## 1 Introduction
However, the printed components often exhibit poor surface finish, dimensional inaccuracies, and anisotropic mechanical properties due to the layer-by-layer nature of the process. To address these limitations, conventional machining processes such as CNC milling, drilling, or sanding are employed as post-processing steps. These methods improve surface quality, enhance dimensional accuracy, and prepare parts for functional or aesthetic applications. Recent studies emphasize the significance of such hybrid approaches. For example, CNC trimming has been shown to significantly enhance the dimensional precision of FFF-printed PLA parts [19]. Additionally, surface roughness improvements via sanding and other subtractive techniques have been documented [20; 21], reinforcing the synergy between FFF and traditional machining in manufacturing workflows. Recent studies include research in the machinability of 3D-printed composites with an emphasis on CFRP materials. [PERSON] al. [22] investigated the machinability of the additively manufactured composite reinforced with short fiber by employing experimental methodologies. [PERSON] et al. [23] worked on the face milling of 3D-printed reinforced PETG, focusing on the surface quality. [PERSON] et al. [24] evaluated the behavior of CFRP materials during laser machining by employing experimental work. A work by [PERSON] et al. [25] presents the cryogenic machining of 3D-printed reinforced thermoplastics through experiments. A number of similar studies utilized numerical and computational methods as well, such as the Finite Element Method (FEM) [26] and machine learning [27]. In light of the above, this study aims to contribute to the field of advanced manufacturing by addressing a critical research gap in the CNC turning of 3D-printed CFRP components, particularly during finishing operations. According to [PERSON] et al. [28], turning is considered to be one of the most applied processes for CFRP finishing. While CFRP machining has been explored in conventional composites, the unique characteristics of additively manufactured CFRP parts remain largely understudied. The literature review conducted clearly identifies a lack of comprehensive studies on this emerging material class, especially under precise machining conditions. A key novelty of the current work lies in the simultaneous evaluation of surface roughness and cutting tool-tip temperature during the finishing of 3D-printed polymer-based composites, a combination that lacks thorough investigation. This dual-response analysis provides a more holistic understanding of the process-performance relationship, revealing the trade-offs between surface integrity and thermal loads at the tool-workpiece interface. The choice of ANFIS modeling further enhances the scientific value of the work. Unlike traditional regression or purely data-driven models, ANFIS offers a synergistic integration of fuzzy logic and artificial neural networks, making it particularly well-suited for systems involving nonlinearities, imprecise data, and multi-factor interactions, which are common features in composite machining. According to [PERSON] and [PERSON] [29], ANFIS, ANN, and similar machine learning techniques are ideal for prediction applications regarding surface quality and optimization processes. Especially ANFIS is a methodology that combines the benefits of both ANN and fuzzy logic, making it particularly useful in prediction modeling. In the work by [PERSON] et al. [30], it is shown that ANFIS performed better compared to ANN and outperformed traditional statistical methods such as the Response Surface Methodology (RSM). This hybrid model enables both transparent rule-based inference and adaptive learning capabilities, which are essential for accurately predicting complex machining outcomes with limited experimental data.