A Hierarchical Axiomatic Evaluation of Additive Manufacturing Equipment and the 3D Printing Process Based on Sustainability and Human Factors

Abstract

As interest in additive manufacturing (AM) continues to increase, it has become more important to have a robust method to help potential users select the AM process that best suits their technological needs while providing the greatest potential benefits in terms of sustainability and its effect on people. This paper presents the development of a framework for selecting the best AM process for a given application by considering both sustainability and human factors through the combination of axiomatic design and the analytic hierarchy process. Thirty-one participants with varying levels of expertise (novice and advanced users) were involved in the study, considering the frequency of 3D printer usage (novice users: never, rarely; expert users: sometimes, almost always, always) for prototyping parts. They employed fused deposition modeling (FDM) and stereolithography (SLA) (both 3D desktop printers) and collected data on five evaluation criteria. The participation of experts helped establish a novel methodology, with material cost deemed most important (49.8%), followed by cycle time (28%), energy consumption (11.7%), error rate (6.6%), and equipment noise (3.9%). The results showed that FDM was the optimal equipment option for advanced users. By examining the information content of the other options, it was found that FDM demanded less information than SLA, regardless of the user’s level of expertise. The proposed method is appropriate to assess the sustainability aspect of FDM and SLA; however, it can be further improved by adding indicators such as environmental impact, recyclability, and ergonomic and occupational health factors.

1. Introduction

The fourth industrial revolution (I4.0) is transforming production worldwide, leading to the rise of intelligent manufacturing, where humans, machines, and products communicate in virtual environments [1,2,3]. This has led to the adoption of additive manufacturing (AM) as a key method for producing complex parts and products [1]. The popularity of AM has grown in recent years due to its declining cost and increased accessibility, as well as its innovative applications in fields such as manufacturing and medicine [4]. AM has experienced significant growth and development since its introduction in the 1980s, with machines and processes like stereolithography (SLA) and fused deposition modeling (FDM) being widely used [4].

SLA is considered the world’s first 3D printing system and was patented by Charles Hull in 1986. This technique uses light to cure a liquid material and form a 3D object by solidifying thin layers of resin with a laser until a 3D part is fully built. SLA is one of the most popular AM processes due to its ability to produce parts with complex geometries, high accuracy, and good surface finishes at a relatively fast speed [5,6,7].

FDM is a 3D printing technology invented in 1988 by Scott Crump and is currently the most widely used 3D printer technology [8]. FDM is a cost-effective method for producing end-use polymer products using a computer-controlled extruder that deposits successive layers of semi-melted thermoplastic material in the form of a thin filament to build 3D parts. FDM has been compared to a sophisticated glue gun by Hull [8,9].

3D printing involves designing the desired geometry using CAD software, converting it into a G-code file using a slicing program, and producing the final part using a 3D printer with specific materials [10]. However, the creation of 3D models requires design software, which has become more accessible in recent years. Using design software effectively still requires a certain level of technical knowledge, as it may be challenging for non-expert users to navigate the human–machine interaction [11,12]. Online stores offering 3D designs have reduced the gap between expert and non-expert users, allowing non-experts to access complex designs without advanced technical knowledge [13].

The solution may be helpful for some, but it does not address the DIY trend. Successful and sustainable 3D printing requires users to evaluate capabilities, identify use cases, and make decisions on orientation, process parameters (temperature, layer height, etc.), and support structures, which can be challenging for some [14].

Furthermore, while significant advances have been made in additive manufacturing research, it is critical to acknowledge the existence of unexplored domains, particularly in the application of additive manufacturing in creative industries such as fashion, architecture, sculpture, and cultural heritage preservation [15]. Concurrently, large-scale automated additive construction has issues related to robotic solutions and environmental sustainability. These issues can be solved by improving processes, materials, and large-scale printed structures, which will promote the development of more efficient and sustainable construction technologies [16].

When compared to traditional manufacturing processes, direct metal laser sintering (DMLS) has been shown to offer the highest environmental risk to human health, whereas computer numerical control (CNC) machining has an impact on ecosystem quality. In contrast, AM is more suited for complicated geometries, though at a 91% greater cost than CNC machining. These comparisons highlight the substantial differences in production processes, emphasizing the significance of considering environmental and economic aspects when determining the ideal manufacturing process [17].

Human factors, including ergonomics, psychology, anthropometry, sociology, medicine, safety, and accessibility, are crucial for the use, design, and performance of systems, products, or environments [3,18]. Moreno-Cabezali and Fernandez-Crehuet identified risks that can negatively impact research and development projects focused on automation (AM) in terms of scope, deadline, cost, and quality [19].

Xiong et al. examined the relationship between humans and machines in the context of AM and classified collaborative activities into active–supportive and active–active activities [20]. Reiman et al. reviewed the challenges faced in manufacturing processes in the context of I4.0 in five areas: human aspects, technology, work tasks, work environment, and organizational levels. They concluded that humans will not be eliminated from manufacturing processes due to technological development, but their roles may change and call into question current practices and processes of human factors and ergonomics (HF/E) [21].

