1. Introduction
Additive manufacturing has received increasing attention as industries have pursued new profit paths through the small volume production of more innovative, customized, and sustainable products with high competitiveness [
1]. Additive manufacturing, defined as the process of building up materials layer by layer to make objects from 3D model data [
2], initially emerged for rapid prototyping to create prototypes in a short time [
3]. Additive manufacturing as a means of rapid prototyping has been extended to rapid manufacturing to take advantage of various materials and the design freedom provided by additive manufacturing [
1,
4,
5]. Nowadays, additive manufacturing is employed for various application areas including patient-specific medical implants [
6], lightweight parts in high-end engineering [
7], artistic devices [
8,
9], and so on.
The emergence of additive manufacturing to replace traditional manufacturing processes has initiated the development of various additive manufacturing techniques. These include fused filament fabrication (FFF), stereolithography (SLA), selective laser sintering (SLS), laminated objective manufacturing (LOM), and three-dimensional printing (3DP) [
10]. Among the various additive manufacturing techniques, FFF has become the most popular method commonly employed in a wide variety of application areas for polymer fabrication due to its cost-effectiveness and technological robustness [
11]. In addition, FFF is able to accommodate various types of polymer-based materials. Common polymers for FFF are acrylonitrile–butadiene–styrene copolymers (ABS), polyamides (PA), polycarbonate (PC), and polylactide (PLA), which are placed at a commodity plastic level with low chemical and mechanical strength [
11,
12]. As the applications of additive manufacturing to advanced engineering and bio-medical devices have arisen simultaneously with the technological evolution of FFF, high-performance polymers such as polyetherimide (PEI) and polyether–ether–ketone (PEEK) have also been considered for FFF [
13].
Carbon fiber reinforced PEEK (CFR-PEEK) is a newly emerging polymer, which is a semi-crystalline thermoplastic and a composite of PEEK with carbon fibers. CFR-PEEK has received a great deal of attention as an alternative material of metal for medical implants due to its high bio-compatibility [
14,
15]. CFR-PEEK provides more bio-compatibility advantages over normal PEEK due to chemical stability, and resistance to prolonged fatigue strain, the reduction in stress shielding and bone resorption, and manufacturability to realize the modulus of bone densities [
16,
17]. With the benefits in bio-mechanical and -chemical aspects, FFF can be more effective to fabricate CFR-PEEK than SLS due to the advantages of FFF in cost-effectiveness and easier material processing [
18].
Despite the potential advantages of FFF for CFR-PEEK, CFR-PEEK has not been sufficiently discussed in the literature relevant to FFF applications. Most existing studies have considered low-end polymers such as PLA and ABS to identify the impact of variable process parameters for FFF mainly on mechanical properties [
13]. Although Li, et al. [
19] addressed the operational aspects of 3D printers, including the manufacturing cost, environmental impact, and surface quality, they focused on the general operational outcomes of PLA and ABS outputs through FFF with fixed process parameters. This research tendency brings the necessity of operational aspects to identify the effectiveness of FFF for CFR-PEEK to enhance the manufacturability of CFR-PEEK in practice. Since the process parameters of FFF that should be pre-determined can significantly affect additive manufacturing results [
20], it is essential for practitioners to be able to determine optimal process parameter values by understanding the underlying trade-offs among various manufacturing performance variables. However, FFF process parameters are often determined in an ad hoc manner, causing unsatisfactory cost, time, and quality during the additive manufacturing process in practice. The negative impacts become even more serious problems for CFR-PEEK applications due to the higher material cost, longer processing time, and greater dimensional accuracy needs than other material applications.
Motivated by the above issues, this study aims to identify the dynamics of key FFF process parameters (i.e., layer thickness, build orientation, and printing speed) for CFR-PEEK on manufacturing performance measures (i.e., printing time, dimensional accuracy, and material cost) that are closely related to manufacturing time, quality, and cost. Herein, different design samples are considered to see whether the optimal combination of the process parameters varies depending on the design types. For each sample type, a design of experiments is repeatedly performed to identify the relationships between the process parameters and the performance measures through the analysis of variance (ANOVA) tests, and then a multiple response optimization model is built to look for the optimal process parameter settings that maximize the overall manufacturing performance. Findings from this study enable additive manufacturing practitioners to better understand the influence of the FFF parameters for CFR-PEEK on additive manufacturing performance that can lead to more cost-effective and reproducible applications using CFR-PEEK.
