## 1 Introduction
They innovatively identified the optimal phase and amplitude of the input voltage for the MFC actuators through an incremental domain search. Likewise, [PERSON] et al. [27] explored the application of advanced techniques of 3D-printed biocomposite created from Lygeum sparture fibers and PLA such as digital image correlation and acoustic emission to evaluate mechanical performance during flexural testing. Moreover, [PERSON] et al. [28] presented a new method for creating thermoplastic tubular composites reinforced with carbon fiber, utilizing the Bladder-Assisted Resin Transfer Molding (B-RTM) process. The new impact flexural test showed that these composites outperformed traditional carbon/epoxy tubes, achieving 16.3% higher peak load and 18.9% more major damage energy. Moreover, [PERSON] et al. [29] investigated the acoustic properties of FDM-printed PLA wood composites by examining tensile and flexural strengths based on varying parameters, ultimately finding the optimal conditions for strength. The new acoustic feature developed involved printing micro-perforated panels with different perforation sizes and shapes to analyze their sound transmission loss and absorption characteristics. An acoustic vibration test method has been used by [30] to assess the acoustic performance of fiber-reinforced composites made from hemp and ramie in epoxy. This approach systematically evaluates how factors like impulse, humidity, and thickness influence the composites' acoustic response. Also, [PERSON] et al. [31] introduced a method for detecting structural defects in machine-made parts using acoustic resonance testing and synthetic training data generated through simulations. The novel aspect is the shift from traditional experimental training data to simulated data, enhancing the classification of defective and non-defective components. In the reference [32], the authors examine the acoustic emission method to track the loading and failure mechanisms of carbon/epoxy composites. The new aspect of this study is the assessment of the frequency range of flexural loading concerning the bending behavior of the composite and its acoustic signals. IET is emerging as a powerful non-destructive testing method for additively manufactured (AM) parts. IET also can classify parts manufactured with different process parameters [30] and detect microstructural defect [24]. In this context, there is a lack of explicit focus on defect characterization for polyamide parts and identification of their impact on vibrational and acoustic sensor responses. Therefore, we need to explore where the variation in defect size enhances or diminishes damping, amplitude, and frequency responses. This study focuses on the application of impulse excitation techniques to evaluate defects in FDM-printed polyamide in flexural mode. To achieve this, we created defects inside the piece to analyze their impact on its frequency response and damping using acoustic and vibrational detectors. The vibration detected by the vibration detector is the result of the displacement probe, which precisely measures the microscopic displacement at the needle's point. Meanwhile, acoustic detectors or microphones employ cells to pick up environmental sound waves and record the resulting frequency. Consequently, both detectors analyze the recorded signal to derive resonance frequencies. So, this study intends to experimentally examine the modal responses of FDM-printed polyamide carbon fiber composite components utilizing acoustic and vibration sensors. Specifically, our study intends to investigate how modal responses, such as peak frequency, amplitude, and damping, are affected by the size of internal defects situated along the neutral line axis. Hence, the paper is structured as follows: first, we present the materials, experimental plan, and impulse excitation technique principles used to detect internal defects. Secondly, the experimental results and their implications for frequency and damping assessment for different defect sizes are analyzed. Section 3 summarizes the key findings.