With the rise in popularity of 3D printing, there is a need to create easier methods, tools, and frameworks to help users choose the best AM process for their needs, considering technical and economic factors while also considering sustainability and human factors [20,22,23]. Studies have published on the decision making involved in the selection of AM processes, but many focus only on one or two aspects of the decision, such as material, process, and machine combinations [23].

Song and Telenko’s research focused on how the decisions and actions of humans affect the environmental impact associated with the use phase of FDM 3D printers. They recognized that novice users often make inadequate design and operation decisions, leading to increased manufacturing failures and increased material and energy consumption [24].

Therefore, the use of an HF-centered approach could be very beneficial for analyzing, understanding, and designing human work in I4.0. The use of this approach to design work systems in I4.0 could improve the overall performance of complex socio-technical systems and workers’ well-being.

Zaman et al. introduced a generic decision-making methodology using multiple criteria decision-making (MCDM) tools to consider feasible combinations of additive manufacturing (AM) materials, processes, and machines [25]. Furthermore, Agrawal discussed sustainable materials for FDM, SLA, and selective laser sintering (SLS) technologies [26]. Kadkhoda-Ahmadi et al. proposed a design for an additive manufacturing (DfAM) approach focusing on manufacturing capability, manufacturing process, and resource selection [27].

Accordingly, there is a need for a more comprehensive approach to selecting AM processes [23]. General functionality indices and weights must be considered for each application area, including cost, material strength, energy consumption, environmental impact, and recyclability [25]. Yao et al. solved a multidisciplinary design optimization (MDO) problem for customized AM products, aiming to maximize functionality utility, meet customer performance requirements, and minimize total cost [28].

Mahadik and Masel developed an additive manufacturing cost estimation tool (AMCET) using a decomposition approach to calculate the total cost of AM by summing machine, material, labor, and post-processing costs [29]. Energy consumption is a relevant factor in the adoption of AM, with Telenko and Seepersad indicating that AM adoption could provide energy savings, especially for small production volumes [30]. The waiting period and preheating time in additive manufacturing can vary between tasks, leading to variability in energy consumption. Errors in printing can increase material consumption and energy consumption and can occur due to insufficient preheating time, the improper geometry of parts, or printer malfunctions [31].

The analytical hierarchy process (AHP) has been utilized in several studies to solve challenges associated with the selection of AM equipment [32,33,34,35]. However, while AHP has significance in this context, it may not provide a complete assessment of the AM equipment selection process. This limitation can be effectively addressed by integrating axiomatic design (AD) principles, which can stimulate technical creativity and be seamlessly integrated with the AHP [36]. A hierarchical decision-making model using AD, customer requirements, quality, time, and costs makes it possible to not only give weight to qualities but also systematically select the most suited equipment based on the complete assessment of many design possibilities, thus optimizing evaluation indicators [37].

The main objective of this research is to provide a method for identifying the optimal AM process by adopting a hierarchical, axiomatic evaluation based on sustainability and human factors. This evaluation was applied to undergraduate and graduate engineering students with varying degrees of expertise (novice and advanced users). By enhancing the knowledge base and methodology for selecting and evaluating AM processes, this research can help improve the efficiency, effectiveness, and overall impact of additive manufacturing across a wide range of industries and applications.

1.1. Theoretical Background

1.1.1. Axiomatic Design

Axiomatic design (AD) theory provides a scientific basis for the design and improvement of activities, offering a solid understanding of theories based on logical and rational thinking processes and tools. It is applied in several different fields, such as software, manufacturing, healthcare, and e-commerce [38,39].

AD is a methodology that starts with customer needs (CNs), taking into account the relationship between adjacent domains as shown in Figure 1. For each pair of adjacent domains, the domain on the left represents what we want to achieve, while the domain on the right represents how the designer proposes to achieve it. Functional requirements (${FR}_s$) refer to what customers want to achieve with the system or the main objective of the design. Design parameters (DPs) describe how to meet the functional requirements. Finally, process variables (PVs) encompass the development of the system process based on the established DPs [39,40].

AD establishes that the following axioms must be met to achieve good design: (1) ${FR}_s$ must be independent (independence axiom) and (2) the information content of the design must be reduced (information axiom) [41].

When the independence axiom is met, a diagonal decoupled design matrix is formed, which demonstrates that each FR corresponds only to one DP [39]:

$$\left(\begin{array}{c}{FR}_1\\ {FR}_2\end{array}\right)=\left(\begin{array}{cc}X& 0\\ 0& X\end{array}\right)\left(\begin{array}{c}{DP}_1\\ {DP}_2\end{array}\right)$$

(1)

Typically, the principles of AD are deployed based on probability. The information content is the probability of satisfying the ${FR}_s$. For a given ${FR}_i$, the information content $I_i$ is defined as follows:

$$I_i={log}_2\left(\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$p_i$}\right.\right)$$

(2)

where $p_i$ expresses the probability of achieving the given ${FR}_i$ and “$log$” is a base 2 logarithm (with a unit of bits). If there are n ${FR}_s$, the total information content is the sum of all these probabilities [42].

