3D printing and blockchain: aeronautical manufacturing in the digital era

ABSTRACT

This research highlights the incorporation of 3D printing and blockchain technology into aeronautical manufacturing and certification procedures with an aim to increase operational efficiency. The integration of 3D printing allows for streamlined production, while the immutable features of blockchain provide traceability and certification advantages, thus offering the potential for transformative impacts within the aviation supply chain. This study primarily focuses on investigating the implications and multiple benefits of this integration into the aviation industry. Furthermore, given the absence of economic assessments priorly conducted on this integration, the study also includes a mathematical formulation that provides a quantifiable measure of the achievable return on investment. Two distinct case studies provide real data as evidence supporting the efficacy of this proposed integrated model. The outcome suggests a new paradigm for the supply chain ecosystem, leading to operational improvements and economic benefits.

KEYWORDS:

• 3D printing
• aeronautical manufacturing
• aeronautical maintenance
• distributed ledger technology
• blockchain

1. Background

Distributed Ledger Technologies (DLTs) are emerging technologies capable of making a disruption of traditional centralized technologies in multiple sectors of the economy (Bokolo, 2022), not only finance, but also in governance, environment, mobility, production industry, supply chain and aviation, among others (Hari et al., 2023; Lopes et al., 2023). DLTs such as blockchain are peer-to-peer networks that continuously share information between members. Each block of the chain connects to the previous one and has a unique hash value as identification and a timestamp. Because of that, the information cannot be tempered or erased, which helps the certifying entities access the data and verify its validity (Nguyen et al., 2022; Rana et al., 2023). DLTs and 3D printing are fast emerging technologies that are gaining significant importance in various areas (Klöckner et al., 2020). Individually, they are already being used in the aviation industry. However, if used together, it could help stakeholders to produce and replace damaged airplane parts faster, minimizing costs, aircrafts ground time and optimizing the aviation supply chain (Rodríguez & Monzón, 2023; Y. Wu et al., 2021). This altogether helps to maximize the economical factor for both airlines and manufacturers (Klöckner et al., 2020). In the aviation context, both DLTs and 3D printing, can revolutionise the industry by shortening the aircraft ground time, making aviation a non-stop operation and ensuring the civil aviation regulatory framework like the one from the European Union Aviation Safety Agency (EASA), which requires the issuance of certificates, the traceability and also the immutability.

During aircraft operations, the cabin is probably the most visible and more scrutinised area from the aircraft by the passengers. Despite that, most of the cabin material is not essential to conduct a safe flight, it is among the most damaged one. When a specific cabin part is damaged beyond repair, usually the aircraft can operate, but, in some situations, it can limit the seat where the damage is, making it unavailable to the customers. As the cabin interiors are highly customized in accordance with the operator’s standards, the manufacturers do not usually have a high availability of stock for customized parts. Consequently, the operators need to wait for those parts to be produced, which can lead to weeks or months of waiting, causing flight or passenger constraints such as blocked seats or seats without certain functions. To help mitigate this problem, it is proposed in this study to analyse the effectiveness of 3D printing the part and issue its certificate using a blockchain solution.

Following the context of the discussion on DLTs and their application in aviation, Zhang et al. (2019) underscores the potential of blockchain in cloud manufacturing, highlighting how it can decentralize and increase transparency in manufacturing services. This has a huge application in the aeronautical and aerospace manufacturing, specially in the context of aircraft cabin parts manufacturing, which are among the most subcontracted aircraft parts. Another relevant application, and perhaps one of the few that already has on-field applications is the Supply Chain. DLTs can increase transparency in the Supply Chain Quality Management significantly as it allows tracing, for example, all environmental conditions during the transportation. This is particular important, for example, for aircraft sealants and paints, because they required very strict environmental conditions for its transportation. The failure to comply with this can, for example, endanger the aircraft and its occupants since the sealants are vital to keep the aircraft pressurized, the aerodynamic performance and the aircraft drag decrease. J. Li et al. (2020) studied exactly how DLTs can be applied to the role of Supply Chain Quality Management, and how can be integrated with Industrial Internet of Things for automated processes. J. Li et al. (2020) perfectly illustrates how blockchain can enhance traceability and trust in the supply chain. This is particularly relevant in the context of aviation, where ensuring the quality and traceability of parts is critical, and suggests that blockchain’s attributes of decentralization, transparency, and traceability could be pivotal in revolutionizing supply chain management in aviation.

One parallel technology that DLTs enabled was the emergence of Non-Fungible Tokens (NFTs). According to Wu et al. (2023) NFTs represent assets that have undergone the process of tokenization through a blockchain. These tokens are generated as unique identification codes, derived from metadata through an encryption function. Subsequently, the tokens are recorded and preserved in the blockchain, whereas the actual assets are maintained in separate storage locations. It is the distinct linkage between the token and its corresponding asset that imbues NFTs with their uniqueness characteristic. The use of NFTs to represent physical assets it is not a new topic, Tommy et al. (2023) refers how a future management of critical assets can be managed and supervised via their tokenization and how they can help industries with high societal impact, like aviation, to comply with the heavy regulatory framework, enabling easier compliance procedures and processes, and enhancing assets management.

Regarding the future applications of DLT, it is expected that as soon as more reliable solutions appear, the adoption by the aerospace ecosystem will gain more traction. For example, blockchain can transform the aircraft Automatic Dependent Surveillance – Broadcast (ADS-B) communications into more secure channels (Pennapareddy & Natarajan, 2023), or even streamline the process of air traffic management (Lu et al., 2024). Since the core of the aviation industry is the reliability and immutability of their records, blockchain has also a role to play in this field (Kara et al., 2023).

DLT and blockchain can also merge with other technologies where the sum of the benefits is greater than their individual value. Combining blockchain and Artificial Intelligence (AI) can bring an entirely new technology called Distributed AI (DAI). DAI can bring auditing into AI decisions made, for example, in predictable maintenance or the origin and movement of aircraft parts (Abdulrahman et al., 2023).

In the near future, all airlines must report their greenhouse gas emissions as well as the source of the fuel used in their aircraft. Blockchain can bring means to audit the source of all the fuel, helping to create a more sustainable aviation business (Batoul et al., 2023).

Regarding 3D printing, the future of this technology is also bright. The massification of composite and metal 3D printing can change the supply chain in an unprecedented way (Prashar et al., 2022). From the printing of circuits to the development of 4D print with the integration of AI and Internet-of-things (IoT) will transform not only the aviation and aerospace industry, but also the housing and infrastructures building, the automotive and naval industries (Baigarina et al., 2023).

1.1. Objectives

To fulfill the generic aim of this study, which is to evaluate the potential for DLT technology and 3D printing in the aviation industry, the following research questions need to be addressed:

• Can blockchain be used to issue aeronautical certificates? If yes, what limitations and advantages does it have?

• What is the cost-benefit analysis of implementing a blockchain-based certification system for 3D printed components?

Building upon the research questions, the specific objectives of this study include:

1. correlate blockchain and 3D printing and its significant utilities in the aviation industry;

2. analyse the regulatory framework of the European Union Aviation Safety Agency regarding the production and replacement of materials, and the application of blockchain and 3D printing in aviation;

3. explore how blockchain, in conjunction with 3D printing, can be effectively applied within the sector;

4. assess the economic viability of implementing a blockchain solution for the certification of 3D printed components.

