(MY)CAT4SLM© – A novel advanced SLM costing analysis tool for small and Medium-Sized Enterprises: Development, Implementation and Validation
- Open Access
- 12.12.2025
- ORIGINAL ARTICLE
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Abstract
1 Introduction
Selective laser melting (SLM) is one of the metal additive manufacturing (MAM) processes which is used to produce metallic components with intricate geometrical shapes. The SLM process fabricates metallic components with a lower buy-to-fly ratio as a result of its layer-by-layer process in producing near-net final components in contrast to conventional subtractive machining. During the SLM process, a direct laser beam is directed onto a powder bed to selectively melt the metal powder based on a sliced model derived from computer-aided design (CAD) data. The melted powder fuses with adjacent layers to form the desired component. A support structure is necessary to hold the component and enable heat transfer and a low-oxygen environment is required to minimise thermal stresses and prevent deformation [1]. SLM is seen as a future technology to facilitate the production of lightweight components in the aerospace sector for its geometric and manufacturing flexibility, enabling designers to fabricate intricate and customised parts in mixed batches, suitable for high-variety, low-volume production. Additionally, it allows the creation of components that are not feasible with conventional manufacturing technologies [2]. Freedom of design owned by the SLM process impacted the final component weight, lifecycle cost, material cost, energy consumption, post-processing and labour expenses, which could help manufacturers make decisions that would enhance the efficiency of this process.
Techniques employed in SLM offer significant advantages in terms of complexity and customisation. However, it is associated with drawbacks such as higher upfront equipment expenses than conventional subtractive manufacturing methods and often necessitates additional post-processing steps. To stay competitive, a developed AM cost model should accurately describe the overall cost likely related to the production phase of AM. The detailed calculations which account for various influenced parameters in the SLM process need to be considered and improved compared to the existing cost model to calculate the conventional manufacturing processes. The current method to get an overview of the costs of AM processes usually indicates the cost level using a cost-to-mass index such as €/kg, as stated in [3]. However, the cost of metal parts manufactured using the MAM process is relatively high compared to those conventionally fabricated components. The current state of research is still investigating the cost modelling that appropriately covers all economic aspects to extend the MAM technology in industrial applications by implementing various approaches [4, 5]. Various cost models have been developed in the existing literature to analyse the cost aspects of MAM processes. These models are utilised for various research objectives, such as evaluating the cost related to manufacturing diverse part geometries and different batch sizes [6], comparing the support cost using different overhang angles and support structures [7], exploring the opportunity for reducing the cost for different building volumes, comparing the cost of traditional CNC and metal-based AM [8] and applying topology optimisation with cost constraints. While these cost models provide useful insights into understanding the different cost components in metal-based AM, most of them are based on a constant selection of process parameters within one batch. In other words, they consider the values of process parameters to be constant during the fabrication.
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In analysing the costs of the SLM process, current studies showed that the most relevant factors are the machine cost, material cost, post-processing cost, preparation cost and building time cost. The most challenging factor is the task of estimating the building time since its influence on the printing cost, which can be considered a significant parameter in determining the final cost. Several cost methods have been developed in the past, however there is a lack of research in calculating the cost for single and serial production of SLM process. A cost model which investigates SLM costing is developed in which the breakdown of material, machine, labour, equipment, consumables and energy costs is considered [9]. Another study on the cost model for the SLM process highlighted five main cost parameters: material, machining, heat treatment, post-processing and human work. The material cost depends on the volume of the part and support structure, followed by the powder recovery rate and density [10]. An economic assessment of an activity-based cost estimation model to fabricate stainless steel parts using SLM reveals that the machine cost per part greatly influences the total cost per part, followed by post-processing [11]. Previous studies have directed their attention towards several key elements, namely the preparation of geometry data, the assembly of the build job, the configuration of the machine, the process of constructing the part, the extraction of the part from the SLM machine, the separation of the part from its substrate plate and the subsequent post-processing procedures [12]. In contrast to alternative methodologies, the present analysis framework does not incorporate energy consumption expenses as an independent input, as these considerations are encompassed within the overall machine costs.
Classification of cost models can be divided into three categories, depending on the purpose of cost utilisation and the perspective of the individual managing the cost model. The cost can be viewed from finance and accounting, management and manufacturing perspectives with different classification techniques, namely method-based, level-based and task-based [13]. The total lifestyle costs incurred within a supply chain were evaluated using a developed level-based cost model, which includes the design to cost reduction, remanufacturing and value engineering and lifecycle costing comparison between conventional and additive manufacturing [14]. An existing task-based cost model for the SLM process was improvised by considering a detailed cost breakdown of heat treatment and hot isostatic pressing on fabricated parts, revealing that build cost mainly affects the total manufacturing costs, followed by machining cost and material cost [15].
This paper proposed a developed time-informed and process-based costing model to estimate the total cost of production and the cost per part to fabricate a metallic component considering SLM machines available in Malaysia. The developed model considers seven key processes, which include the geometrical data of selected components, flow process, material, machine, process, operation and labour. Some data required for machine, process and labor were obtained from local company. An economy analysis is then performed by comparing the time and cost saving for single, batch and serial production.