The probability of satisfying the ${FR}_s$ can be represented by the design range and the system range, as presented in Figure 2. The design range can be considered as the tolerance that the designer is trying to achieve, while the system range is a measure of how well the system is able to comply. Acceptable solutions for design alternatives are determined by the overlapping area between the design range and the system range, called the common range [38].

In the simplest case, $p_i$ can be formulated by considering that the system probability is uniformly distributed within its range:

$$p_i=\raisebox{1ex}{$common range$}\!\left/ \!\raisebox{-1ex}{$system range$}\right.$$

(3)

A higher $p_i$ denotes that the design presents a high degree of compliance with what the designer wants to achieve; thus, the design is more appropriate in terms of satisfying ${FR}_i$. Given this, $I_i$ can be rewritten as follows [44]:

$$I_i={log}_2\left(\raisebox{1ex}{$system range$}\!\left/ \!\raisebox{-1ex}{$common range$}\right.\right)$$

(4)

1.1.2. Analytical Hierarchy Process

The analytical hierarchy process (AHP) is a method used to structure and analyze complex decision problems. It consists of breaking down a problem into a hierarchical structure with objectives, criteria, and sub-criteria and then evaluating the relative importance of each element [45,46]. Currently, AHP is widely used in fields such as operations research, management, engineering, and economics, among others [47].

The process begins by creating a hierarchical structure that represents the aim, criteria based on the goal, and alternatives based on the criteria. Then, comparative judgments are made by constructing a pairwise comparison between the n criteria; thus, a matrix of order n is formed based on these comparisons. It is important to note that the number of comparisons for n factors is given by [45,46,47,48]:

$$Number of comparison=\raisebox{1ex}{$n\left(n-1\right)$}\!\left/ \!\raisebox{-1ex}{$2$}\right.$$

(5)

A pairwise comparison is performed with the help of the fundamental scale of Saaty’s relative importance on an absolute scale of 1 to 9. Here, a numerical score of 1 indicates that the two activities contribute equally to the goal, while a numerical rating of 9 represents the extreme order of assertion of one activity over the other [49]. By applying the reciprocal values from the upper diagonal, the lower triangular matrix of the pairwise comparison matrix $A$ is filled, as observed below:

$$A=\left[\begin{array}{ccc}{a}_{11}& \cdots & a_{1n}\\ ⋮& \ddots & ⋮\\ \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$a_{1n}$}\right.& \cdots & a_{nn}\end{array}\right]$$

(6)

To assign weights to the parameters, the values in each column of the pairwise comparison matrix $A$ are summed, the matrix is normalized by dividing each element by the total of its column, and then the sum of the elements in each row of the normalized matrix is calculated, obtaining vector w [48]:

$$\left[w_1,\dots w_n\right]$$

(7)

In a real-world problem, it is not always possible to find perfect judgments; therefore, a consistent pairwise comparison matrix $A$ is needed after performing pairwise comparisons [36]. The consistency ratio $\left(CR\right)$ is an important aspect of AHP, as its value primarily determines optimal decision making in pairwise comparisons [50]. The $CR$ can be verified using the following equation:

$$CR=\raisebox{1ex}{$CI$}\!\left/ \!\raisebox{-1ex}{$RI$}\right.$$

(8)

where $CI$ is the consistency index and $RI$ is the randomness index.

Table 1. Randomness index (RI) [51].

N	1	2	3	4	5	6	7	8	9	10
RI	0	0	0.58	0.90	1.12	1.24	1.32	1.41	1.45	1.49

presents the $RI$ for different values of $n$. The $CI$ is calculated as follows:

$$CI=\raisebox{1ex}{${\lambda }_{max}-n$}\!\left/ \!\raisebox{-1ex}{$n-1$}\right.$$

(9)

${\lambda }_{max}$ is the principal Eigenvalue, which is obtained from the sum of the products between each element of vector w and the sum of the columns of the pairwise comparison matrix $A$ [51]. Judgment entries are not reliable and not accepted if the pairwise comparison matrix A has a high $CR$ value. However, a $CR$ value of less than or equal to 10% is acceptable [52].

2. Materials and Methods

2.1. Materials

The study required the equipment under evaluation, the materials for printing the case study, and formats for collecting data from the participants. In terms of FDM equipment, we used a high-performance LulzBot Mini 1 desktop 3D printer, (Aleph Objects, Loveland, CO, USA) designed to work immediately without a complicated assembly process. It had an open filament system, a fully metal extrusion nozzle that heated up to 300 °C (572 °F), and a bed that heated up to 120 °C (248 °F), which means it could heat up to the temperatures required for industrial-grade materials [53]. To evaluate the SLA technology, a FormLabs Form 2 3D printer (Formlabs Inc., Somerville, MA, USA) was used, which was considered during its launch in the market as a standard in professional and affordable 3D printing, featuring good dimensional accuracy [54]. Additionally, two pieces of auxiliary post-processing equipment (washing and curing) for stereolithographic prints were included: Form Wash and Form Cure (Formlabs Inc., Somerville, MA, USA) [55]. Figure 3 shows the equipment described above.