1.2. Novelty

This study innovates by integrating 3D printing with blockchain to enhance aviation supply chains, presenting a novel framework for companies to manufacture and certify 3D printed parts locally, while complying all the regulatory framework from aviation authorities and regulators. It fills a significant research gap by offering a mathematical model for aeronautical organizations to assess the profitability and ROI of adopting this technology. This dual approach simplifies supply chains, ensures regulatory compliance, and marks a significant advance in manufacturing and supply chain efficiency. The contribution of this research lies in providing a practical solution to streamline part production and certification recurring to 3D printing with certification over blockchain.

1.3. Organization of the paper

The paper is organized as follows: a literature review assessing the state of the art of 3D printing and blockchain is conducted in Section 2; the overall framework development and the proposed innovative solution together with the considered case studies to assess the research questions are described in Section 3; the obtained results are discussed in Section 4; and the main conclusions and future work directions are summarized in Section 5.

2. State of the art

2.1. Distributed ledger technology

DLTs has been one of the most prominent topics of the last decade, especially since the emergence of cryptocurrencies, in particular Bitcoin (Bencic et al., 2019). DLT, in general, can be defined as a distributed and decentralized database located across multiple locations and users, called nodes (Asante et al., 2023), organized in a peer-to-peer (P2P) network without centralised management and/or administrator, which enables secure and transparent transactions (Asante et al., 2023). Data security is granted through cryptography, being the system designed to establish trust among untrustworthy parties (Soltani et al., 2022). In addition to the use of data cryptography as a security measure, DLT also implements mechanisms that facilitate data immutability, transparency, censorship resistance, service availability, resiliency, distributed storage and computation (Nurgazina et al., 2021). Some of these features exhibit high relevance to the present study, being immutability and transparency the most important.

Immutability is a feature achieved by applying a consensus mechanism and a cryptographic hash function. In general, after the majority of the network’s nodes agree on validating a transaction, and before record it (Hunhevicz & Hall, 2020), a cryptographic hash and a timestamp value are added to the transaction data, granting the legitimate and non-repudiation of the record (Asante et al., 2023). As any modification is reflected on all other ledgers, registers cannot be edited or deleted (Santhi & Muthuswamy, 2022). Any change must result in a new entry in the ledger in order to maintain the immutability of the records (Rajasekaran et al., 2022), producing a tamper-proof history (Abdo et al., 2021; Wang et al., 2018).

Transparency, other key feature Gkogkos et al. (2023), refers to the property of allowing each participant to have a ledger copy (Golosova & Romanovs, 2018). By sharing the ledger among all involved members and having access to the same dataset Nikander et al. (2019), ensures that the registration of each transaction or changes made to data are visible to all relevant parties in a timely and accurate manner (Anthony, 2023). Transparency substitutes the need for trusted third-party mediators with an agreement of appropriateness that provides a transparent, immutable, and irreversible record (Asante et al., 2023), making auditing and inspection easier as any transaction can be traced in real-time (Anthony Jnr., 2023).

Other important aspect is DLTs governance. Depending on the network access level, DLTs can be categorized as private or public, both having as sub-levels: permissioned, permissionless and hybrid (Bokolo, 2022). A private DLT network is distinguished by its stringent control over node access to network (Santhi & Muthuswamy, 2022); one viable approach is the utilization of a public key infrastructure (Anthony, 2023). In a private network, access to participant nodes is accredited by a centralized authority (Santhi & Muthuswamy, 2022). Consequently, only authorized and trusted entities are permitted to engage in ledger activities (Chowdhury et al., 2019). This approach ensures the confidentiality of ledger data by selecting network participants meticulously, which proves advantageous in certain scenarios, for instance, industrial applications (Santhi & Muthuswamy, 2022). In contrast to the private access level, the public access level has less stringent control over network entry. It allows a fully open and decentralized network that extends access to all participants, enabling them to generate and validate transactions. Moreover, participants can alter the ledger state by storing and updating data through transactions (Chowdhury et al., 2019). This approach renders the ledger transparent and universal accessibility, with each node maintaining a replica of the ledger to ensure a high level of availability (Anthony, 2023).

Permissioned DLT networks exhibit restricted accessibility, permitting only authorized participants to engage (Erbguth & Morin, 2018). This controlled admission ensures that solely certified participants can enter (Chen, 2018), thereby enabling the management of data accessibility and the execution of tasks such as transaction validation and ledger maintenance (ITU, 2019). Identity management mechanisms are responsible for enforcing access control, typically identifying and verifying participants by some form of authentication (Bokolo, 2022). This approach ensures strong confidentiality, as participants retain elevated authority over data privacy and confidentiality, enabling them to enforce limitations on data visibility and availability (Chen, 2018). This mode of governance, encompasses a degree of centralization, where a entity or organization wields central control and decision-making authority over the network, being usually classified as centralized (Anthony, 2023).

In a permissionless DLT network, participation is unrestricted, therefore, no access control or limitations are in place (Tang et al., 2023). In practice, every individual can connect to the network without needing explicit authorization (Zia et al., 2020). The integrity of this open and decentralized network is collectively upheld through consensus mechanisms, ensuring ledger security and immutability (Anthony, 2023). Any entity has the opportunity to join, contribute to computational resources, and verify transactions (Bokolo, 2022). This implies the absence of a central governing authority; instead, decision-making is distributed among participants (Bokolo, 2022).

A Hybrid DLT network includes elements from the preceding models, striving to offer a harmonious equilibrium between openness and control via the provision of diverse access levels and governance structures (Kim et al., 2022). Within this framework, specific participants may enjoy complete network and data access, whereas others face limitations based on predefined regulations (Chen, 2018). Hybrid network governance frequently involves a consortium or coalition of trusted entities tasked with overseeing certain facets of the network, such as transaction validation and consensus mechanisms (Kim et al., 2022).

2.2. Blockchain

The origins of blockchain can be traced back to 1991 with the publish of the work done by Stuart Haber and W. Scott Stornetta about cryptography secured chains of blocks. One year later, ‘they incorporated Merkle trees into the design allowing several documents to be collected into a block’, but it was only in 2008 when this technology gained significant importance due to Satoshi Nakamoto’s publication of the Bitcoin white paper (Nakamoto, 2008). Blockchain as DLT has as most notorious characteristic the way of ledgers linkage store. Being like a chain of blocks containing data, in which each block has a unique hash value and a unique timestamp to keep the information secured. In this way, every change in the chain’s blocks does also provoke a change in its hash to prevent frauds from occurring (Nofer et al., 2017). Other significant characteristic of this chain is decentralisation as there is no physical database that can easily be erased. This occurs because the network is exclusively run by its members (Tijan et al., 2019). To add data, it must be sent in broadcast and shared to all network node members following a peer-to-peer (P2P) model, keeping each one a local copy of it (Chowdhury et al., 2019).

Blockchain technology can also reduce certificates tempering and ensure security and validity (Madala et al., 2019). According to Al-rahmi and Alkhalifah (2021), Blockchain is the main tool to facilitate this need and when combined with different hashing techniques, this becomes a powerful method for protecti

ng the data. This has the potential to be applied in many different areas and tasks requiring certification to operate. Blockchain immutability is ensured as once a block is added to the blockchain, its contents cannot be altered or deleted. This is achieved through the use of cryptographic hash functions capable of generating a unique identity (ID) of the data contained within each block. This ID is stored in the following block in the chain (Rajasekaran et al., 2022), creating a tamper-evident record of all transactions or data stored on the blockchain. Because of it, it is practically impossible to alter or delete data that has been stored on the blockchain. This makes blockchain technology particularly well-suited for applications where data integrity and security are paramount (Min, 2019). There are also no external intermediaries involved in validating the information, which increases trust in this network.