2 Method development of costing tool procedure for SLM
The procedure to develop a costing tool consists of the detailed breakdown entities which are time, material cost, machine cost and process. The developed costing model is different from past researches by integrating a time-informed process-based approach to evaluate process continuity across multiple batches of printing. The major cost drivers identified in previous studies are compared with those in the developed model to highlight its innovation, as tabulated in Table 1.
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While the time factor is not directly involved with the costing tool, it accounts for factors that are influenced by costs over time. Detailed breakdown for each mentioned factor is included in the costing tool as shown in Fig. 1 with highlighted time and process information. The user input section is designed to be filled up based on the client’s requirements to accommodate the detailed specifications of the printed part. These specifications typically include the quantity of parts to be printed, powder material selection, SLM machine selection and post-processing requirements.
As the geometry of the component is obtained from the client, the CAD geometry will be interpreted by the costing tool in order to describe the dimension of the bounding box in which the length, width and height of the component will be used to estimate the required total volume of metal powder. Other than that, the developed costing tool allows clients to define the requirement of additional substrate during the process. The user can select additional substrates depending on the number of printed parts to maximise the smoothness of the whole production. However, the additional substrate plate provided by the company comes with an extra cost. In this section, the client will be notified earlier before making any decision.
Fig. 1
Framework of time-informed and process-based costing analysis model
The calculation of total production time necessitates a comprehensive evaluation of the duration required to complete various stages of the manufacturing process. This includes the pre-printing, actual printing, process continuity and post-printing procedures. Each stage encompasses specific tasks and time allocations that collectively determine the overall production timeline. An accurate assessment of these stages is critical for optimising production efficiency and ensuring precise scheduling in additive manufacturing workflows. In the pre-printing stages, the geometrical modelling of the component to be printed is prepared. This includes the preparation of a CAD model, determining the optimal positioning of the component on the base plate, generating support structures, slicing the model, simulating and analysing the manufacturing process and transferring the data to the SLM machine. The printing stage encompasses the actual layer-by-layer fabrication process, where laser scanning parameters are optimised to achieve printed components’ desired build quality and strength. This stage accounts for the majority of the total operating time as contributing factors like the build rate, recoater time and layer height are considered.
2.1 Main element of costing analysis tool: material cost
In the SLM process, the material cost can significantly impact the overall production cost of the final product. Therefore, the accurate estimation of material cost is essential to optimise the manufacturing process to ensure cost-effectiveness. The material cost estimation includes several factors, such as raw material cost, waste material cost and recycling cost. The raw material cost is determined by the cost of the metal powder used in the SLM process. This developed costing tool will discuss the details of the breakdown of material.
Support structures play a crucial role in the SLM process by providing stability and structural support to the components being manufactured. During the SLM process, the part is built layer by layer using the metal powder and the support structures are used to hold up overhanging sections and dissipate excess heat away from the printed component to the substrate plate, hence reducing the effect of thermal stresses on the deformation of the component. These support structures are added to the component during the design phase and are made up of the same material as the component itself. However, the addition of support structures increases the overall material usage and leads to an increase in the cost of production. Therefore, it is essential to include the cost of support structures in the overall costing analysis of the SLM process. In this developed costing tool, the total mass of support, \(\:{T}_{{mass}_{\left(S\right)}}\), is calculated from the area of the component, \(\:{A}_{\left(c\right)}\), minimum support height, \(\:{H}_{\left(s\right)}\), and the percentage of support structure coverage, \(\:{\varvec{{\rm\:P}}}_{{\varvec{p}\varvec{e}\varvec{r}\varvec{c}\varvec{e}\varvec{n}\varvec{t}\varvec{a}\varvec{g}\varvec{e}}_{\left(\varvec{S}\right)}}\) as shown in Eq. 1
$$\:{T}_{{mass}_{\left(S\right)}}=\:{A}_{\left(c\right)}\:*\:{H}_{\left(s\right)}\:*\:{{\rm\:P}}_{{percentage}_{\left(S\right)}}$$
(1)
The required material refers to the material that makes up the final product being manufactured. The required material cost includes the cost of the raw material used in the SLM process and the material loss that occurs during the process. The material loss can be due to the partially sintered powder during build and the excess powder that remains in the build chamber after the process is complete. In this model, the support structure will not be considered as a material loss since it is already defined under raw materials needed to complete the build.