Furthermore, we used a Microsoft Surface Pro 4 2-in-1 portable computer (Microsoft Corporation, Redmond, Washington, DC, USA) with the Cura LulzBot 3.6.20 Edition (Aleph Objects, Loveland, CO, USA) and PreForm 3.3.0 software (Formlabs, Somerville, MA, USA) to prepare the files for printing and control the operation of the 3D printer [56,57].

Materials such as polylactic acid (PLA) and acrylonitrile butadiene styrene (ABS) were used for prototyping parts, considering the suppliers authorized by LulzBot. The Draft V1 resin was used with the Form 2 3D printer, which handled print resolutions (300 microns) like the Lulzbot Mini (50–500 microns) [53,58]. Information related to the printers and materials used and the process features are presented in

Table 2. Equipment, materials, and process features.

Feature	Lulzbot Mini	Form2
3D printing technology	Fused deposition modeling	Stereolithography
Material	PLA and ABS	Draft V1 resin
Material consumption	4–7 g	7.13–7.30 mL
Fill density	50–100%	100%
Layer height	250 microns (standard print)	300 microns

. The study was limited to polymeric materials to focus on specific applications where polymers are commonly used and to provide a starting point for developing their methodology to expand research to include other types of materials in the future.

A Steren HER-432 120Vca wattmeter and a Steren HER-403 (Steren Electronics, Mexico City, Mexico) sound meter were used to measure the electrical consumption and noise intensity of the equipment used. Likewise, a timer was used to measure the duration of the cycle required to produce a prototyped part.

It is important to note that the participants used a single STL file for a stove knob to simulate a prototyping application for replacement parts. This was established by the research team based on information gathered from different maker communities such as MyMiniFactory, Cults, or Thingiverse. In addition, a series of videos (own elaboration) was provided that broke down and described each subgoal, starting from the quick start guide and user manual of each piece of 3D printing equipment. Finally, an error and doubt log were used to obtain information related to mistakes made and doubts about each of the tasks performed by the participants and estimate their error rate.

2.2. Participants

Given that the study was carried out during the coronavirus (COVID-19) pandemic and considering the pivotal role of 3D printers in manufacturing essential medical models [59], a convenience sampling technique was used, which is a non-probabilistic and non-random sampling technique. A total of 31 subjects were invited to participate according to the following inclusion criteria: gender not specified (45% women and 55% men); over 18 years old (average of 22 years); and students of bachelor’s and graduate degrees who also have different levels of expertise (novice and expert user) when considering the frequency of use of a 3D printer. The participants were specifically 1 architecture student, 1 industrial design student, 7 mechatronics engineering students, 19 industrial engineering students, and 3 graduate students (Master of Science). Students from other universities were excluded for health and safety reasons. All the subjects utilized both 3D technologies as part of the study.

To determine the frequency of use of 3D printers, participants were interviewed and categorized as novice users or expert users. The categories were defined as follows: novice users had either never used a 3D printer or used it rarely (a few times in their lives), while expert users used it regularly (at least once per semester). Advanced students, who were classified as expert users, generally created school projects that involved designing and producing prototypes, frequently depending on 3D printing.

In addition, a team of four experts in AM with 3 to 12 years of experience dedicated to the provision of prototyping and design services for parts were also included. These experts comprise materials engineers and project managers with specific roles spanning from product design to prototype manufacturing. Additionally, the team has been involved in relevant projects, such as prototyping automotive components and manufacturing aerospace parts. Their selection was based on their technical skills and expertise to address specific challenges based on their track record of successful projects in the industry, as well as recommendations from colleagues.

2.3. Limitations

The research described has some limitations that permit consideration. Firstly, the study solely focused on polymeric materials, thereby limiting the scope of applications explored and potentially failing to fully capture the diversity of materials used in AM techniques. Additionally, conducting the study during the COVID-19 pandemic may have affected resource availability and the dynamics of the research setting.

Furthermore, the study concentrated on two specific AM processes—FDM and SLA—each represented by a single 3D printer. This narrow focus may restrict the generalization of the findings to other AM techniques and equipment configurations. Additionally, the study’s use of a convenience sampling method to select participants could have introduced biases and reduced the sample’s representativeness.

The study’s cross-sectional design provides a snapshot of the current situation, offering valuable insights into prevailing beliefs and behaviors. However, it is important to note that due to logistical and temporal constraints, following up with the same individuals after the pandemic period was not feasible. Moreover, the lack of access to these participants post study limits the potential for gathering additional information. Therefore, considering the continuation of this study as a longitudinal investigation in the future would be beneficial to examine how attitudes and behaviors evolve over time.

2.4. Methodology

This study adopted an integrated methodology, utilizing both AD and AHP methodologies as they were considered ideal and consistent to create a method for evaluating and selecting 3D printing processes. AD provided the initial structure to clearly define and understand the design requirements and alternatives, while AHP added a layer of quantitative analysis to prioritize these alternatives based on their ability to meet the defined criteria. This integrated approach ensures that the selected 3D printing process not only aligns with the design goals but is also optimized based on a thorough evaluation of all relevant factors. The methodology consists of five stages to evaluate equipment or processes using AD and AHP. Figure 4 illustrates the entire methodological flowchart.

