Modelling Applications of Additive Manufacturing in Defence Support Services

This is a contribution by Alessandro Busachi. For questions or discussions on his article, feel free to get in touch with him per e-mail.

“Modelling Applications of Additive Manufacturing in Defence Support Services”

Current technological developments in “Additive Manufacturing” (AM) have increased confidence in the disruptive potential of this technology. Leading Industrial Product-Service System’s (IPS2) are increasingly investing in R&D activities to better understand AM, its limitations and how to benefit today and in the future from its potential. AM capability acquisition may represent a source of competitive advantage and a means to develop enhanced service solution. AM is an emerging and promising technology which is an enabler of rapid, delocalised and flexible manufacturing. The main advantages of AM applications in “Defence Support Services” (DS2) are to provide platforms with the ability to sustain their systems, recover its capability after damage and collapse dramatically the supply chain.

This research provides increased understanding and fundamental evidence of Additive Manufacturing applications in the Defence Support Services (DS2) sector. Four novel and original scientific developments contribute to the body of knowledge in Systems Engineering and Through-Life Engineering:

  • The “System of Interest” (SoI) of a DS2 which defines its boundaries, links and elements
  • The creation of a conceptual framework for Additive Manufacturing assessment in DS2
  • The development of mathematical models for estimating the time and costs of Additive Manufacturing
  • The Decision Support System for Additive Manufacturing applications in DS2

The Decision Support System (DSS) is a software prototype engineered for “Research & Development” (R&D) units employed in early stages of “Capability Acquisition” (CA) programs. The targeted capability which is investigated for acquisition is defined as follow:

” the capability to additively manufacture critical-to-availability components next/close to the point of use only when they are required, to maximise Operational Availability and reduce cost and time of Defence Support Services (DS2)”.

The software tool includes four novel mathematical models on Selective Laser Melting (SLM), Wire+Arc Additive Manufacturing (WAAM), Fused Deposition Modelling (FDM) and on the Supply Chain of a DS2. The DSS performs accurate and detailed product and service cost estimation and can simulate current and next-generation practices where AM is delocalised in various stages of the support system (i.e. a DS2 provider, a vessel, a port and a forward base).

Figure 1 - Conceptual Framework

The Conceptual Framework of the DSS is presented in Figure 1. It consists of five phases through which the user must go through to perform an exhaustive assessment of AM applications in DS2. Phase 1 consists in providing the data input of the logistic platforms employed in delivering the spares, Phase 2 allows to define the distances between the stages of a support service system, Phase 3 consists in retrieving the AM product data, Phase 4 allows to define the System Configuration of the support service, Phase 5 allows to simulate the scenario and estimate key performance indicators such as availability, logistic delay and service cost.

Phase 1 and 2 are embedded in Module 1 which is outlined in Figure 2. Through this module the user must input the distances expressed as Km for each available logistic (Lo1 to Lo5).

Figure 2 - Logistics

Moreover, the user needs to populate the model with the financial data on each logistic platform: platform investment (£), platform maintenance (£/year), operating cost (£/year), time of utilization (years), rate of utilization (%), payload (kg) and the average speed of the platform (km/hrs).

The variables are fed into a mathematical equation which computes the hourly rate per kg for each platform. Once the variables have been loaded the module sends them as outputs to Module 3.

Module 2 represents the mathematical model of the AM technology, in Figure 3 an example of Wire+Arc Additive Manufacturing (WAAM) which is largely considered the most promising solution for large structural components in the maritime context.

Figure 3 - Additive Manufacturing

The user needs to input the product data, type of material (Aluminium, Titanium, Stainless Steel), deposition volume of both the model and substrate, the deposition area and the substrate thickness. Moreover, the Wire Feed Speed (WFS) and the wire diameter have been included as these are variable of the process which have major impact on performance data. The module allows to include the setup time and design time which in some situation may lead to high costs (i.e. in topology optimisation). Once the user fires the model, the results are displayed on the right side of the Graphical User Interface (GUI). These include a detailed Cost Breakdown Structure (CBS) with 7 cost elements and a set of performance data such as the cycle time, deposition rate and design time. Moreover, a small mathematical model of machining allows to outline the time and cost to shift from a Near-Net Shape deposition to a Net-Shape one without the typical waviness of WAAM processes. Once the variables have been loaded data on product weight and cycle time of the WAAM process are sent to Module 3.

Module 3 represents the simulation environment where the user can compare the current practices, where manufacturing occurs in the back-end of a DS2 and next generation practices where AM is delocalised in the front-end.

Figure 4 - Simulation

The user needs to select the System Configuration of both current and next generation practices and the location of the AM system. Moreover, data on Mean Time Between Failures (MTBF), Administrative Delay Time (ADT) and Procurement Delay Time (PDT) must be defined. In case of the next generation solution, the PDT is eliminated and substitute with the Cycle Time of the AM system. Once the selection has been made, the DSS performs automatically the calculations and provide the user with the following key performance indicators as outputs: Availability, Travel Times, Service Cost.

The following graphic gives an overview of all modules of the decision support system:



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