# Deep Autoencoder Framework for Classifying Damage Mechanisms in Repaired CFRP
## 1 Introduction  
The 21 st century has intensified the global energy crisis, driving the automotive industry towards reducing fuel consumption and CO2 emissions [1, 2]. Lightweight materials have emerged as a critical solution, with several studies highlighting their potential to reduce vehicle weight, thereby enhancing fuel efficiency and minimizing environmental impact [3, 4, 5, 6]. Among these materials, carbon fiber-reinforced plastic (CFRP) composites have gained widespread acclaim for their exceptional strength-to-weight ratio and design flexibility compared to traditional materials such as steel and aluminum [7]. CFRP composites also offer superior corrosion resistance, thermal and electrical insulation, and wear resistance, making them integral to large structural components, including aircraft parts and high-pressure cylinders [8]. In addition to material selection, assembly techniques significantly impact vehicle performance [9, 10]. Traditional joining methods like riveting, while ensuring assembly strength, can increase weight and complicate repairs [11]. Adhesive bonding offers substantial advantages, such as weight reduction, improved stress distribution, and enhanceddamage tolerance [12]. Unlike riveeting, it reduces the stress concentration and enhances the overall durability of joints without significantly affecting the mechanical properties of the adherends [13]. Given these benefits, adhesive bonding has emerged as a crucial joining technology in modern vehicle manufacturing. It is particularly valuable for lightweight vehicle design, allowing for the effective joining of various materials, including light metals, steel, plastics, and fiber-reinforced composites [14; 15]. This adaptability enables manufacturers to meet weight reduction targets while preserving essential vehicle characteristics like structural rigidity, crash performance, and noise-vibration-handling properties [16]. The choice of appropriate adhesives, such as epoxy or polyurethane, along with precise control of bond thickness, is vital for optimizing joint performance [17]. Moreover, the process demands meticulous attention to surface preparation, interfacial coatings, and joint design to ensure long-term reliability [16]. As the automotive industry increasingly focuses on lightweight and energy-efficient structures, adhesive bonding has become integral to multimaterial vehicle construction. It not only facilitates weight reduction but also improves assembly efficiency, thereby contributing to the overall sustainability and performance of modern automotive design [17]. Despite the benefits of adhesive bonding, the detailed analysis of damage evolution in bonded composites remains relatively underexplored. Studies like those conducted by [PERSON] et al. have shown that adhesive joints dissipate energy under stress, resulting in various damage mechanisms such as matrix cracking, fiber/matrix debonding, and fiber breakage [18]. Traditionally, assessing the reliability of these joints has relied on both destructive and nondestructive testing (NDT) methods across different environmental and loading scenarios. The primary goal of these methods is to monitor assembly quality and predict the progression of damage [19]. Among NDT methods, the acoustic emission (AE) technique has demonstrated exceptional effectiveness for real-time monitoring of complex composite structures [20; 21; 22; 23]. AE captures stress wave energy, offering valuable insights into internal structural changes [24]. Additionally, the AE technique can be broadly interpreted to include applications such as counting events or locating them through spatial triangulation. However, the inherent complexity of CFRP composites often leads to internal damage that accumulates without visible surface signs, making assessment challenging [25]. While other methods such as X-ray, ultrasonic, and infrared thermography are employed, AE is distinguished by its capability to monitor damage in real time, providing a critical advantage for ongoing structural health evaluation [26].