In Stage 1, the variables related to the equipment or process are defined. The alternatives are established according to the customer needs (CNs) and the ranges of the functional requirements (FRs), design parameters (DPs), and process variables (PVs) of the AD. With this information, the objectives and criteria are defined, and the experts are identified.

In Stage 2, the process is executed. The equipment is used or the process is developed to obtain and record the data.

In Stage 3, the criteria are evaluated. The information content for each FR is calculated, and AHP is used to make the paired comparisons and generate the normalized matrix to calculate vector w according to the relative weight of the FRs.

In Stage 4, the results are analyzed. The consistency index (CI) is calculated, and if it is ≤10%, the information content for each FR is adjusted considering the relative weight, and the information content graph is made. If the CI is not ≤10%, another expert is invited to perform the paired comparisons, and the steps of the methodology continue.

Stage 5 determines the best alternative, which is the option with the lowest information content, and prioritizes the FR.

2.4.1. Stage 1: Define the Variables

Most of the concepts discussed in Stage 1, which focused on establishing the study variables, are defined in Section 2.1 and Section 2.2 of the paper. Section 2.1 provides a detailed description of the equipment and materials used, while Section 2.2 outlines the characteristics of the study participants.

A kitchen stove knob was chosen as the piece to produce. This decision was based on its simplicity of printing using the existing setup, minimal material usage, and consideration of equipment limitations such as printing volume. Despite the possibility of selecting a necessary medical item, the stove knob was chosen for its suitability and potential to present a relevant design and functionality challenge. Figure 5 presents the parts that were manufactured using the two examined AM processes.

Table 3. Steps for printing using each AM process.

Lulzbot Mini	Form2
1. Connect and turn on the printer	1. Insert tank of resin, cleaner, and printing platform
2. Turn on the computer and set up printing software	2. Insert resin cartridge
3. Connect and gain control of the printer	3. Connect and turn on the printer
4. Remove the filament	4. Level the 3D printer
5. Insert filament	5. Turn on the computer and set up printing software
6. Print	6. Print
7. Remove part from the printer	7. Remove part from the printer
	8. Wash part
	9. Cure part

lists the tasks necessary to print a part using each AM process studied. As can be seen, the SLA 3D printing process had two additional steps that were associated with the two pieces of post-processing equipment. A series of videos (self-made) was provided to the participants that broke down and described the subgoal of each piece of 3D printing and post-processing equipment.

To define the alternatives (CR) for AD, data collected from the novice and expert participants specified in Section 2.2 were considered, along with the two 3D printing procedures (FDM and SLA) detailed in Section 2.1 Materials. The diagram used to determine the four options that need to be assessed is illustrated in Figure 6.

Regarding the evaluation criteria, the following were considered to propose a more sustainable selection approach: material cost (MXN), cycle time (h), energy consumption (kwh), error rate (number of events), and equipment noise (dB). These factors can be seen as key sustainability considerations in additive manufacturing. In the current evaluation, sustainability was considered from multiple perspectives, including its economic, environmental, and social impacts. Material cost can have a direct impact on the total cost of production and therefore the economic feasibility of an AM process [60]. Cycle time refers to the time it takes to complete a production cycle and can affect efficiency and production capacity [61]. Energy consumption refers to the use of energy to operate AM equipment and can have a significant effect on the environmental impact of the process [30]. Equipment noise can have an impact on the health and well-being of workers [62], while errors have an impact on both efficiency and safety [63].

In addition, an error and doubt log were given to each participant in which they were asked to record the errors and doubts they had when performing each task; the log was used to estimate their error rate. Because the study included two 3D printing processes (FDM and SLA) and two types of users (novices and experts), a total of four alternatives were evaluated under the five criteria.

The system range data presented in

Table 4. System range data.

Alternatives	C1: Material Cost (MXN/Part)	C2: Cycle Time (h)	C3: Energy Consumption (kwh)	C4: Error Rate (Events)	C5: Equipment Noise (db)
FDM Novice	2.08–4.34	0.732–1.829	0.048–0.268	0–3	49.3–78.0
FDM Advanced	2.08–4.34	0.796–1.309	0.075–0.123	0–2	49.3–78.0
SLA Novice	20.32–20.80	1.589–2.264	0.061–0.139	0–1	57.0–72.3
SLA Advanced	20.32–20.80	1.448–1.709	0.069–0.095	0–2	57.0–72.3

were obtained by selecting the minimum and maximum values achieved by the participants for each study alternative. It can be observed that C1 and C5 are the same since they are directly related to the AM equipment. In other words, the values of C1 and C5 are consistent for each equipment, independent of the user type.

The design range can be considered as the tolerance that the designer is trying to achieve. Taking this into account, it is important to consider the information from the provider of each piece of 3D printing equipment. However, the studied AM equipment has been discontinued and there is no technical information related to the proposed criteria. In addition, it is important to note that both pieces of 3D printing equipment were launched on the market in 2015; therefore, it is expected that they have lost efficiency over time. Therefore, the design range data shown in

Table 5. Defined design range data.

