## 2 Experimental Method  
### Taguchi Method  
The Taguchi method is a low-cost, high-efficiency quality engineering method that emphasizes that product quality is improved not through inspection but through design. In the product design process, the Taguchi method uses the functional relationship of quality, cost, and benefit to develop high-quality products at low cost.  
In this study, the modified Taguchi method of [PERSON] was used [31]. The evaluation method of fuzzy set theory and correlation matrix was combined to establish a factor selection model for the selection of Taguchi method factors. The three evaluation indicators of severity (\(S\)), detection degree (\(D\)), and controllability (\(C\)) of the factors were considered comprehensively, and the corresponding fuzzy linguistic term sets established by [PERSON] were referred to. Finally, they are quantified using fuzzy numbers, which are determined based on the recommendations of published articles in the field and the experience of practical experiments. The fuzzy language term description level is divided into five levels, and the corresponding fuzzy numbers are from 1 to 5 points, respectively. The language terminology is shown in Table 1.  
The formula for assessing the importance of an impact factor \(X_{i}\) by using the correlation matrix method is as follows:  
\[V_{i}=W_{S}V_{iS}+W_{D}V_{iD}+W_{C}V_{iC} \tag{1}\]  
In the equation, \(X_{i}\) (\(i\) = 1, 2, \(\ldots\), \(n\)) represents the impact factor to be evaluated. \(W_{S}\), \(W_{D}\), and \(W_{C}\) represent the weights of severity (\(S\)), detection (\(D\)), and controllability (\(C\)). \(V_{ij}\) represents the fuzzy number of each dimension under different impact factors, (\(i\) = 1, 2, \(\ldots\), \(n\); \(j\) is severity, detection, and controllability). \(V_{i}\) represents the combined assessment value of the impact factor \(X_{i}\) under the three dimensions of severity (\(S\)), detection (\(D\)), and controllability (\(C\)). The higher the value of \(V_{i}\), the higher the importance of the factor.  
The factors of the research were first selected by using the Taguchi method. Where the fuzzy number was determined based on the recommendations of published articles in the field and the experience of the actual experiments. The specific scores obtained by referring to the language terminology table for different print parameters are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Level** & **Severity (S)** & **Detection Degree (\(D\))** & **Controllability (\(C\))** & **Fuzzy Number** \\ \hline \multirow{2}{*}{Very low} & Almost zero effect on & The pattern and magnitude of & Almost no human & \\  & output items & impact is almost impossible to & control is possible & 1 \\ \hline \multirow{2}{*}{low} & Slight impact on & Low probability of detecting & Human control & \\  & output items & impact patterns and sizes & is difficult & 2 \\ \hline \multirow{2}{*}{average} & Affects product appearance & Probability of impact pattern & Human control is & \\  & and some functions & and size detection in general & possible, but additional & 3 \\  & & & costs required & \\ \hline \multirow{2}{*}{high} & High impact on product use, & & & \\  & function; customer & & & \\  & dissatisfaction & & be detected & \\ \hline \multirow{2}{*}{very high} & Serious impact on product & & & \\  & use, function, and even & & & \\  & endanger personal safety & & & \\ \hline \end{tabular}
\end{table}
Table 1: The language terminology.  
According to the influence factor assessment correlation matrix table, combined with the purpose of this paper, the printing temperature, layer thickness, and printing speed of the reinforced fiber were selected as the study process parameters. Initially selected parameter ranges are reinforced fiber print temperature 190-230 \({}^{\circ}\)C, reinforced fiber print layer thickness 0.2-0.4 mm, and reinforced fiber print speed 120-400 mm/min.