The density of the powder used in the SLM process is also an important parameter to consider when calculating the component material cost. A higher density of powder will result in less material loss during the process, which can result in lower overall material costs. Additionally, machine build volume, which depends on the machine type, is included in the calculation. A bigger build volume allows more material to be occupied inside the machine chamber. The total mass of powder material used in production, \(\:{T}_{{mass}_{\left(M\right)}}\), can be calculated from the multiplication of number of builds, \(\:{N}_{build}\), component bounding box area, \(\:{A}_{\left(c\right)}\), component height, \(\:{H}_{\left(c\right)}\), and density of material, \(\:{\rho\:}_{\left(M\right)}\), added with total mass of support, \(\:{T}_{{mass}_{\left(S\right)}}\) as expressed in Eq. 2.
$$\:{T}_{{mass}_{\left(M\right)}}={N}_{{required}_{\left(C\right)}}*{A}_{\left(c\right)}*{H}_{\left(c\right)}*{\rho\:}_{\left(M\right)}+\:{T}_{{mass}_{\left(S\right)}}\:$$
(2)
Recycled material remains the leftover powder after the SLM process is complete. The powder is recycled and used in future builds as long as it meets certain specifications and has not been contaminated. The use of recycled material in the SLM process can help reduce material costs and waste, making it a more sustainable manufacturing option. In this developed costing tool, the cost of recycled material is taken into consideration along with other material costs. The calculation of the recycled material is related to the recycle fraction. The manufacturer can adjust the recycle fraction based on the product design. Equation 3 shows the calculation to determine the recycled material, \(\:{T}_{{mass}_{\left(RM\right)}}\) can be calculated from the product of the total mass of used material and recycle fraction, \(\:{P}_{recycle}\).
$$\:{T}_{{mass}_{\left(RM\right)}}=\:\left({T}_{{mass}_{\left(M\right)}}-\:{T}_{{mass}_{\left(S\right)}}-{T}_{{mass}_{\left(C\right)}}\right)*{P}_{recycle}$$
(3)
2.2 Main element of costing analysis tool: machine cost
In this developed costing tool, the machine’s section consists of the details of the SLM machine in the market. The most commonly used SLM machines were observed, such as SLM EOSM100, EOSM400-4 and Renishaw RenAM 500E. Each of the machines comprises its own specifications, such as build volume, laser speed, price of purchase, etc. These details of the SLM machine can be seen in Fig. 2, where all data were obtained from the brochure of the machine, which can be downloaded from the company’s website [16‐18].
Fig. 2
Details of SLM Machines with Price Illustrations
The investment cost of the machine is an essential factor that needs to be considered in the costing analysis of the SLM process. The investment cost of the machine will vary depending on the manufacturer, type of machine and its capabilities. Proper consideration of investment cost is important to determine the profitability and feasibility of using the SLM process for manufacturing components. In this developed costing tool, the investment cost, \(\:{C}_{investment}\) can be calculated by using Eq. 4, in which the total summation of the number of machines, \(\:{N}_{machine}\) and infrastructure cost, \(\:{C}_{infrastructure}\) times with the cost of the machine, \(\:{C}_{machine}\).
$$\:{C}_{investment}={C}_{machine}*\:\left({N}_{machine}+{C}_{infrastructure}\right)$$
(4)
Depreciation cost refers to the machine’s value reduction over time due to wear and tear, undesirability and other factors. In the SLM process, the machine used for producing parts is a significant investment and has a limited lifespan. Therefore, the depreciation cost must be considered when calculating the overall cost of producing a part using SLM. In this developed costing tool, the depreciation cost is to be annually estimated until the machine is fully paid by the company based on the mortgage contract. The annual depreciation cost, \(\:{C}_{depreciation/year}\) can be calculated using Eq. 5 when the interest rate, \(\:i\) and machine lifetime, \(\:{\eta\:}_{lifetime}\) were defined.
$$\:{C}_{depreciation/year}=\frac{i{C}_{upfront}}{{\left[1-\left(1+i\right)\right]}^{\left(-{\eta\:}_{lifetime}\right)}}$$
(5)
Maintenance cost is one of the components of the total production cost of SLM process. It involves the expenses associated with the regular maintenance and repair of the SLM machine and its components. These costs are incurred to ensure the machine’s smooth functioning and prevent any unexpected breakdowns that can result in downtime and production loss. Maintenance costs can include expenses related to the replacement of worn-out parts, routine inspections and repairs. Proper maintenance of the SLM machine is essential to ensure its longevity and optimal performance, which can directly impact the overall production cost. The annual maintenance cost, \(\:{C}_{maintenance/year}\) is the total product of machine cost, \(\:{C}_{machine}\) and maintenance percentage, \(\:{P}_{maintenance}\) as shown in Eq. 6. Maintenance percentage was set at 5% for each available machine in the costing tool.
$$\:{C}_{maintenance/year}=\:{C}_{machine}*{P}_{maintenance}$$
(6)
The amount of capital for the company to commit annual machine cost, based on yearly commitment for the SLM production is the total amount of annual depreciation and annual maintenance cost which can be written in Eq. 7.
$$\:{C}_{Machine/Year}=\:{C}_{depreciation/year}+{C}_{maintenance/year}$$
(7)
Qualitative insights from industry experts were incorporated through a structured questionnaire designed to gather information relevant to adapting the model for Malaysian context. It was distributed to local industry practitioners with relevant metal additive manufacturing experience. Feedbacks collected were used to validate early assumptions, refine parameters and ensure the developed costing model reflects local manufacturing and costing practices. Quantitative data analysis was conducted by comparing the figures provided by the industries to estimate total production costs and evaluate the accuracy and applicability of developed costing model in the local market context.