2. Literature Review
Various reviews relevant to additive manufacturing are available in the literature. For example, printing methods, materials, and recent developments for additive manufacturing are introduced in Wong and Hernandez [
21] and Ngo, et al. [
22]. Survey studies [
23,
24,
25,
26] are also available with a specific focus on application areas such as supply chain, aerospace engineering, dentistry, and medicine. Following the growing interest in additive manufacturing, many studies have investigated FFF and its various applications [
27]. Since the additive manufacturing performance of FFF depends on the selection of process parameters, most studies have performed design of experiment (DOE) methods to investigate the effects of the process parameters on the performance measures of interest [
20].
Table 1 summarizes the additive manufacturing studies for FFF based on DOE analysis. Input parameters commonly addressed in the existing additive manufacturing studies using DOE are layer thickness, build orientation, infill properties, and build temperature [
20]. For the output parameters in the FFF experiments, mechanical properties have been mainly considered to optimize them by controlling the process parameters [
13,
20]. Typical response variables for mechanical properties include tensile strength, flexural strength, comprehensive strength, modulus of elasticity, residual stress bending strength, and angle of displacement [
13,
20]. Only a few studies, however, considered the impact of FFF process parameters on manufacturing performance. Sood, et al. [
28] employed Taguchi’s DOE to investigate the effects of layer thickness, build orientation, raster angle, air gap, and raster width on dimensional accuracy, and they observed that various conflicting factors distinctively affect the dimensional accuracy. Nancharaiah [
29] identified that the highest levels in layer thickness and air gap are statistically significant to minimize printing time. Durgun and Ertan [
30] considered different raster angles and build orientations to examine their effects on surface roughness and showed that build orientation affects the surface roughness more significantly than raster angles.
There are several research gaps in the current studies that should be scrutinized to boost the applicability of FFF in actual practice. Although some macroscopic operational performance measures (e.g., printing time, dimensional accuracy, and production cost) are considered in several studies, most studies in
Table 1 focus on the mechanical properties as output variables. From a manufacturer’s vantage point, operational parameters such as manufacturing cost, printing time, and dimensional accuracy are not ignorable since these parameters can significantly affect the total production cost. For example, the existing studies in
Table 1 mostly disregard the cost factors in analyses, although the manufacturing cost of FFF outputs can be calculated from the material cost and printing time [
19,
30]. Moreover, manufacturing performance tends to be placed as a single performance measure in the existing studies, and therefore possible trade-offs among process parameter settings are not explicitly addressed in the literature. Since multiple input variables can have different effects on outputs in the FFF process [
20], the DOE analysis using critical process parameters for FFF and operational performance measures is required to fully understand the dynamics among the relevant variables.
Furthermore, most materials for FFF examined in the existing studies are low-performance polymers such as ABS, PLA, and PC. There are a few studies relevant to FFF using high-performance polymers such as PEEK and CFR-PEEK, but the process parameters for the materials and their operational aspects have not been sufficiently discussed [
37,
42]. In particular, CFR-PEEK has been pointed out as a very promising material for 3D printing, since it can be used not only for various engineering applications, but also for medical applications due to its sturdy mechanical properties and low biological toxicity [
15,
17,
42,
43]. While the preliminary studies on CFR-PEEK are available in the literature, the breadth and depth of the relevant studies are less comprehensive than that of other common polymer materials.
In response to the above stated shortcomings, this study focuses on the FFF process of CFR-PEEK to identify the relationships between the FFF process parameters and manufacturing performance measures through a full factorial DOE, in which the information loss from the experiments is minimized. For this, the effects of important FFF process parameters (i.e., layer thickness, build orientation, and printing speed) [
20] on the printing time, dimensional accuracy, and material cost for the experiments are investigated, respectively. Moreover, three different designs are considered to confirm whether identified relationships vary depending on design characteristics. Based on the DOE results, the optimal parameter settings considering all the manufacturing performance measures as well as the individual optimal parameter settings for each performance measure are suggested through the methodology proposed in the next section.