A main goal of blockchain is to achieve transparency, making everyone in the network equal as all members have access to the same data. Blockchain technology can also reduce certification forgeries and ensure security and validity (Madala et al., 2019). Transactions acceptation and/or validation depends on a consensus mechanism, requiring network-wide consensus from all validating nodes. Blockchain has a vast list of validation mechanisms, such as Proof-of-Work (PoW) (Nakamoto, 2008) and Proof-of-Stake (PoS), Delegated PoS, Round Robin, Proof-of-Authority/Proof-of-Identity (PoA/PoI) and Proof-of-Elapsed-Time (PoET) (Yaga et al., 2018).

The blockchain validation relays mostly on consensus mechanisms, being the most prominent ones PoW (Proof-of-Work) and PoS (Proof-of-Stake). PoW was proposed by Nakamoto in his most famous publication known as ‘Bitcoin White Paper’ (Nakamoto, 2008). In PoW validation mechanism, network nodes, known as miners, engage in a competitive process to incorporate new transaction blocks into the blockchain. This involves solving complex cryptographic puzzles, which serves the dual purpose of validating preceding transactions. As miners successfully complete these tasks, they are rewarded with transaction fees for their efforts (Guo & Yu, 2022). In the PoS mechanism, network nodes, known as validators, invest digital tokens directly into the blockchain itself, thereby increasing their chances of being selected to verify a block. The likelihood of being chosen is directly proportional to the validators’ investment, often referred to as their stake (Guo & Yu, 2022; Bokolo, 2022). This approach addresses the problem of inefficient resource consumption, which is a concern in certain other validation mechanisms (Bokolo, 2022).

NFTs has been a hot topic in blockchain. The use of NFTs to represent artistic assets has ignited a transformation in the art world, democratizing access to art ownership and distribution. Through blockchain technology, NFTs provide a verifiable, immutable record of ownership and provenance for digital artworks, thus addressing issues related to copyright and authenticity that have long plagued the digital art space (Tommy et al., 2023; L. Wu et al., 2023). Nevertheless, and despite all the buzz around the valorization of this type of assets, the applicability of NFTs to other type of use cases is still well undervalued. One field of application of NFTs is to perform the traceability of assets (Dietrich et al., 2023). Blockchain is an excellent tool to perform end-to-end traceability information, nevertheless, and because of the blockchain architecture, this is a very laborious and sometimes limited work. As referred in Dietrich et al. (2023), NFTs can provide an easier way to perform traceability as they can be programmed as unique and distinguishable within the network itself. This feature makes them easier to follow and trace during the entire life-cycle of any type of asset. Senay et al. (2022) uses the medical field to exemplify how NFTs can ease the traceability system and ownership management of medical devices, for the entire life cycle of the equipment. Their results shown that NFTs decrease traceability and ownership problems, leading to a simpler and lighter solution.

2.3. Regulatory guidelines

EASA already has study groups on blockchain and its applicability in aviation. The existing EASA regulatory provisions ‘do not represent a barrier to the adoption of blockchain technology, but rather an opportunity for the aviation industry to leverage the benefits of this technology. By complying with these regulations, the industry can ensure the authenticity and integrity of digital certificates and signatures, which are crucial for the safety and airworthiness of aircraft’ (EASA, 2023). Although in some regulatory documents the term blockchain may appear, it is not included in the regulation itself. EASA’s role consists in stating the criteria to be met, and then it is up to the users to decide what methods or technologies should be adopted to meet those requirements.

EASA’s Part 21.A.307 and Part M.A.802 emphasize the need for authorized release certificates to confirm conformity of aircraft parts. Tempered certificates are an increasing concern, making blockchain a promising solution to validate certificate and data authenticity. EASA’s Part M.A.603 addresses certification of manufacturing companies, crucial due to the market inundation of uncertified components resembling originals. The challenge of maintaining accurate and secure aircraft records is tackled in EASA’s Part M.A.305 and Part M.A.613, where blockchain stands out as a decentralized, tamper-proof solution. This is particularly significant as paper and isolated digital records are prone to damage, loss, or tampering. EASA’s Part 145.A.55 underscores the necessity of detailed maintenance records and certificate copies. The persistence of paper-based issuance remains problematic, potentially grounding aircraft due to document loss. Blockchain offers a resilient alternative to enhance accountability, security, and operational continuity.

2.4. Blockchain and 3d printing in the aviation industry

Many applications for blockchain technology in the aviation sector include payment, identity management, customs clearance, air traffic control and tracking (X. Li et al., 2021). Blockchain can help tracing the real-time position of the luggage during every phase (X. Li et al., 2021), digitize aircrew certificates (Ahmad et al., 2021), create a transparent and trustworthy view of an aircraft’s historical data and maintenance records, help document handling by reducing the paperwork while sharing reliable and real-time information between every part involved (X. Li et al., 2021) and implement a digital identity management system that reduces the chances of ticket, fraud and duplicate sales, which can improve the passenger experience (Ahmad et al., 2021).

Other ‘fast-emerging technology’ that has been gaining popularity in the aviation industry is 3D printing (Shahrubudin et al., 2019). 3D printing consists of producing three-dimensional items previously modelled in the computer. The printing device gets information from a computer as an input to print the model, and as it works without any help from the exterior, less waste is generated while being more effective (Sathish et al., 2018).

While 3D printing can be traced back to the 1980s, it has been used mainly for industrial projects and applications due to its high cost. Nowadays, it is getting more affordable and reachable (Sathish et al., 2018). Since this technology involves producing objects with many different shapes and geometries through printing them layer by layer, the printing can be stopped and resumed as needed. Also, the printing of several independent items can be done simultaneously, without human interference, becoming a very efficient way to manufacture the components needed, increasing production speed while keeping costs low. The most widely used manufacturing processes are classified into three main groups: formative, subtractive and additive. Formative manufacturing is mostly used for high volume productions of the same model, quickly ensuring production at a low cost per unit. Subtractive manufacturing is best suited for simple geometries and low-mid volume productions, usually made from metal. Additive manufacturing (AM) is appropriate for low volume productions and complex designs that the other types of manufacturing cannot achieve (Redwood et al., 2017). There are various methods of AM, depending on the target application (Shahrubudin et al., 2019).

Nowadays, 3D printing enables the production of various materials such as plastic, ceramic, graphene, and metal. Because of that, it has been proven feasible to reproduce complex items (Shahrubudin et al., 2019). Moreover, the current state of the aerospace spare parts industry includes challenges such as long lead times, low flexibility, and the difficulty of obtaining spare parts for older aircraft (Binoy et al., 2022). AM is very likely to revolution spare parts’ manufacturing and distribution process as it can offer customisation with many details with a lower cost and energy consumption (Gisario et al., 2019). Also, when compared to conventional manufacturing procedures, AM production line sustainability increases in a highly noticeable way, which has a positive impact on the environment (Binoy et al., 2022). Inventory management, which is a difficult task for many aircraft companies, can also be aided by AM (Binoy et al., 2022). As the same authors conclude, when the forecast for a traditionally manufactured spare component is lower than the actual need, AM could be utilized to fill the gap, thus reducing the risk of spare part shortages. In addition, producing spare parts on demand reduces the inventory holding costs and increases the spare parts availability (Thembani et al., 2019).

