**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 [23–26] 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.


**Table 1.** Summary of additive manufacturing studies for fused filament fabrication (FFF) based on the design of experiment (DOE).


#### **Table 1.** *Cont*.

Acrylonitrile–butadiene–styrene copolymers (ABS); polycarbonate (PC); polylactide (PLA); polyether–ether–ketone (PEEK).

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.

### **3. Methodology**

This section illustrates the principal information of experimental design to identify the impact of the FFF process parameters for CFR-PEEK on the manufacturing performance of different product designs.

#### *3.1. Preparation of Experiments*

Experimental samples were fabricated by Apium P220 [44], which is a FFF-based 3D printer and compatible with a wide range of materials including high-performance polymers such as PEEK and CFR-PEEK. Table 2 summarizes the technical specifications of the machine. TECAPEEK CF30 [45], which has a 1.38 g/cm3 density, 6000 MPa tensile modulus, and 112 MPa tensile strength, was used as the material for the experiments.


**Table 2.** Technical specifications of Apium P220 [44].

The experimental samples used for this study were the three specimen types based on ASTM D638 [46], ASTM D695 [47], and ASTM D3039 [48] (see Figure 1). The standard size of each specimen type was resized to have time efficiency in the experimental runs. Each sample design in Figure 1 was processed as follows: first, a pre-defined computer aided design (CAD) model of each design was created through SolidWorks [49] and then saved to a STL file. Since FFF deposits materials layer by layer, each CAD model needs a slicing process that transforms the designed CAD model into a series of layers to be printed. For this process, Simplify3D version 4.1 [50] was employed to transform each original CAD model into its G-code file which has all the operational commands for the additive manufacturing of the CAD model.

**Figure 1.** Sample designs and their dimensional information used for the experiments (unit: mm). (**a**) ASTM D638; (**b**) ASTM D695; (**c**) ASTM D3039.

Figure 2 shows the examples of the 3D printing outputs simulated by Simplify3D. The areas in purple, blue, blue-green, and orange colors indicate the brim, outer perimeter, inner perimeter, and infill, respectively. Support structures were not generated to eliminate possible effects of support generation on the performance measures considered in experiments. Other process parameters, except for the input parameters, were fixed to the default settings for CFR-PEEK provided by the manufacturer through the parameter configuration of Simplify3D (see Table 3).

**Figure 2.** Sliced models for the experiments. (**a**) ASTM D638; (**b**) ASTM D695; (**c**) ASTM D3039.


**Table 3.** Fixed process parameters.