5. Conclusions and Discussion
Although many studies investigated the FFF process parameters, the majority of the existing works used common low-performance polymers to observe the effects of process parameters on the mechanical performance of fabricated outputs. Therefore, it has been hard to extract implications for other operational aspects of the FFF process using high-performance polymers. Since high-performance polymers are more expensive and should be carefully treated to be used for FFF, the relationships between the FFF process parameters for CFR-PEEK and manufacturing performance should be understood to achieve successful additive manufacturing operations for CFR-PEEK in practice. In this regard, this study focused on the impact of FFF process parameters for CFR-PEEK on manufacturing performance to investigate their dynamics and optimal parameter settings for different designs. For this, the layer thickness, build orientation, and printing speed were considered as key process parameters for FFF. Then, a full factorial experimental design of the parameter combinations with three replicates was planned for each of the three designs (i.e., ASTM D638, ASTM D695, and ASTM D3039) to measure the printing time, dimensional accuracy, and material cost of the fabricated outputs. The ANOVA results and regression models of each performance measure on the process parameters showed that there are common relationships observed across the three design cases. The minimum printing speed was related to greater layer thickness (0.3 mm), regular horizontal orientation (0°), and faster printing speed (1400 mm/min) in all the design cases. All the design types also had similar parameter effects that lead to the minimum dimensional accuracy at lower layer thickness (0.2 mm), but the 0° build orientation and the 1400 mm/min printing speed were significant parameters only for the ASTM D638 design case that formed a bridge structure at the vertical build orientation. Layer thickness and build orientation were statistically significant for the material cost in all the design cases, and the 0.2 mm layer thickness and the 0° build orientation resulted in the minimum cost.
The findings from this study show that the effects of the process parameters on the manufacturing performance measures are overall similar across the design cases. However, the dimensional accuracy is distinctively affected by the process parameters in the ASTM D638 case, in which the vertical orientation of the design can cause a sagging problem. This indicates that the parameter settings should be carefully determined for a design with complex shapes if the dimensional accuracy of the fabricated part is the most important factor for the additive manufacturing process since various parameters can simultaneously affect dimensional accuracy. Moreover, the optimal parameter settings separately obtained for the individual performance measures reveal that there are trade-offs in the performance measures caused by the layer thickness levels. That is, a greater layer thickness level decreases the printing time due to a decrease in the number of deposited layers, but it negatively affects the dimensional accuracy and material cost by causing over-deposition, due to a decrease in the printing resolution and an increase in the printed volume. However, such trade-offs in the performance measures are not observed for the build orientation and printing speed. This implies that the process parameter determination should be considered as a multi-objective decision-making problem that has conflicting manufacturing performance measures affected by the process parameter settings. Multiple response optimization was performed to consider the above trade-offs in optimal parameter determination, and the 0.2 mm layer thickness, the 0° build orientation, and the 1400 mm/min printing speed were identified as the parameter settings to optimize the overall manufacturing performance.
The manufacturing performance measures of each experiment are displayed in
Figure 12. Since all the performance measures are desired to be minimized, a data point can be optimal as it becomes closer to the right lower corner of the performance space in
Figure 12. The data points in red indicate the manufacturing performance measures of the optimal parameter settings from the multiple response optimization. They show that all three designs can properly achieve the overall manufacturing performance at the same parameter settings; universal parameter settings across designs to optimize the overall manufacturing performance can exist for the FFF process using CFR-PEEK. The optimal parameter settings for the overall manufacturing performance are obtained under the equal importance assumption among the performance measurements. Thus, the optimal settings can be varied if each performance measure has different importance. It indicates the necessity of an appropriate decision-making framework that enables the decision maker to reflect relative importance among performance measurements in finding optimal parameter settings for the overall performance improvement.
The primary contribution of this study is to establish a basis for additive manufacturing capabilities for CFR-PEEK applications from manufacturing performance perspectives. The findings from this study will provide useful information about the optimal parameter settings to enhance the manufacturing performance of fabricated products using CFR-PEEK. The approach of this study attempts a transition of prevailing mechanical performance viewpoints to manufacturing performance viewpoints. Thus, the approach will open potential research opportunities in understanding the complex dynamics among the different manufacturing performance measures and in addressing effective operational methods for additive manufacturing to improve the overall manufacturing performance. Nonetheless, the current study should be extended by considering several issues in future work. First of all, additional designs should be analyzed to generalize the findings of the optimal parameter settings since the current research compares three simple designs. Second, additional FFF process parameters and manufacturing performance measures that are critical for CFR-PEEK applications should be considered along with other advanced composite polymers to fully address the relationships between the process parameters and the manufacturing performance measures. Lastly, both the mechanical performance and manufacturing performance should be investigated together to identify the possible trade-offs between them depending on process parameters. Then, the determination of the optimal parameter settings will be formulated as a more complex decision-making problem in which various trade-offs exist between the mechanical and manufacturing performance.