2.3 Main element of costing analysis tool: process cost
Process cost in the SLM process involves considering various parameters, including build preparation, additional consumables, process continuity and post-processing activities. By considering all these parameters in process cost, manufacturers can accurately assess the total cost of producing a component using SLM. This evaluation helps in determining the economic feasibility of utilising SLM as a manufacturing method and aids in making informed decisions regarding process optimisation, material selection and overall cost management.
Build preparation involves the activities and resources required to prepare the build job before it can be initiated on the SLM machine. Build preparation includes tasks such as part orientation, part position on the substrate plate and support structure generation. Part orientation involves determining the optimal orientation of the parts within the build chamber to minimise support structures, reduce distortion and achieve desired mechanical properties. Part position refers to the arrangement of multiple parts within the build chamber to maximise the utilisation of the available space and optimise build efficiency. Support structure generation involves the generation of supports to provide stability to overhanging or delicate features during the build process. These activities require skilled engineering expertise and specialised software tools. The time and effort invested in build preparation directly impact the overall production cost such as material consumption, machine runtime and post-processing requirements. In this developed costing tool, build preparation has been summarized into two major components, which comprise total build time and total build preparation cost. Both mentioned components will be further discussed in the next section.
Total build time refers to the cumulative time required to complete the additive manufacturing process for a specific build job. The build time directly affects the machine utilisation and production capacity, as it determines how long the machine is occupied for a particular build job. Longer build times can result in reduced throughput and increased production lead times, which may impact the overall cost and efficiency of the manufacturing process. The total build time, \(\:{T}_{build}\) can be calculated from the number of builds, \(\:{N}_{build}\), first-time build preparation, \(\:{N}_{build\left(i\right)}\) and subsequent build preparation, \(\:{N}_{build\:(i+n)}\) as written in Eq. 8. From Eq. 9, the total build preparation cost, \(\:{C}_{preparation}\) is the product of the total build time and salary per hour, \(\:{C}_{salary/hour}\).
$$\:{T}_{build}=\left({N}_{build}-1\right)*\left({N}_{build\left(i\right)}+{N}_{build\:(i+n)}\right)$$
(8)
$$\:{C}_{preparation}=\:{T}_{build}*{C}_{salary/hour}$$
(9)
Build consumables refer to the materials and resources consumed during the SLM process for a specific build job. This developed costing tool considers several factors related to total build consumables. The total warm-up time accounts for the duration required to heat up the SLM machine and its components before printing. The total print time represents the cumulative duration of the actual printing process, considering factors like part complexity and printing parameters. The total cool-down time allows the printed part and machine to reach ambient temperature safely. These mentioned parameters are considered as the total cost of operation, \(\:{C}_{operation}\) can be calculated by the total product of additional cost, \(\:{C}_{additional}\) with the summation of the total warm up time, \(\:{T}_{warmup}\), total print time, \(\:{T}_{print}\) and total cool-down time, \(\:{T}_{cooldown}\) as written in Eq. 10.
$$\:{C}_{operation}={C}_{additional}*\left({T}_{warmup}+{T}_{print}+{T}_{cooldown}\right)$$
(10)
The cost of printing consumables includes the cost of the substrate plate in which its initial purchase and fabrication costs are to be considered. The substrate’s price and size depends on the selected material’s price and the build volume of the selected SLM machine, respectively. Cost of printing consumables, \(\:{C}_{p.consumable}\) is the total product of the number of substrate plates, \(\:{N}_{substrateplate}\) and cost of substrate plate, \(\:{C}_{substrateplate}\) as written in Eq. 11. The total cost of build consumables, \(\:{C}_{b.consumable}\) is a total of the cost of operation and the cost of printing consumables as shown in Eq. 12.
$$\:{C}_{p.consumable}={N}_{substrateplate}*{C}_{substrateplate}$$
(11)
$$\:{C}_{b.consumable}=\:{C}_{operation}+{C}_{p.consumable}$$
(12)
Process continuity refers to the consideration of an additional substrate plate to ensure a continuous production flow without interruption. Instead of waiting for the completion of the post-printing stage to reuse a substrate plate, an additional substrate plate can be fed into the machine subsequently after the printing stage. In the post-printing stage, heat treatment, support removal and quality control measures are performed to ensure the integrity and accuracy of the fabricated parts. This includes inspections, measurements and testing, which contribute to the overall time required. Accurate time costing of these stages is crucial for production planning, resource allocation and determining the production capacity of the SLM process. The general processing time can be divided into two groups, namely Group A and Group B which follow in sequence to complete the SLM process workflow. The use of an additional substrate plate eliminates the need for Group B processing time and ensures process continuity, as shown in Fig. 3.