AM has been mostly used in the aviation industry to produce spare parts of components such as engines or landing gears. In the future, it is predicted that larger spare parts like airplane wings will be produced by AM technology (Binoy et al., 2022). For example, ‘in the Airbus A350 aircraft the engine pylons, most parts of the landing gears, some reinforced structural frames, and a great extent of brackets are made in Titanium’ (Gisario et al., 2019). The combination of blockchain and 3D printing could be a revolutionary solution for a wide range of industries including aviation, by linking the two into a value chain platform. The digital nature of 3D printing makes easier to incorporate blockchain technology as the 3D printing process is already largely digital, so connecting it to a digital ledger as blockchain is both practical and feasible (Klöckner et al., 2020). These technologies” emergence also allows the creation of new business models, which can vary from the creation of local 3D printing, secured shared factories, and design marketplaces (Klöckner et al., 2020). The combination of blockchain and 3D printing has proven beneficial for aviation companies, allowing them to print aircraft components as needed and inventory and logistics costs (Attaran & Gunasekaran, 2019). As the authors state, ‘Blockchain securely transfers the data to a verified 3D printer, enables authentication of the part, and helps technicians to ensure that it was not counterfeit before the installation into an aircraft’ .

2.5. Evolution of DTL and 3d printing

Far et al. (2023) affirms that blockchain has the potential to contribute to the next pivotal industrial revolution. The study delves into how blockchain’s offshoots – decentralized finance (DeFi) and the Metaverse – and how it can radically alter everyday life and digital business landscapes, leading to the next generation of customer support, Decentralized Finance (DeFi) 2.0 and Centralized-Decentralized Finance (CeDeFi). This can bring the next generation of customer support and customer feedback systems to the metaverse. It can also enable a more proficient and personalized customer support and provide a novel way where customers can share their experience. Airlines can create offer through the metaverse, virtual environments that allow customers to explore their intended destinations, services, and amenities with an unprecedented detail, while blockchain ensures these interactions are secure, transparent, and tailored to individual preferences. This synergy not only elevates the customer experience to new heights but also opens novel avenues for customer loyalty and engagement strategies, setting a new standard for the industry. The metaverve can also be used for a much more efficient training in the aviation industry. Aircraft maintenance technicians, pilots and Air traffic Controllers can also enhance their training resourcing to the metaverse and their immersive potential.

Regarding aircraft operations, the use of blockchain technology has the ability to secure the ADS-B aircraft signal. The spoofing of the ADS-B signal is a real threat, and it is today conscripted to military counter-operations, nevertheless, and regarding the civil operations, the hazard is present and if its not addressed, could eventually lead to a malicious actor takes advantage of the ADS-B weakness and conduct a spoof attach that could lead to an aircraft accident (Pennapareddy & Natarajan, 2023). Moreover, blockchain can bring a cutting-edge approach to safeguard ADS-B data through a tamper-proof, distributed public ledger that records authenticated flight plans. This system ensures the verification of an aircraft’s position and various parameters by matching them with flight paths/routes preserved in the blockchain ledger. By continuously monitoring ADS-B data transmissions against filed flight plans in real-time, this method offers a way to detect falsified aircraft messages and alert ground stations for the verification of such potentially malicious aircraft presence, marking a departure from existing solutions in the field. Lu et al. (2024) also debates the role that a blockchain infrastructure can play in tomorrow’s aviation Air Traffic Management (ATM). It is referred that the center core to ensure comprehensive 4A (Authentication, Accounting, Auditing and Authorization) security functionalities, which are crucial for ATM activities, the blockchain technology can play a significant role in it by providing the architecture which incorporates three key security modules: trusted authentication, data sharing, and access management. Batista et al. (2021) proposes using Autonomous Tethered Aerostat Airships not only as platforms for attaching sensors but also as bases for drone operations and blockchain nodes for a mesh data network. The suggested infrastructure is designed to support a variety of independent services offered by both private and public entities. The solution incorporates AI for the autonomous management of this infrastructure, enabling the AI system to operate independently of continuous cloud connectivity. This local AI system, fueled by data from on-site sensors, coordinates with other network AI services to execute complex tasks. Additionally, blockchain technology is utilized within this framework to ensure that decisions and operations are secure and auditable, with validations carried out across multiple ledgers.

Perhaps the biggest challenge to aviation since its start is to ensure the sustainability of the entire sector, which required by 2030 a drop of 55% of its greenhouse gas emissions, returning to the levels of 1990, and a complete netzero industry by 2050. Regulators are already providing a regulatory framework to comply with this schedule, and the aviation industry parties will need to report their fuel consumption in a verifiable manner, traced back to its source. For the next aircraft generation fuels, the Sustainable Aviation Fuels (SAF), the procedures are similar. Airline operators must report the origin of their SAF to prevent the use of SAF that can compete with human or animal food chains. When the introduction of hydrogen fuel (e-SAF), replacing totally today’s jet fuel and biological and synthetic SAF. The use of blockchain can ensure that the e-SAF has its origins from green sources from renewable energy, like green-hydrogen, ensuring the traceability of the e-Fuel during its entire lifecycle (Batoul et al., 2023).

Kara et al. (2023) state that another bright application for the DLT that soon can be brought to the aviation ecosystem floor is the implementation of blockchain to manage the industry records. The industry standards, provided by the aviation regulators, state that all records must be immutable, when they need to be changed, it must be ensured a traceability of amendments. It is also necessary to ensure the data back-to-birth to all aviation components as well as the reliability of those records (Earhart, 2020). The legacy method to guarantee all these procedures is ensured by paper records and by isolated data silos which are not connected to any other platform. Santos et al. (2022) also provides a working framework to use blockchain for aviation maintenance records, which besides ensuring the traceability, immutability, and back-to-birth of all data, it also ensures the data reliability, security and high-fidelity.

The integration of blockchain together with other technologies also poses a potential for future adoption. One of these examples is mentioned by Nair and Tyagi (2023), where the combination of blockchain with cloud computing, IoT, AI can play a crucial role in modern society, supporting a diverse array of applications from infrastructure management to social media platforms. It is also integrated with IoT and AI where it can enhance cloud computing systems in terms of performance. These types of applications can enhance the development of autonomous aircraft to reliable predictive maintenance, ensuring more profitable and reliable aircraft operations. Abdulrahman et al. (2023) introduces the concept of Distributed AI, where it is conducted thorough exploration of how blockchain and AI can be utilized within aerospace engineering, with a specific focus on improving supply chain management and enhancing operational efficiencies. Blockchain’s decentralized nature offers substantial improvements across various aspects of aircraft life cycle management, while AI has the potential to transform predictive modeling in supply chains and enhance the structural health monitoring and predictive maintenance.

Baigarina et al. (2023) provides a comprehensive analysis of the integration of AM in the construction industry, underscoring its key advantages and challenges. The study conducts a comparative examination to identify prominent technologies and future possibilities that have been harnessed in this field. With a surge in global research on the application of 3D printing in construction, it is discussed the integration of robotic systems to boost productivity. It is also discussed how can 3D printing be used not only in small composite parts, but also in large ones, and how this technology can also be used in custom circuit printing. These applications can lead to a huge positive impact on the aviation maintenance and manufacturing activities.