C1: Material Cost (MXN/Part)	C2: Cycle Time (h)	C3: Energy Consumption (kwh)	C4: Error Rate (Event)	C5: Equipment Noise (db)
2.05–20.50	0.889–1.753	0.066–0.164	0–1	56.8–68.6

are defined as follows: (1) the cost of material commonly represents between 45 and 55% of the total cost of the manufactured part—the design range was estimated considering this statement [64]—and (2) the rest of the criteria were valued based on the mean ±1 standard deviation of the measurements obtained by the participants in the study.

2.4.2. Stage 2: Execute the Process

The goal of the study was explained to the participants by the research team meeting with them. All participants were informed of the characteristics of the study in which they were going to collaborate, emphasizing that it was completely voluntary participation and, by request, anonymous.

Then, participants were asked to print the selected part using each of the AM processes in the university facilities. During each session, information about the participant (name, gender, age, and type of user) and the configurations of the 3D printing equipment and pre-processing equipment (predetermined by the providers) were recorded, as well as the criteria to be evaluated.

2.4.3. Stage 3: Evaluate the Criteria

In this stage, the AD methodology was employed, particularly the information axiom to determine the information content for the four alternatives. The main advantage of the information axiom is that it allows decision makers to define the desired characteristics for the considered criteria. In addition, each alternative has its system characteristics [65,66]. On the other hand, the uniform distribution was used for its simplicity, ease of understanding, and compliance with many of the fundamental axioms of probability theory, such as the axiom of independence [67].

The results are presented in

Table 6. Primary information content results.

Alternatives	Imc: Material Cost	Ict: Cycle Time	Iec: Energy Con.	Ier: Error Rate	Ien: Equipment Noise	∑I
FDM Novice	0	0.343	1.169	1.584	1.281	4.379
FDM Advanced	0	0.285	0	1	1.281	2.567 *
SLA Novice	1.432	2.039	0.107	0	0.397	3.976
SLA Advanced	1.432	0	0	1	0.397	2.830

* Is the most suitable in terms of design requirements.

. The option with minimum information content (*) is the most suitable in terms of design requirements. The use of FDM 3D printing equipment by advanced users is marked with an asterisk. Based on the information content of the remaining alternatives, it could be stated that an advanced user requires less information content than a novice, regardless of the AM process used.

Regarding the information content of the error rate (Ier) for an SLA Novice (zero) and an SLA Advanced (one), it indicates that no errors were recorded for novice users, while errors were identified for expert users. This discrepancy could be attributed to the lack of experience and knowledge among novice users, leading to a lower likelihood of error detection. On the other hand, expert users, having a more solid understanding and experience in operating the equipment, might be more aware of potential errors and report them, as reflected in the error rate value of one. Further discussion on this topic will be provided in detail in Section 3.4.

In addition, the opinion of experts was included to derive the relative importance of each criterion using the AHP methodology and thereby estimate a higher level of objective information content for each alternative. For the latter, an instrument was applied using Google Forms that included the following: (1) expert identification data (name, academic background, experience, position, and place of work); (2) a description of the attributes to be evaluated; and (3) pairwise comparisons.

Table 7. Paired comparison of criteria by experts.

Comparison	Expert A	Expert B	Expert C	Expert D	Average
C1–C2	0.333	3	5	5	3.333
C1–C3	0.142	9	9	0	4.619
C1–C4	9	5	7	7	7
C1–C5	9	9	7	9	8.5
C2–C3	0.142	7	9	0	4.119
C2–C4	0.111	9	7	1	4.277
C2–C5	9	9	7	9	8.5
C3–C4	0.142	5	7	1	3.285
C3–C5	0.111	0.111	9	1	2.555
C4–C5	0.111	0.2	9	1	2.577

presents the paired comparisons of the criteria applicable to the study determined by a group of four AM experts.

With the average of each paired comparison, a paired comparison matrix was formed, as shown in

Table 8. Paired comparison matrix.

Criteria	Material Cost	Cycle Time	Energy Consumption	Error Rate	Equipment Noise
Material cost	1	3.333	4.619	7	8.5
Cycle time	0.3	1	4.119	4.277	8.5
Energy con.	0.216	0.242	1	3.285	2.555
Error rate	0.143	0.233	0.304	1	2.577
Equipment noise	0.117	0.117	0.391	0.387	1

. With the data from Table 8, the normalized matrix and vector w, which represents the relative weights of each criterion, were obtained (see

Table 9. Normalized matrix and vector w.

Criteria	Material Cost	Cycle Time	Energy Con.	Error Rate	Equipment Noise	w
Material cost	0.563	0.676	0.443	0.436	0.367	49.8%
Cycle time	0.169	0.203	0.395	0.268	0.367	28%
Energy con.	0.122	0.049	0.095	0.206	0.111	11.7%
Error rate	0.08	0.047	0.029	0.063	0.111	6.6%
Equipment noise	0.066	0.024	0.038	0.024	0.043	3.9%

).