Fig. 3
General Processing Time
Equation 13 shows the total processing time without an additional substrate plate and Eq. 14 shows the total processing time with an additional substrate plate. In this section, the manufacturer is able to propose an extra substrate plate to the client in order to ensure the smoothness of the process. The time saving to fulfil client’s requirement with and without extra substrate plate can be estimated.
$$\:\left(A+B\right)+\left(A+B\right)=2A+2B$$
(13)
$$\:\left(A+B\right)+\left(A-B+B\right)=2A+B$$
(14)
In the calculation of costing analysis for the SLM process, post-process cost refers to the expenses incurred after the completion of the printing phase. Post-processing includes various operations, such as post-treatment, support removal and quality control. In this developed costing tool, the total cost for post-treatment, \(\:{C}_{post.treatment}\) is the total product of the number of printings, \(\:{N}_{printing}\) and cost of heat treatment, \(\:{C}_{heat.treatment}\) as written in Eq. 15.
$$\:{C}_{post.treatment}={N}_{printing}*{C}_{heat.treatment}$$
(15)
The geometrical complexity of SLM components often requires the inclusion of support structures to stabilize the part during fabrication and to dissipate thermal heat during build. However, these supports are temporary features that serves no purpose on the final design after the manufacturing process and therefore must be removed. The removal process would be affected by the area of support structure to be removed, accessibility to reach geometrically restricted regions and the hardness of the material, which may be translated to its machinability. Hardness refers to the material’s resistance to cutting or deformation and material with higher hardness generally have low machinability. Support removal of these materials is more difficult and time consuming due to lower feed rates in minimizing tool wear.
The cost of support removal, \(\:{C}_{support.removal}\) can be determined from the product of support area, A, index of support thickness, d and worker skill level, \(\:{P}_{l}\), with dependencies on predefined reference area, \(\:{A}_{reference}\) and reference time to remove the support, \(\:{t}_{reference}\), which varies based on the metal powder used during build. The whole formula is described in Eq. 16. The values for \(\:{A}_{reference}\) and \(\:{t}_{reference}\) are based on research data indicating that removing 10 cm2 support structure of SS316L component requires 20.87 min. As for other metal powder, the value for \(\:{t}_{reference}\) is approximated based on reported hardness from past studies [21‐26]. The summary of reference time for each metal powder is presented in Table 2.
$$\:{C}_{support.removal}={t}_{reference\:}*\:d\:*\:{\left(\frac{A}{{A}_{reference}}\right)}^{0.5}/{P}_{level}$$
(16)
Table 2
Reference time for various SLM powder
Metal Powder | Reference Area (cm2) | Reference time (min) |
|---|---|---|
Aluminium | 10 | 11.90 |
SS316L | 20.87 | |
Inconel 625 | 32.56 | |
Ti6Al4V | 36.11 | |
CoCrMo | 36.31 | |
Inconel 718 | 44.66 |
From Eq. 17, the cost of quality control, \(\:{C}_{quality}\) is the total product of cost for the operator, \(\:{C}_{operator}\), cost of inspection, \(\:{C}_{inspection}\) and the number of printings. From Eq. 18, the total cost for post-process, \(\:{C}_{post.process}\) is the total summation of the total cost of post-treatment and the total cost of quality control.
$$\:{C}_{quality}={C}_{operator}*{C}_{inspection}*{N}_{printing}$$
(17)
$$\:{C}_{post.process}={C}_{post.treatment}+{C}_{quality}+{C}_{support.removal}$$
(18)
To develop, test and analyse a novel costing tool for the Selective Laser Melting (SLM) process, a case study will be designed and executed, focusing on both single and serial production. For this study, an aileron aircraft bracket made from SS316L will be selected as the component to be fabricated using the Renishaw RenAM 500E SLM machine. Following successful printing, the additively manufactured component will undergo a heat treatment process.
3 Development of costing analysis tool for Malaysian Context - (MY)CAT4SLM©
A comprehensive analysis of a costing tool specifically tailored to the Malaysian context is undertaken through the application of advanced Excel spreadsheet functionalities and macro programming. This methodological approach not only enhances analytical efficiency but also affords a significant degree of flexibility in addressing and resolving the mathematical equations previously articulated. By harnessing the capabilities of Excel, this tool facilitates the input of diverse parameters, the execution of intricate calculations and the generation of meaningful outputs, thereby enriching the understanding of cost dynamics within the Malaysian economic environment. Additionally, the integration of macros serves to streamline repetitive tasks, thereby augmenting both the effectiveness and precision of the analysis.
The overarching objective of this study is to establish a robust framework for cost analysis that is not only adaptable to various scenarios but also reflective of the complexities inherent in the Malaysian market. Additionally, this preliminary development of the costing tool is expected to be enhanced with better representation. The layout designs of the developed costing tool with 4 tabs are shown in Figs. 4, 5, 6, 7 and 8.
The first tab introduces the costing tool with the list of the developers, as shown in Fig. 4. The second tab consists of a layout for the user to give input on their designated product. The inputs include the intended print quantity, product dimensions with its mass, selected powder material and SLM machine. Additionally, the post-processing heat treatment requirements can be defined. In this tab, a better understanding related to the process can be forecasted by both the manufacturer and clients, as shown in Fig. 5.
Fig. 4
Introduction Page of (MY)CAT4SLM©
Fig. 5
“User Input” Section in (MY)CAT4SLM©
The third tab consists of powder material information, including the mass and cost required to build the component and the calculation of the support structure required for the whole production run. In this section, metal powder is divided into two, namely non-recycled powder, which cannot be used due to partial melting and recycled powder, which can be used for the next production. The output from this tab is the total cost of the required material, as shown in Fig. 6.