On the other hand, Prashar et al. (2022) mentions that 3D printing is revolutionizing the production landscape by enabling the customization of products with reduced development costs, shorter lead times, lower energy usage, and minimized material waste. Positioned as a cornerstone of Industry 4.0, AM and 3D printing are set to become a dominant technology across various sectors due to its technological maturity, its expansive capabilities, and substantial institutional support. The study also refers that 3D printing facilitates the creation of complex parts, helping companies reduce inventory, produce on-demand items, foster smaller localized manufacturing setups, and even streamline supply chains, being expected to generate 2 trillion US Dollars in components and end products by 2030. It is also worth mentioning that the nexus of 3D printing is the massification of metal 3D printing. This last feature can open the opportunity to the aerospace industry to locally print, upon necessity, almost all aircraft parts, turning almost unnecessary robust supply-chains.

Kantaros et al. (2023) discusses the transformative potential of 3D printing because it enables the creation of complex and finely detailed objects from a digital model using various materials, including plastics, metals, and ceramics. It is also mentioned that the development of 4D printing, which introduces the dimension of time, allows printing materials to change shape or properties autonomously. This innovation is gaining prominence in the era of Industry 4.0, which integrates advanced technologies such as AI, IoT, and robotics into manufacturing. Regarding the technological adoption by the industrial sector, these technologies are increasingly applied across diverse sectors such as aerospace, regenerative medicine, dental, and automotive, facilitating the production of intricate geometries and components that are difficult or impossible to achieve with traditional manufacturing methods. Additionally, 4D printing presents new possibilities in emerging fields where materials that can adapt and evolve over time offer significant advantages, opening innovative applications and solutions, and as well fully automated manufacturing plants.

3. Framework development

The proposed framework offers the aviation ecosystem a model upon which its procedures and evaluations are based. Initially, it outlines a systematic workflow or methodology through which organizations can effectively engage. Subsequently, it introduces a streamlined mathematical assessment designed to provide to the aviation sector ecosystem the necessary tools to ascertain the potential profitability of implementation within their specific business models and operational reality. Furthermore, this evaluation facilitates the determination of the timeline for achieving a return on investment (ROI), thereby enabling a precious strategic planning and decision-making.

3.1. Methodology

This study comprises two distinct components: a qualitative investigation and a quantitative analysis. The qualitative phase involves comprehensive document analysis and scrutiny of regulatory and technical frameworks. In the quantitative phase, a model is constructed to evaluate the implementation costs of 3D printing and blockchain solutions. Furthermore, the methodology will elucidate how the analysis corresponds with pertinent regulatory frameworks and will incorporate a quantitative evaluation of the ROI using a mathematical model tailored to operational contexts. This will be supported by two real case studies, bolstered by operational data obtained from two distinct airlines.

3.2. Aircraft supply chain

The aircraft supply chain structure is mainly composed by three parties: sellers, service providers and customers (Al-Banna et al., 2023). The main responsibility of this aviation sector is providing an operator with an aircraft that is fully serviceable at a reasonable cost, and of the highest possible quality (Ayeni et al., 2016). For this, OEMs (Original Equipment Manufacturers) are the components designers and, in most cases, also the sellers, while MROs (Maintenance Repair and Overhaul) ensure the aircraft’s maintenance during the periodic inspections (Singamneni et al., 2019).

The aircraft supply chain is composed by all the processes and businesses that make possible to produce parts in time to meet supply with demand (Ghadge et al., 2018). OEMs together with MROs and maintenance organizations allow the consumers (airlines) to maintain their fleet operational and adequately supplied. The efficiency of these chains relies heavily on both external factors such as transportation methods, as well as internal factors such as the availability of inventories to meet demand (Yash & Panfilov, 2017). According to the authors, these chains involve ‘vast quantities of exorbitant amount of flow information, product and money’ .

Aircraft components consist of many parts that need high-standard inspections, but demand is usually unpredictable, which can stretch inventory levels to their limits. Regular replacement is only needed for around 10% of the spare parts, while the remaining parts, referred to as ‘slow-moving parts’ or ‘Long-Term Storage parts,’ are hard to predict the exchange times and become expensive (Singamneni et al., 2019). At times, a spare part might not be in production anymore, making aftermarket service difficult and leading to inefficiencies in the supply chain systems of the aircraft operations industry. High inventory levels can reduce aircraft downtime, but the total financial investment becomes (Singamneni et al., 2019).

Commonly, airline operators have their own maintenance teams and warehouse operations in place, where they keep a supply of frequently replaced spare parts. Meanwhile, customers either keep a small inventory of infrequently replaced parts or purchase them from nearby MRO companies, manufacturers or distributors (Singamneni et al., 2019). The industry also faces supply chain risks related to competition for resources such as titanium and aluminium alloys, carbon fibers, and their composites. The traditional manufacturing methods used in the industry are also inefficient and wasteful, making it necessary to explore more advanced and alternative methods of manufacturing, such as AM (Singamneni et al., 2019).

3.3. Integration of DLT and 3D manufacturing

Regarding the supply chain reality presented above, an innovative approach is proposed, enhancing the development of 3D manufacturing and blockchain. Figure 1 represents the high-level model of the solution when adding the blockchain technology as an intermediate between the customer and the manufacturer, allowing to certificate the components produced and maintain them traceable and accessible for every part along the way.

Figure 1. Proposed model for a Blockchain-based solution for 3D printing in the aviation industry.

First, whenever a customer, usually a Maintenance Organization, needs a spare part, it would request it and pay the manufacturer. The request is made by the legacy procedures, which are still efficient. Regarding the payment, it can be made via a normal bank transfer or by a blockchain payment when this method is completely regulated and accepted by all involved entities. Following a comprehensive analysis, the manufacturer would record the pertinent information within a blockchain NFT. This NFT would contain a unique identifying hash, as well as the technical information (drawings and instructions) encrypted in order to avoid other network participants accessing this information. The manufacturer must send the NFT to the customer so that the technical information can be accessed and, finally, 3D printed. Subsequently, the component would go through quality control to ensure that the printed part is compliant with manufacturer standards provided by the manufacturer technical information. After this evaluation, the manufacturer would issue the part certificate (an EASA Form 1 in the EASA regulatory framework), containing also the quality report to a newly created NFT linked to the first one. With two NFTs created, the first representing the compliance with the manufacture procedures and the second establishing the certification of the compliance procedures, the regulatory framework can be assured, enabling from this point a traceable path for the entire life cycle of the 3D printed component. Each NFT includes a specific unique hash, with the second NFT having also the reference to the hash of the first one. With this procedure, the security and fidelity of data are assured. In this way, both customer and the manufacture can access and trace the component throughout its entire life cycle. Additionally, in the case that the aircraft or the part is sold or transferred, any future customer can have access to a detailed record of the component’s entire journey, including its origin, manufacturing data, and all the tests it went through. This helps sustaining maintenance and repair procedures as well as complying with the required accountability regulatory framework. This process is seamlessly described in Figure 2.

Figure 2. Interaction and Documentation System Flow of 3D Printing with Blockchain Validation.

3.4. Economic assessment

Implementing the solution previously proposed involves a variety of costs. In order to quantify them, they can be divided into two main groups: the costs related to the development of the blockchain structure and the costs regarding 3D printing technology. The costs related to the development of internal-use software include hardware installation, software purchase, testing, and expenses related to personnel, namely the remuneration and training (Tools, 2023). Furthermore, the direct costs related to 3D printing involve machine, material, and labour, this last including post-processing activities such as quality checks (PwC, 2018). Based on this, Equation (1) expresses the annual cost $C_d$ of producing $\mathit{x}$ 3D printed parts per year, considering the in-house development of a blockchain solution.

