consolidation (PC) research to improve sustainability by reducing the number of components, or hybrid supply chain studies involving local AM service providers. This study aims to present a new supply chain that combines the advantages of both AM and CM systems to create and analyze the potential of an AM machine that can be made available for rent at a lower cost. In this study, a closed-loop supply chain with an AM hub (CLSCAM) is first designed. Thereafter, to support manufacturers in making the decision to adopt AM, two models are developed: sustainability (cost, environment, and time) evaluation model from the lifecycle perspective of CLSCAM, and a PC method is developed to maximize the three sustainability indices. Since the PC problem with complex product structure is an NP-hard problem, the genetic algorithm is employed as a solution method. Furthermore, an experimental analysis is conducted to validate the proposed model through a real-world application (testbed product). The results reveal that the AM hub and PC model can effectively improve the sustainability of the entire lifecycle. In particular, the results of improved sustainability per unit product in mass production scenarios demonstrate the practical applicability of the proposed model. These results also demonstrate that the pre-manufacturing stage has the greatest impact on the cost sustainability index in the CLSCAM. It is further confirmed that the sustainability improvement effect of PC increases with increasing AM raw material consumption efficiency, volume reduction rate of PC.

Challenges and opportunities to integrate the oldest and newest manufacturing processes: metal casting and additive manufacturing

Purpose This paper aims to investigate the current state, technological challenges, economic opportunities and future directions in the growing “indirect” hybrid manufacturing ecosystem, which integrates traditional metal casting with the production of tooling via additive manufacturing (AM) process including three-dimensional sand printing (3DSP) and printed wax patterns. Design/methodology/approach A survey was conducted among 100 participants from foundries and AM service providers across the USA to understand the current adoption of AM in metal casting as a function of engineering specifications, production demand, volume and cost metrics. In addition, current technological and logistical challenges that are encountered by the foundries are identified to gather insight into the future direction of this evolving supply chain. Findings One of the major findings from this study is that hard tooling costs (i.e. patterns/core boxes) are the greatest challenge in low volume production for foundries. Hence, AM and 3DSP offer the greatest cost-benefit for these low volume production runs as it does not require the need for hard tooling to produce much higher profit premium castings. It is evident that there are major opportunities for the casting supply chain to benefit from an advanced digital ecosystem that seamlessly integrates AM and 3DSP into foundry operations. The critical challenges for adoption of 3DSP in current foundry operations are categorized into as follows: capital cost of the equipment, which cannot be justified due to limited demand for 3DSP molds/cores by casting buyers, transportation of 3DSP molds and cores, access to 3DSP, limited knowledge of 3DSP, limitations in current design tools to integrate 3DSP design principles and long lead times to acquire 3DSP molds/cores. Practical implications Based on the findings of this study, indirect hybrid metal AM supply chains, i.e. 3DSP metal casting supply chains is proposed, as 3DSP replaces traditional mold-making in the sand casting process flow, no/limited additional costs and resources would be required for qualification and certification of the cast parts made from three-dimensional printed sand molds. Access to 3DSP resources can be addressed by establishing a robust 3DSP metal casting supply chain, which will also enable existing foundries to rapidly acquire new 3DSP-related knowledge. Originality/value This original survey from 100 small and medium enterprises including foundries and AM service providers suggests that establishing 3DSP hubs around original equipment manufacturers as a shared resource to produce molds and cores would be beneficial. This provides traditional foundries means to continue mass production of castings using existing hard tooling while integrating 3DSP for new complex low volume parts, replacement parts, legacy parts and prototyping.

The influence of additive manufacturing on early internationalization: considerations into potential avenues of IE research

Novel technologies are key enablers for early internationalization. Researchers and experts imply that industry 4.0 (i4.0) technologies will be exploited by existing firms and new ventures to develop radical business models and market revolutionizing products and services, leading to the disruption of global markets. Additive manufacturing (AM) is currently emerging as one of many disruptive i4.0 technologies. This paper provides the first stepping stone for research into the influence of AM on internationalization in the context of new venturing. Reflecting on current technological trends in the global industrial ecosystem, this paper proposes four themes of particular relevance in guiding future research into the influence of AM on new venture internationalization. Firstly, we indicate that research is needed into the emergence of expert AM service providers and the consequent lurking disruption of the current global value chain. Secondly, we suggest research focused on the role of AM in facilitating increased customer interaction and how this impacts networking, which is a key to early internationalization. Thirdly, we highlight the need for research that acknowledges the current challenges to established legislation and institutions to provide insights into how AM technologies influence international new ventures in the age of i4.0. Fourthly, we propose that research is needed into how AM technologies are used by INVs to overcome and capitalize on regional difference in developing products and services. The paper proceeds to conclude on and summarize these themes. Lastly, further less developed avenues of research are explored.