Fig. 6
“Material Section” in (MY)CAT4SLM©
The fourth tab defines various SLM machine details that were used to calculate costs related to the machine cost, such as upfront, maintenance and depreciation. Additionally, some details, such as the build volume, maximum printing rate, etc., are used to calculate the total printing time. This tab gives the manufacturer a clear idea of the overhead cost to run the company and the total machine runtime cost, as shown in Fig. 7.
Fig. 7
“Machine Section” in (MY)CAT4SLM©
The last tab calculates the total process of SLM, including build preparation, additional consumables, post-treatment, quality control and post-processing support removal. This tab covers the cost, starting with pre-printing stages such as simulation and improvement. The machine setup cost from warm-up until cool down, post-processing heat treatment and support removal and ends with the cost of quality inspection as shown in Fig. 8.
Fig. 8
Process Structure in (MY)CAT4SLM©
In this developed costing tool, the Excel Spreadsheet generates advanced graph data to give a clear view of the costing analysis of SLM production. The advanced graph calculates the production cost per part for any specified production volume and defines the relationship between the variables. Data derived from the graph enables accurate cost assessment and promotes effective budgeting and strategic production planning. Figure 9 shows an example of the calculated cost per part for fabricating the optimised aileron bracket plotted against the production volume up to 100 components produced and compared across three different SLM machines: RenAM 500E, EOS M400-4 and EOS M100. Fluctuations observed in the curves indicate the initiation of a new build, contributing additional cost in terms of machine runtime and post-processing operations.
Fig. 9
Results from Advanced Graph
4 Result and discussion of (MY)CAT4SLM© implementation for aileron bracket
This section analysed the production of the SS316L aileron aircraft bracket in single, optimum and serial production contexts. Serial production refers to the consecutive manufacturing of a specific quantity of 100 aileron aircraft brackets. The costing tool developed in this study utilised the bounding box data presented in Fig. 10 to predict the maximum number of aileron aircraft brackets that can be accommodated on a single substrate plate during a single build job. The model estimated that a total of 15 components can be produced in this manner. The total quantity of printing tasks necessary to manufacture optimum and 100 units of aileron brackets is twofold and sevenfold, respectively. The summary of necessary printing tasks for the production of aileron aircraft brackets in a single and serial production is presented in Tables 3, 4 and 5.
Fig. 10
Dimension of Bounding Box for Aileron Aircraft Bracket
Table 3
Summary of required printing job (single production)
Component(s) Produced | Number of Printing(s) | Number of Components per Printing |
|---|---|---|
1 | 1 | 1 |
Table 4
Summary of required printing jobs (optimum production)
Component(s) Produced | Number of Printing(s) | Number of Components per Printing |
|---|---|---|
8 | 1 | 8 |
Table 5
Summary of required printing jobs (serial production)
Component(s) Produced | Number of Printing(s) | Number of Components per Printing |
|---|---|---|
100 | 7 | 15 |
The developed costing tool successfully predicted the total and average costs per part for single, optimum and serial production. For a single unit of the aileron aircraft bracket, the total production cost is estimated at RM 4,202.65. As the production volume increases, the average cost per part drops significantly before stabilizing due to distributed cost among multiple printed parts. In the case of optimum production using RenAM 500E, the production cost to produce two parts in separate prints is approximately RM 8,405.31, while producing eight parts in a single print would cost almost similarly at RM 8,140.07. The minor discrepancies between the costs emphasize the cost-effectiveness of optimal production setup. For a serial production to produce 100 units of aileron aircraft brackets is RM 78,346.82 and the average cost per part is RM 783.47. The cost for serial production to produce aileron aircraft brackets was significantly reduced by up to 81% compared to a single production. The price breakdown for single, optimal and serial production is depicted in Fig. 11.
Fig. 11
Result of the price breakdown
From the previous statement, it can be seen that this developed costing tool has shown better results in predicting the cost of the additive manufacturing process. The material cost can be accurately estimated since the calculation considers recycled powder. The recycled powder saves a lot of production costs since unused metal material can be reused for the next production cycle. For optimal production, it is suggested to print additional parts in advance to reduce both cost and lead time just in case if more parts is needed in the future. Moreover, in serial production, the cost of machine usage can be reduced due to the machine’s hourly rate being neglected in terms of warm-up time and cool-down time.
The advanced graph data analyses the average cost per part with part quantity. The result shows that the average cost per part reduced tremendously after utilizing the optimum number of printed parts on the substrate plate. Previous research shows that conventional manufacturing production costs decline as fabricated quantities rise. However, the cost of additive manufacturing production remains constant [11]. A case study is demonstrated using the advance graph data to evaluate the utilisation of RenAM 500E in producing up to 100 aileron aircraft brackets. Cost spikes after every 15 components shows that each build can fit 15 components per print when using RenAM 500E. The findings suggest that the fabrication cost per part for single unit production is initially high. However, the cost decreases exponentially to an optimal pricing point at 8th production, leading to cost reduction with more than 50% before and the cost per part gradually decreases up to 80% as the production volume continues until 100 units, as shown in Fig. 12.