(1) $\mathit{Cd}\left(\mathit{x}\right)=\mathit{pI}{T}^r\mathit{IT}+\mathit{h}+\mathit{p}{T}^r\mathit{T}+\mathit{ne}+\mathit{\hspace{0.17em}}\mathit{fx}+\mathit{qe}+\mathit{pH}{R}^r\mathit{HR}+\mathit{t}+\mathit{u}+\mathit{c}$(1)

where $p_{\mathit{IT}}$ is the number of Information Technology (IT) employees allocated to this process, $r_{\mathit{IT}}$ is the average annual salary of the IT employees, $\mathit{h}$ is the annual hardware and software expenses related with the creation and maintenance of a blockchain infrastructure, $p_T$ is the number of tester employees allocated to this process, $r_T$ is the average annual salary of the tester employees, $\mathit{n}$ is the number of 3D printers required for the process, $\mathit{e}$ is the price of one 3D printer, $\mathit{f}$ is the price of one carbon fibre filament spool, $q_e$ is the price of quality control equipment, $p_{\mathit{HR}}$ is the number of Human Resources (HR) employees allocated to this process, $r_{\mathit{HR}}$ is the average annual salary of the HR employees, $\mathit{t}$ is the annual training expenses, $\mathit{u}$ is the annual expenses on software updates and bug fixes, and $\mathit{c}$ is the annual certification expenses.

Alternatively, to quantify the annual cost of the process when the blockchain solution is not developed in-house, the expenses related to software development in Equation (1) should be replaced by the actual cost of contracting it. In this way, Equation (1) is reduced to Equation (2).

(2) $C_c\left(\mathit{x}\right)=\mathit{b}+\mathit{ne}+\mathit{fx}+q_e+\mathit{pH}{R}^r\mathit{HR}+\mathit{t}+\mathit{c}$(2)

Therefore, $C_c$ represents the annual cost of the process considering that an external company is hired to develop the solution, and $\mathit{b}$ as the annual cost of hiring a blockchain development company. By implementing this process, some costs related to the traditional supply chain method will no longer be applicable. In this way, the annual ROI can be quantified by adding the costs related to warehouse (personnel, stocking), operational (cost of selling one seat), supply chain (transporting), data security, and price difference between the original part and the printed one. This is expressed in Equation (3),

(3) $\mathit{G}\left(\mathit{x}\right)=\mathit{p}{L}^r\mathit{L}+\mathit{g}\mathit{\hspace{0.17em}}\mathit{x}+\mathit{iax}+\mathit{kx}+\mathit{ox}+\left(1-0,95\right)\mathit{ox}$(3)

where $\mathit{G}(\mathit{x})$ represents the annual ROI of this process when compared with the traditional way of acquiring the parts, $\mathit{x}$ is the number of 3D printed parts per year, $p_L$ is the number of warehouse employees, $r_L$ is the average annual salary of the warehouse employees, $\mathit{g}$ is the cost of storing one spare part, $\mathit{i}$ is the annual number of seats not sold due to broken parts, $\mathit{a}$ is the annual revenue per seat, and $\mathit{k}$ is the supply chain cost of one spare part. The cost of securing data was considered to be the same as the price of the original spare part, represented by $\mathit{o}$. When comparing 3D printing with traditional manufacturing methods, the cost of the 3D printed part is much lower as the only direct costs are the material and energy, being this last one negligible since cabin parts are relatively smaller and faster to print. In the context of this study, it is hypothesized that 3D printing could offer a cost advantage of up to 95% over traditional manufacturing methods. This assumption is based on the notion that the only direct costs involved in 3D printing are the material and energy, the latter of which is generally negligible for smaller and faster-to-print cabin parts.

3.5. Case studies

First, one general case will be presented based on the value of the printable component. Since the price of a cabin part can vary, three specific cases will be conducted considering Component Value (CV [€]) = 200, 400 and 600. Afterwards, the general case will be applied into a specific case study regarding KLM Royal Dutch Airlines reality. For every case, the employees’ salaries per year were calculated considering 14 months and were estimated based on data from job search platforms. The cost estimates for software, quality control equipment, training, and engagement of a blockchain development were derived from industry-standard data and practices provided under non-disclosing agreement.

The 3D printer chosen for this process was Modix BIG-60 V4 since it has important characteristics such as dual extrusion composites for advanced geometries and strong, heat-resistant metal replacement parts. These optimal print conditions allow the parts produced to be assembled in an aircraft. Regarding the material, carbon fibre was found to be the most appropriate for 3D printing aircraft cabin parts due to its resistance and strength. Because of these characteristics, this material is widely used in the aviation industry (Shahrubudin et al., 2019). In Europe, one carbon fibre filament spool of 500 g costs around 40€

In accordance with Regulation (EU) 2019/2153, which outlines the fees and charges established by EASA, the approval fee for Product Organisation Approval is set at an annual certification expense of 20,650€. This fee is determined based on the relatively small scale of this process, given that the number of printed parts falls within this category. An overview of the variable values used for the calculation of $C_d(\mathit{x})$ for the (a) general case can be found in Table 1:

Table 1. Variable values applied in $C_d(\mathit{x})$ and $C_c(\mathit{x})$ for (a) General Case and (b) KLM.

Variable	(a) general case	(b) KLM	Description
${\mathit{p}}_{\mathit{IT}}$	3	3	number of IT employees
${\mathit{r}}_{\mathit{IT}}$	42.000	50.000	annual salary of one IT employee
h	1.000.000	1.000.000	cost of blockchain hardware/software
${\mathit{p}}_T$	1	1	number of tester employees
${\mathit{r}}_{\mathit{T}}$	22.000	40.000	annual salary of one tester employee
n	1	2	number of 3D printers
e	5.000	5.000	price of a 3D printer
f	40	40	price of a carbon fiber filament spool
${\mathit{q}}_{\mathit{e}}$	50	50	price of control quality equipment
${\mathit{p}}_{\mathit{HR}}$	1	1	number of HR employees
${\mathit{r}}_{\mathit{HR}}$	20.000	40.000	annual salary of one HR employee
t	1.000	1.000	annual training expenses
u	1.000	1.000	annual cost of software updates
c	20.650	20.650	annual certification expenses
b	50.000	50.000	annual cost of blockchain hiring

Considering these values and a CV = 200, the annual costs of the process as developing the blockchain solution can be determined by Equation (4),

(4) $\begin{array}{rl}& C_d\left(\mathit{x}\right)\mathit{\hspace{0.17em}}=\mathit{\hspace{0.17em}}630\mathit{CV}\mathit{\hspace{0.17em}}+\mathit{\hspace{0.17em}}5000\mathit{CV}\mathit{\hspace{0.17em}}+\mathit{\hspace{0.17em}}110\mathit{CV}\mathit{\hspace{0.17em}}+\mathit{\hspace{0.17em}}25\mathit{CV}\\ & +\mathit{\hspace{0.17em}}0,2\mathit{CVx}+\mathit{\hspace{0.17em}}0,25\mathit{CV}\mathit{\hspace{0.17em}}+\mathit{\hspace{0.17em}}100\mathit{CV}\mathit{\hspace{0.17em}}+\mathit{\hspace{0.17em}}5\mathit{CV}\mathit{\hspace{0.17em}}+\mathit{\hspace{0.17em}}5\mathit{CV}\mathit{\hspace{0.17em}}+\mathit{\hspace{0.17em}}103,25\mathit{CV}\end{array}$(4)

which simplifies to Equation (5)

(5) $C_d\left(\mathit{x}\right)=5978,5\mathit{CV}+0,2\mathit{CVx}$(5)

To determine the value of $C_c(\mathit{x})$ under the general case the same values were used for the variables that were already in $C_d(\mathit{x})$, except for the cost of hiring a blockchain development company (see Table 1).