2.4.4. Stage 4: Analyze the Results

The index and consistency ratio of the answers given by the experts in the paired comparisons were estimated. The results are shown in

Table 10. Index and consistency ratio.

Principal Eigenvalue	Consistency Index	Consistency Ratio
5.443	0.111	9.882%

, which suggests an acceptable consistency [52].

The information content for each criterion concerning the different alternatives—taking the priority vector into account—is presented in

Table 11. Final information content results.

Alternatives	Imc: Material Cost	Ict: Cycle Time	Iec: Energy Con.	Ier: Error Rate	Ien: Equipment Noise	∑I
FDM Novice	0	0.096	0.136	0.105	0.05	0.388
FDM Advanced	0	0.08	0	0.066	0.05	0.196 *
SLA Novice	0.713	0.572	0.013	0	0.016	1.313
SLA Advanced	0.713	0	0	0.066	0.016	0.795

* Is the most suitable in terms of design requirements.

. Figure 7 presents these results as a chart to assist in understanding.

2.4.5. Stage 5: Determine the Best Alternative

Considering the relative weight in Table 11, the alternative with the least amount of necessary information is the most suitable in terms of design requirements (*) and is the same as that presented in Table 6.

However, by examining the information content of the other options, we can see that FDM technology requires less information than SLA technology, regardless of the user’s skill level. This observation is reinforced by the fact that FDM is one of the most used commercial technologies for 3D-printed parts using thermoplastic polymers [68].

By evaluating each 3D printing technology (FDM and SLA) concerning the user, it can be determined that a novice requires almost double the information for printing a part than that required by an experienced user. This affirmation aligns with the fact that experts tend to perform at faster speeds, exhibit more accurate motor behavior, and operate with greater efficiency than novices [69]. Additionally, expert users can complete a larger proportion of tasks with higher dimensional accuracy and require less effort and revisions compared to proficient users [70].

After comparing the criteria in pairs and reaching vector w shown in Table 9, it was observed that the priorities in the selection of 3D printing equipment were as follows, according to the expert group: (1) material cost (49.8%); (2) cycle time (28%); (3) energy consumption (11.7%); (4) error rate (6.6%); and (5) equipment noise (3.9%). The priorities in selecting 3D printing equipment were primarily associated with economic indicators, as evidenced by the data presented in Table 9, where material cost and cycle time accounted for the majority percentage (77.8%). This shows a clear priority for economic indicators, considering the lack of importance given to HFs (users).

Nevertheless, the relative weight can vary depending on the user’s experience, the complexity of the part, and the specific application. Therefore, the results obtained may not be applicable to all scenarios and should be interpreted with caution. Further research could explore the impact of varying indicator weights on the evaluation of different 3D printing technologies to provide a more comprehensive and accurate understanding of the benefits and limitations of each process.

3. Results and Discussions

As previously mentioned, the AD methodology—particularly the information axiom—was used in this study to evaluate the four alternatives (FDM novice, FDM advanced, SLA novice, and SLA advanced). The following sections present a comprehensive analysis of the information content results presented in Table 6.

3.1. Material Cost

Firstly, it is important to note that FDM technology is the most economical option in terms of material cost, regardless of whether the user is a novice or advanced. It is worth noting that the use of filament in FDM 3D printers and its profitability with materials such as ABS or nylon has been demonstrated [71].

However, it is important to keep in mind that 3D printing technologies have different strengths and limitations, and the choice of technology should be based on the specific requirements of the application. For example, there is a wide range of resins available on the market for SLA printing, where the user can choose a resin based on their application and the properties that are of most interest to them. These resins include options for general, engineering, manufacturing, automotive, aerospace, dentistry, jewelry, and medical uses [72]. Consequently, there are very specific applications that require the simple and cost-effective fabrication of devices where the possible use of SLA stands out, even when compared to the FDM 3D printing process [73]. Therefore, it is important to thoroughly evaluate the specific requirements of the application and choose a piece of 3D printing technology that best meets those requirements. By doing so, the resulting 3D-printed parts will not only be cost-effective but also of high quality and reliability for the intended application.

The proposed methodology does not consider some factors such as mechanical properties and geometrical accuracy, which play a significant role in determining the quality and suitability of a 3D-printed part for a specific application. Future research could incorporate these factors to improve our understanding of 3D printing and its potential applications.

3.2. Cycle Time

Although SLA technology requires two post-processing tasks (cleaning and curing), it remains the most advantageous option in terms of cycle time. To achieve these results, both AM processes must be operated by users with a high level of experience and skill. This contrasts with what Finnes reported in a comparison of FDM and SLA printing technologies, where—in general—printing the same part with SLA technology took two-to-four times longer than with an FDM 3D printer [74]. Song and Telenko pointed out that FDM technology must wait for preheating periods that change with each printing task, resulting in variability in energy consumption and cycle time [75].

When comparing the limits of each system in Table 4, it can be observed that the Lulzbot Mini has high variability (wide system ranges) in manufacturing times, which reinforces the notion presented by Song and Telenko [75]. Additionally, a loss of efficiency in the 3D FDM printer time was observed. Although Form 2 has a clear technological disadvantage when compared to the Lulzbot Mini [74], it has narrow system ranges, indicating high precision in its cycle times.