Fig. 12
Results from Advanced Graph special for RenAM 500E
The cost variation associated with producing up to 100 parts using various SLM machines was analysed and compared with a quotation obtained from local company to provide an overview of the manufacturing cost covered by local MAM company. For single-unit production, the cost per part using RenAM 500E have the closest estimation of RM 4,202.65 compared to quoted RM 4,300.00 per part using the same machine. The deviation of 2.26% from local Malaysian manufacturer validated the tool’s accuracy and practical applicability in the local market context with high accuracy. However, at production volume of 100 units, a significant cost variation can be seen between RM 3,870.00 quoted from local company, whereas RenAM 500E’s cost drops significantly to RM 754.29 per part. The difference increased to 80% and is believed to be caused by the developed tool calculating the actual process-based production cost and excluding profit margins to provide a more objective reflection of true manufacturing cost. The model still remains efficient and valuable for SME manufacturers to obtain data-driven cost estimation in preventing underquoting or overquoting during quotation preparation.
Moreover, EOSM100 consistently demonstrates lower cost per part than offered by local Malaysian company, starting from the single unit production up to 100 units production. The performance of this machine is surpassed by EOSM400-4 beginning from the third part production onward by having a lower cost per part which is mainly attributed to the fact that EOSM100 has lower build volume, which restricts it to only fit two parts per printing, hence the cost increases as the requirements to setup the machines and post-processing increases. As the demand for higher production volumes increases, EOSM400-4 shows better efficiency by maintaining a lower production cost per part. This lower cost is largely due to the machine having the fastest build rate despite having the highest build volume, making it more suitable for large parts production. Figure 13 shows the cost variation per part up to 100 units from three different SLM machines and quotation from local company, with each curve fluctuation represents an increase in cost per part resulting from the addition of a new build job, which in turn increases material consumption to fill up the build volume. For EOSM100, each print job can fit two components, whereas RenAM 500E can fit up to 15 components per print. The machine with largest build volume, EOSM400-4, can accommodate up to 40 components per print.
Fig. 13
Cost validation per part from various SLM machine and local manufacturer
The calculated manufacturing cost differs depending on the machine type used and the maximum printing rate percentage. The calculated serial production cost for 100 heat treated aileron aircraft brackets fabricated using various SLM machines, specifically the RenAM 500E, EOS M100 and EOS M400-4. The notable differences between each machine for a single production unit are attributed to their build volumes, which are 62,500 mm2, 7,900 mm2 and 160,000 mm2 and maximum printing rate of 6.9444 mm3/s, 1.3889 mm3/s and 29.7222 mm3/s, respectively. The calculated cost for low-volume production is significantly influenced by the build area of the SLM machine, whereby larger build volumes generally have higher initial costs, but the cost per part decreases as the production volume increases. This behaviour can be attributed to greater amount of metallic powder required to fill the build area, although the component only occupies a small volume.
Moreover, all machines show an exponential decrease in cost per part as the production volume increases because the cost is divided over a larger number of parts. As costs decrease with increasing production volume, some spikes are identified occasionally before diminishing again. These fluctuations are attributed to the increased usage of substrate plates that eventually increase the costs associated with machine usage and post-processing heat treatment. The build capacity of each machine influences the frequency and location of these cost spikes. Smaller machines like the EOSM100 experience higher and more frequent spikes due to their limited build volume, which can only fit in a few components, which requires more frequent machine setup, substrate plate changes and heat treatment. However, The EOSM400-4 has lower and fewer spikes because it can fit more parts per substrate plate, reducing the frequency of setup interruptions. Based on the results, it is revealed that cost efficiency improves with higher production quantities, highlighting the importance of selecting and appropriately sized SLM machine in achieving the most cost-effective outcome for specific production requirements, hence optimising budget and manufacturing strategy. The average cost per part for fabricating 100 aileron aircraft bracket using RenAM 500E, EOSM100 and EOSM400-4 is RM 783.47, RM 1,628.65 and RM 526.30, respectively as shown in Fig. 13.
Upon deeper analysis of the cost breakdown, it was identified that RenAM500E and EOSM400-4 each allocate at least 50% of the total cost to the machine usage, whereas EOSM100 contributes only 10.7% of the total cost, despite having the highest machine usage. The contributing factor of this result is the machine runtime cost, which is calculated based on the initial price of the machine. The EOSM100 is an entry-level SLM machine featuring a smaller build area and is priced significantly lower than the two professional-grade machines, thus contributing to a smaller percentage contribution to the total production cost despite higher usage. The cost breakdown aligns well with findings from past studies which consistently identified the SLM machine as the primary cost driver between 39.5% and 75.3% [11, 15, 18, 27]. The dominance in cost driver arises from the machine’s high capital investment, depreciation costs and its influence on the total build time. Consequently, enhancement of machine efficiency by optimizing build schedule, as introduced in the developed tool, represents a strategy to reduce overall production cost.