Considering these values and CV = 200, the annual costs of the process as contracting the blockchain solution can be determined by Equation (6),

(6) $C_c\left(\mathit{x}\right)=250\mathit{CV}+25\mathit{CV}+0,2\mathit{CVx}+0,25\mathit{CV}+100\mathit{CV}+5\mathit{CV}+103,25\mathit{CV}$(6)

which simplifies to Equation (7)

(7) $C_c\left(\mathit{x}\right)=483,5\mathit{CV}+0,2\mathit{CVx}$(7)

In order to determine the value of $\mathit{G}(\mathit{x})$, the cost of storing one spare part is estimated to be equal or superior to 25 percent of the cost of that part (Azzi et al., 2014). In this case, an average price of 800€ was considered for cabin spare parts. This estimation takes into account the varying costs of different parts, which can range from hundreds to thousands of euros. That said, 200€ were assumed as an average cost of stock, per year, of one cabin spare part. The value of 1 disruption occurrence in the fleet represented by $\mathit{ia}$ was considered to be 3.763€. This value was obtained by firstly multiplying the number of inoperable seats per year in the fleet (7) with the time it usually takes to get the seat back in operation (7 days), the average number of an aircraft’s daily flights (4) and the annual revenue per seat (134,40€). Then, the value obtained which represents the annual gains due to disruptions was divided by the number of occurrences in the fleet in that same time (in this case, 7). This data was provided by SATA Internacional – Azores Airlines. The supply chain cost is estimated to be 10 percent of the cost of the spare part in question (Rodrigue, 2020). Considering the same average price of 800€ of a cabin spare part, a supply chain cost of around 100€ was taken into account in this case. An overview of the variable values used for the calculation of $\mathit{G}(\mathit{x})$ under the general case can be found in Table 2:

Table 2. Variable values applied in $\mathit{G}(\mathit{x})$ for the (a) general case and (b) KLM.

Variable	(a) general case	(b) KLM	Description
${\mathit{p}}_{\mathit{L}}$	1	2	number of warehouse employees
${\mathit{r}}_{\mathit{L}}$	19.600	36.400	annual salary of one warehouse employee
g	200	200	cost of storing one spare part
$\mathit{ia}$	3.763	3.696	annual cost of $\mathit{i}$ unsold seats (broken parts)
k	100	100	supply chain cost of one spare part
o	-	342,38	cost of the original spare part

Considering these values and CV = 200, the annual ROI of the process can be determined by Equation (8),

(8) $\mathit{G}\left(\mathit{x}\right)=98\mathit{CV}+\mathit{CVx}+18,815\mathit{CVx}+0,5\mathit{CVx}+\mathit{CVx}+0,05\mathit{CVx}$(8)

which simplifies to Equation (9).

(9) $\mathit{G}\left(\mathit{x}\right)=98\mathit{CV}+21,365\mathit{CVx}$(9)

Table 3 presents the equations used in the general case by replacing the values of CV in the previous symplified equations of $C_d(\mathit{x})$, $C_c(\mathit{x})$ and $\mathit{G}(\mathit{x})$:

Table 3. Equations used in the general case.

	CV=200	CV=400	CV=600
${\mathrm{C}}_{\mathrm{d}}(\mathrm{x})$	1195700+40x	2391400+80x	3587100+120x
${\mathrm{C}}_{\mathrm{c}}(\mathrm{x})$	96700+40x	193400+80x	290100+120x
$\mathrm{G}(\mathrm{x})$	19600+4273x	39200+8546x	58800+12819x

For the KLM case, the values of the employees’ salaries were selected considering the airline framework and The Netherlands’ socio-economic environment. In addition, as the process is being applied to a bigger company, two 3D printers were taken into account. The other values were considered the same as in the general case.

To determine the value of $C_c(\mathit{x})$ for the first year of the process, the same values were used for the variables that were already in $C_d(\mathit{x})$ (see Table 1). Additionally, the cost of hiring a blockchain development company was estimated to be around 50.000€‚ per year, as for the general case.

This process focuses on small plastic parts from the cabin. Based on the information provided by SATA’s maintenance team, damaged meal tables are one of the most common plastic parts that causes seats to become inoperable. Sometimes, it takes up to 2 or 3 weeks to conclude its replacement and put the seats back into normal operation. As shown in Figure 3, the Recaro bi-fold seat table P/N 470-00-500-01 used in the economy class of the Boeing 777 fleet in KLM has a closing hook mechanism to firmly secure the table to the seat as well as a hinge mechanism for opening and shutting the table. If the table cannot comply with these requirements and, consequently, cannot be closed or properly attached to a seat, that specific seat gets blocked and cannot be sold. When the seat in question is on the aisle side, the entire row is blocked (Kamber, 2019).

Figure 3. Recaro bi-fold table (Kamber, 2019).

As KLM does not publicly disclose the exact number of unsold seats each year resulting from broken parts, an estimation was conducted by scaling SATA’s figures to match the size of KLM’s fleet, which is 21 times bigger than the former. That ratio was multiplied by the annual occurrences observed in SATA to estimate their equivalent for KLM. Therefore, the value of 1 disruption occurrence in the fleet represented by $\mathit{ia}$ was considered to be 3.696€. This value was obtained by firstly multiplying the number of inoperable seats per year in the fleet (147) with the time it usually takes to get the seat back in operation (7 days), the average number of an aircraft’s daily flights (6) and the annual revenue per seat (88,02€), estimated considering KLM’s 2022 annual report (Group, Air France KLM, 2023). Then, the value obtained which represents the annual gains due to disruptions was divided by the number of occurrences in the fleet in that same time (in this case, 147). The cost of the meal table amounted to 342,38€ $(\mathit{o})$ (Kamber, 2019). An overview of the variable values used for the calculation of $\mathit{G}(\mathit{x})$ can be found in Table 2.

4. Results and discussion

In this section, the results of the case studies previously described are presented and discussed. As the values on the y-axis cover a wide range of values, all graphics are shown in logarithmic scale to make the process outcomes more perceptible and clearer to visualize. The intersection points in the figures denote the process’ breakeven points.

Figure 4 presents the costs and ROI associated with the general case when considering an average component cost of 200 euros.

Figure 4. Costs and ROI per printed parts considering Component Value of 200€.

Figure 5. Presents the costs and ROI associated with the general case when considering an average component cost value of 400 euros.

Figure 5. Costs and ROI per printed parts considering Component Value of 400€.

Figure 6 presents the costs and ROI associated with the general case when considering an average component cost value of 600 euros.

Figure 6. Costs and ROI per printed parts considering Component Value of 600€.

In all three cases, there is a considerable discrepancy between the process cost considering the self-development of the structure in blockchain and the cost considering the external hiring of it. By opting to contract the blockchain structure, the process proves to be more cost-effective. This allows for a quicker breakeven point, which, for every case, occurs at 18.21 (19) printed parts. In contrast, when internally developing the blockchain structure, the breakeven point is only reached after producing 277.84 (278) printed parts regardless the component value. The main difference that exists in these cases resides in ROI, although the costs also increase with higher CV. For CV = 200€, the first breakeven occurs at 97,429€ and the other at 1,206,814€. For CV = 400€, the first breakeven occurs at 194,857€ and the other at 2,413,627€, and for CV = 600€, the first breakeven occurs at 292,286€ and the other one at 3,620,441€. These findings are logical since the costs and the ROI increase proportionally to the component’s value. Consequently, as the component becomes more expensive, both costs and ROI increase. Hence, due to that proportional increment, the quantity of printed parts necessary to reach the breakeven point remains consistent across all three scenarios.