An overview of information technology standardization activities related to additive manufacturing

As Additive manufacturing (AM) technologies and connected machines are ever more widespread across most industries, there is a need for a better understanding about the standardization activities related to information technology (IT) within the scope of AM. The purpose of this paper is to provide an overview of existing standardization activities related to IT aspects of AM, including but not limited to data transfer and exchange. The paper covers the IT standardization activities in organizations, including ISO TC261, ASTM International, ISO TC 184, ISO/IEC JTC 1, Web3D Consortium, IEC committees, IEEE committees, 3MF Consortium and Khronos, which have the most active and ongoing projects. From this study, it can be observed that the manufacturing sector has generated the majority of standardization work, followed by industry R&D areas and the medical field. This paper also discusses two future IT standards for AM service platforms and for the medical industry.

Additive Manufacturing in Finland: Recommendations for a Renewed Innovation Policy

The objective of this research is to define an optimal innovation policy and funding strategy to improve Additive Manufacturing (AM) capabilities in Finnish companies. To do so, we present an international review of innovation programs in the area of AM. In addition, the study replied upon a survey prepared to evaluate factors for AM implementation. The ultimate goal is to help in the definition of a national policy strategy in the area of AM based on the characteristics of the Finnish industrial ecosystem.The methodology and data collection method involved defining the taxonomy of Finnish AM industry. The target group of the survey was a population of AM experts, and individuals with knowledge on AM and industrial processes. Overall, the survey revealed that research and innovation activities are well positioned in Finland. In order for future innovation policies to further support developments in the field, we estimated that policy strategies need to generate about 6-8 M€/year in national and EU- funding instruments for AM technology transfer, development, and innovation activities. Efforts should be targeted towards strengthening uses of AM in final production. In fact, only 36% of Finnish respondents declared to use AM for final production, while leading countries in AM use it in average more than 50%. Another area in need of development in Finland is the use of AM high performance materials. Moreover, outsourcing of AM services in Finland is 23 percentage point higher in national and 13 percentage point higher in international outsourcing to service bureaus and suppliers. In this regard, future policies and funding strategies should maintain the created momentum. However, there is a need to acquire high-end research and industrial equipment to stimulate AM integration to the existing production systems. This in the end can trigger the creation of new products, processes and intellectual property, enabling innovation and competitive advantage.

Additive manufacturing cost estimation for buy scenarios

Purpose To design for additive manufacturing (AM), the decision to use AM needs to be taken early in the product development process. Therefore, engineers need to be able to estimate AM part cost based on the few parameters available at this point in the process. This paper aims to develop suitable cost estimation models for this purpose, focusing on buy scenarios, as many companies choose to buy parts at service providers. Design/methodology/approach This study applies analogical cost estimation techniques to a data set of price quotations for laser sintering and laser melting parts. Findings The paper proposes easy-to-apply cost estimation models for laser sintering and laser melting for buy scenarios. Further, it generates new insights on the AM service provider market. Research limitations/implications The proposed models are only suitable for buy scenarios and are only a snapshot of cost achievable in 2014. Practical implications The proposed cost estimation models enable engineers to approximate AM part costs early in the product development process and thereby ease the decision to rapid manufacture certain parts. Originality/value This study addresses two gaps in the AM cost literature. It is the first study to take a qualitative approach to AM cost estimation, which is more suitable early in the product development process than the currently available quantitative studies. Further, it develops the first cost estimation for buy scenarios.

Additive manufacturing’s impact and future in the aviation industry

Additive manufacturing (AM) is a technology that has the potential to change the future of aviation supply chains and how the aviation industry designs, manufactures and repairs aircraft in the future. The purpose of this research was to shed light on the current and future states of AM in the aviation industry. From focus groups with nearly 50 aviation professionals from aircraft original equipment manufacturers (OEMs), suppliers, maintenance repair overhaul providers, and AM service providers and AM production firms, we found that the adoption of AM will be triggered by the accumulated AM experience in the industry, the stability of AM technology and the development of new aircraft generations. For AM to be finally adopted, aircraft OEMs must actively manage several success factors and monitor numerous control factors identified and discussed in this research. AM will supplement traditional manufacturing processes at least in the short to midterm, likely grow exponentially and offer benefits for players in the aviation supply chain.

Additive Manufacturing Cloud via Peer-Robot Collaboration

When building a 3D printing cloud manufacturing platform, self-sensing and collaboration on manufacturing resources present challenging problems. This paper proposes a peer-robot collaboration framework to deal with these issues. Each robot combines heterogeneous additive manufacturing hardware and software, acting as an intelligent agent. Through collaboration with other robots, it forms a dynamic and scalable integration manufacturing system. The entire distributed system is managed by rules that employ an internal rule engine, which supports rule conversion and conflict resolution. Two additive manufacturing service scenarios are designed to analyse the efficiency and scalability of the framework. Experiments show that the presented method performs well in tasks requiring large-scale access to resources and collaboration.