It is important to note that the duration of post-processing tasks, such as cleaning and curing, can vary based on several factors. These factors may include the size and complexity of the part, as well as the specific cleaning and curing methods used. As a result, it is crucial to consider not only the printing and post-processing time but also the part’s geometry when evaluating the overall efficiency of different 3D printing technologies. It is, therefore, important to conduct further research on the impact of these factors on the overall efficiency of 3D printing technologies to provide a more accurate evaluation of their suitability for specific applications.

3.3. Energy Consumption

When examining the information content results regarding energy consumption, expert users had better results, regardless of which AM process was used. This result is in accordance with Song and Telenko [75].

Regarding the proper functioning and operation of AM equipment, it was observed that novice users obtained better results when operating the SLA 3D printing technology, followed by expert users when using both AM processes. This result was expected since, as Song and Telenko pointed out, experience does not translate into greater expertise or lower error rates [24].

3.4. Error Rate

After examining the error and doubt logs, it was found that expert users had issues with (1) leveling the 3D printer (which presented serious unevenness) and (2) operating the SLA washing equipment (poor placement of the parts basket). These problems were not reported initially and were caused by poor operation by a previous user. Upon reviewing the historical records of the tests carried out, it was determined that this user was a novice. Therefore, although novice users were able to successfully print a part, due to their lack of knowledge and skills (even after consulting the descriptive videos of each sub-objective), they were prone to ignoring the negative effects of the incorrect operation of the equipment, which could lead them to making important errors during their subsequent use. It is important to consider that the study did not investigate the impact of training or experience on the performance of novice and expert users, which could be a valuable area for future research to further understand the impact of the learning curve on the use of different AM processes.

3.5. Equipment Noise

Finally, it was observed that the SLA 3D printer produced a lower level of noise than its FDM counterpart, regardless of the type of user (novice or expert) that was operating it. This result was expected as all the movements of a 3D FDM printer are controlled by the various stepper motors according to the control program [58]. On the other hand, the SLA AM process uses a stepper motor to control the linear movement of the platform on the Z axis [76]. It is normal to expect stepper motors to generate noise during their operation due to the vibration and rotary movement of their components. The FDM 3D printer uses several stepper motors; hence, it is expected to generate a higher level of noise compared to other equipment that uses fewer motors. However, it is important to note that the level of noise generated by a motor can also depend on other factors, such as the size and type of motor, as well as the conditions under which it is used.

4. Conclusions and Future Work

This paper describes the development of a new method to evaluate and select AM technologies using AD as a scientific basis and the AHP methodology. The proposed framework allows the user to consider equipment conditions, such as efficiency loss over time and the lack of technical information, as well as high variability in cycle time. A novel approach is to also consider human factors like error rate and equipment noise, which are often overlooked in evaluations, without neglecting traditional sustainability criteria such as material cost, cycle time, and energy consumption. Overall, these five aspects provide a good approach to evaluating FDM and SLA 3D printing equipment from a sustainable perspective.

While the presented evaluation criteria may change depending on the specific application, the five factors considered in the proposed methodology are intended to be applicable across a range of potential use cases. The decision to focus on these criteria was based on their potential impact on the overall sustainability and HFs associated with the AM process, as well as their relative ease of measurement and comparison between different technologies and materials. Additionally, by limiting the number of factors considered, the proposed methodology may be more straightforward and accessible to potential users who may not have extensive expertise in the field.

On the other hand, although this study aimed to maintain a sustainable approach in which the proposed indicators were more or less balanced in terms of priority, the experts showed a clear bias towards economic indicators, reflecting the reality that production systems currently face.

For future research, there are multiple alternatives to pursue that could improve the proposed methodology and expand its applicability. Firstly, broadening the evaluation criteria to include additional variables such as environmental impact, recyclability, ergonomics, and occupational health concerns will give a more comprehensive assessment of the sustainability and human factors involved with AM processes. Second, exploring the impact of mechanical characteristics and geometrical precision on the overall applicability and quality of 3D-printed components for various applications may provide useful insights for enhancing component performance. Third, extending the methodology to include other AM methods outside FDM and SLA will require adapting the evaluation criteria and methodology to the specific characteristics of each AM process. Furthermore, a closer examination of user preferences and biases in equipment selection, as well as longitudinal studies to assess the long-term performance and sustainability of selected AM technologies, would contribute to a better understanding of decision-making processes and equipment performance over time. Addressing these areas of future study would improve and broaden the suggested technique, providing more thorough information for evaluating and selecting AM technologies.

Finally, it was demonstrated that the best 3D printing option for a prototyping application for replacement parts is the one that uses FDM technology. Additionally, the user has an important impact on equipment selection, mainly due to their level of skill and experience in using it; in other words, a novice or expert user will define the requirements that they seek to obtain from the manufactured part for its application, such as material properties, print quality, environmental impact, recyclability, effects on individuals, etc., to maximize its benefits in terms of costs, sustainability, and HFs.