From the labor cost perspective, the percentage contribution of the total production cost ranges from 1.3% to 8.4% for the whole production run, which is in contrast with the result of previous research, which identified labor costs as the main cost driver in SLM process with 51% contribution to the total cost [20]. Given the lower labour cost contribution found in this research, it is possible that other factors, such as the machine usage or material costs, plays a greater impact on total costs in this specific production scenario. Moreover, the machine and labor cost estimation are based on European economic conditions, meaning that factor such as import taxes and related expenses influences the total machine cost, along with the machine printing size and specifications. In this case study, the machine most comparable to the one referenced in the cited journal is RenAM 500E, which has higher runtime cost around RM 244.56/hr. This rate is significantly higher than ~ RM 98/hr reported in the journal. In terms of labor cost, the referenced journal used European hourly wage standard of approximately ~ RM 73/hr at that time, whereas the rate used in this research is lower at RM 50/hr. A Detailed cost breakdown of these machines is shown in Fig. 14 below. In this case study, the most suitable machine is EOSM400-4 for its low cost per part because of its faster printing rate despite having the largest build volume, allowing for the fabrication of 40 brackets per printing job.
Fig. 14
Cost breakdown of three different SLM machines
Post-processing heat treatment in AM is performed to mitigate the residual stresses induced from the manufacturing process of the part. The necessity of this process depends on the material, application and AM technology used. Generally, post processing which includes heat treatment, support removal and surface finishing accounts for approximately 27% of metal AM total cost [28]. Understanding of the importance of post processing and strategies for reducing its necessity through parameter optimization could reduce the cost associated with post processing step. The elimination of heat treatment process significantly reduces the overall manufacturing costs, as heat treatment costs are calculated per print job. Given that the EOSM100 machine has a small build space that can only fit two brackets per build, production of 100 brackets would require 50 heat treatment process. By eliminating this requirement, this machine shows significant cost savings. However, despite these reductions, it still falls short of surpassing the performance of EOSM400-4 which has fastest build rate and remains as the optimal option for this production scenario with lowest cost per part recorded at RM 434.12 as shown in Fig. 15.
Fig. 15
Graph of average cost per part without heat treatment
5 Conclusion and recommendations
The highlight of this investigation is to develop comprehensive costing tool in order to predict the cost production of SLM process. According to the results of analysis, the following conclusions can be summarized:
i.
The developed costing tool (CAT4SLM) provides an accurate cost estimation framework suitable for SMEs adopting metal additive manufacturing.
ii.
Optimum number of aileron brackets on a substrate plate can be estimated by improving build utilization.
iii.
Comparable total costs for two single-part build and optimal eight-part builds indicate significant efficiency gains from larger batch sizes.
iv.
The high initial cost-per-part of SLM can be significantly reduced by 50% at the eighth production and further decreased gradually up to 80% at 100th production.
v.
The implementation of additional substrate plate has demonstrably decreased the overall costs associated with serial production when compared to scenarios in which no additional substrate plate is utilized.
vi.
The total cost for serial production to produce aileron aircraft brackets was significantly reduced by up to 80% compared to a single production.
vii.
Support removal time is dependent on both worker skill and mechanical properties of metal powder used which can significantly influence the support removal time with softer material being easier to be removed and vice versa.
viii.
Selection of appropriately sized SLM machine is important to improve production cost efficiency, depending on the production scale.
ix.
Results demonstrated strong agreement with manufacturer quotations (2.26% deviation) which demonstrates its practicality on cost estimation for SME.
x.
This novel costing model can be used for other countries with different currency value.
As further recommendations, the following research initiatives are proposed:
i.
Conduct a detailed analysis of operating costs with a specific focus on labor.
ii.
Integrate the costs of SLM post-processing such as sandblasting, hot isostatic pressing (HIP), and secondary machining into the existing costing tool.
iii.
Include gas consumption and costs in the costing model.
iv.
Develop dedicated costing tools for other Metal Additive Manufacturing (MAM) processes, such as Wire Arc Additive Manufacturing (WAAM), Direct Energy Deposition (DED) and Binder Jetting.
Acknowledgements
The authors would like to express their gratitude to the staff member at Smart Manufacturing Research Institute (SMRI), Advanced Manufacturing Laboratory at School of Mechanical Engineering, Universiti Teknologi MARA (UiTM) in Shah Alam, Malaysia and Department of Industrial Engineering at Universitas Sumatera Utara. This internationally collaborated research was conducted under the Adjunct Professor Senior Research Fellow at Universitas Sumatera Utara (USU) in Medan, Indonesia. The work entitled “(MY)CAT4SLM: Costing Analysis Tool for SLM Product in Malaysia” is hereby asserted as a copyright work subject to protection under the Copyright Act 1987 of Malaysia. Notification of this copyright has been duly filed with and acknowledged by the Malaysian Intellectual Property Office (MyIPO), an agency under the purview of the Ministry of Domestic Trade and Cost of Living and is held under the official Notification Number CRLY2025W08502.
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Conflict of interest
All authors declare that there is no conflict of interest.
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