For KLM, two scenarios were studied, one without considering inflation and the other considering an inflation rate of 3% every year. This value was chosen according to the average world inflation rate of the past 10 years by Macrotrends data. The inflation rate was applied in the annual salary of personnel and in costs such as hardware, software, carbon fibre material and the price of the original part. Furthermore, in the first scenario, the costs were considered constant, and the same as in the first year of the process. However, in the second scenario, those costs vary, taking into account that there are costs that are mostly significant when the process is being started, such as equipment costs. Hardware, software, and 3D printers are examples of initial costs that require a significant investment at the beginning of the process and then only require lower amounts to maintain.

Figure 7 presents the costs and ROI associated with the same process implemented at KLM Royal Dutch Airlines, disregarding the inflation rate. There is a large discrepancy between the process costs, although opting to contract the blockchain structure is still the most cost-effective option. The quicker breakeven point, in this case, can be reached at 10.17 (11) printed parts and costs 117,107€. In contrast, when internally developing the blockchain structure, the breakeven point is only reached after producing 275.68 (276) printed parts and incurring an expense of 1,273,727€.

Figure 7. KLM’s costs and ROI per printed parts, disregarding the inflation rate.

Figure 8 presents the costs and ROI associated with the process implemented at KLM considering an inflation rate of 3% every year. In this case, both cost projections show slight variations with the number of printed parts. Furthermore, the costs of contracting the blockchain structure and the costs of developing it internally are more similar. Contracting the blockchain solution continues to be the most profitable option for the process, with a quicker breakeven point that, in this case, can be reached at 9.98 (10) printed parts and costs 158,055€. In contrast, when internally developing the blockchain structure, the breakeven point is only reached after producing 52.98 (53) printed parts and incurring an expense of 306,467€.

Figure 8. KLM’s costs and ROI per printed parts, considering the inflation rate.

These results indicate that in a major airline as KLM, the breakeven point can be reached faster. Given that, in companies that may register around 150 occurrences per year, this process could be a viable solution for replacing damaged plastic cabin parts. At around 150 printed parts, the ROI would be approximately 10 times higher than at the beginning. Furthermore, the implementation of this process in KLM also offers relevant long-term benefits such as improved efficiency and reduced downtime. These advantages, along with the expected ROI, provide a strong rationale for pursuing the process in the larger airline. The innovative aspect of the process and its positive impact on KLM’s reputation, by ensuring that seats do not stay inoperative due to damaged cabin parts, can create a trust and reliable relation between the company and its passengers.

The results presented by these case studies prove the great potential that DLT technology combined with 3D printing have in the aviation industry. The transparency, security, and traceability of certificate issuance and verification ensured by blockchain can leverage aeronautical certificates to be digitally stored, authenticated, and accessed by whoever needs in a secure and efficient manner. On the other hand, using this technology to issue aeronautical certificates has some limitations, which include the expertise needed that may pose challenges for organizations unfamiliar with the technology. It may as well require cooperation and consensus among various regulatory entities and industry organizations. The existing EASA standards and regulations create an opportunity for implementation of DLT and blockchain solutions. In airworthiness and parts traceability management, the use and handling of digital data has yet no worldwide recognized standard, so it is important to study and provide a worldwide accepted standard in this matter. Comparing to the traditional processes, the use of 3D parts printing and blockchain has proven to be a promising solution for larger airlines, where they can mitigate supply chain issues and maximize economical turnover.

The theoretical implications of integrating of both blockchain and 3D printing technologies in the aviation ecosystem primarily revolve around advancing the understanding of DLT within complex supply chain systems. This study contributes to theoretical knowledge by demonstrating how blockchain can be effectively applied beyond its traditional applications to manage and secure critical manufacturing data and logistics. It extends existing theories on supply chain transparency and efficiency by showcasing a practical application that mitigates risks associated with part authenticity, supply delays, and regulatory compliance. Moreover, the study enriches the theoretical discussion on the integration of emerging technologies in established industries. By leveraging blockchain for certification and traceability, the study offers a new theoretical framework for understanding how technology can enhance operational efficiency and strategic decision-making within highly regulated industries. Regarding the practical implications, this work provides, step-by-step, a clarification on how the current civil aviation regulatory framework provides the opportunity to implement both technologies to overcome the supply chain inefficiencies. Another useful aspect that this study provides is a comprehensive mathematical modulation for the industry to evaluate how much economical resources can be saved by opting for this process and their ROI.

5. Conclusions

This paper has proposed a framework for the integration of 3D printing and blockchain to facilitate the production and certification of aerospace parts, contributing for efficient production and the immutable traceability of the process. In addition, a mathematical formulation has been established to quantify the return on investment that can be obtained using the proposed solution. The proposed process proved to be a good solution for large airlines with the potential to invest, where, in just one year, it is expected that the ROI will exceed the costs incurred. In this situation, the whole process of replacing cabin parts would be revolutionized, positively impacting on the departments involved. The proposed methodology is efficient in order to overcome supply chain inefficiencies in the time to replace parts that can be 3D printed. The presented case studies demonstrated that it profitable for organizations that decide to invest in the merge of this two type of technologies.

The fact that the existing EASA standards and regulations provide an opportunity for the implementation of DLT solutions makes the proposed process viable among the industry. The findings emphasize the importance of further research and development, collaboration among industry organizations and regulatory authorities, and the need for wider acceptance and adoption of these technologies in order to ease aviation supply chain.

The limitation of this study was that in the reviewed case studies, the focus was only on meal tables, therefore, the full potential gains that the aviation ecosystem can obtain are underestimated. Nevertheless, the proposed mathematical formulation its not limited to meal tables. Another limitation, and despite being presented as a frontier of innovation, new technologies often encounter a slow adoption curve within the aeronautical ecosystem. The use of 3D printing technology by the end users is often still met with some apprehension, who may perceive it as a potential vector that could compromise an independent quality control. Regarding the integration of blockchain, a constraint may be its somewhat tarnished reputation. This negative perception could engender skepticism, thereby inhibiting its adoption for legitimate and transformative applications within various industries.

Concerning future developments and possible applications, and to fully quantify the economic benefits that the integration of blockchain and 3D printing may offer, it would be beneficial to expand the scope of study beyond merely meal tables to encompass a broader range of small parts that are suitable for 3D printing such as armrests or window shades. Additionally, broadening the study’s application, it is important to note that the aviation ecosystem’s regulatory framework places significant emphasis on data maintenance, traceability, and reliability. Therefore, including the proposed solution to ensure data immutability and traceability, not only during manufacturing but also throughout maintenance and operational activities, becomes essential in order to fully adopt blockchain technology in the aviation ecosystem.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The research presented here was conducted at the Aeronautics and Astronautics Research Center (AEROG) of the Laboratório Associado em Energia, Transportes e Aeroespacial (LAETA), supported by projects with the DOIs 10.54499/UIDB/50022/2020 and 10.54499/UIDP/50022/2020. Additional support was provided by the Fundação para a Ciência e Tecnologia, under the projects with DOIs 10.54499/LA/P/0079/2020 and 10.54499/UIDB/00319/2020.