## 1 Introduction  
The innovative way of production method operated by additive manufacturing (AM) or 3-D printing takes the manufacturing process in a whole new perspective [1]. With this emerging technology, engineers are up to challenge in transforming their concept on designing a product and at the same time, it is a chance to showcase their creativity in solving modern engineering problems. AM unlocks endless possibilities towards the manufacturing scene; stretching its accessibility even in remote places, while radically reduces manufacturing time, cost, and material waste. Those attributes gained the attention of multi-disciplinary field to adapt this emerging manufacturing technology. For example, in aerospace sector, additively manufactured rocket engine [2], astronauts utilize 3D printers on an in-space manufacturing [3], and even looking at the feasibility in manufacturing food during space flights [4]. Another is the use of AM manufactured scaled model to measure the integrity of building designs [5], printing antenna [6], electrical circuits [7] and revolutionizing modern medicine with tailored bio-printed products [8]. Although there are countless applications of additive manufacturing, there are underlying issues on printing reliability [9] and quality of the fabricated product [10]. For instance, in one type of printing technique called fused deposition modeling (FDM) wherein the object is built layer-by-layer out of melted polylactic acid (PLA), the productsbecame susceptible on having anisotropy in their mechanical structure mainly attributed on overlooking the influence of the printing mechanism to the intrinsic properties of the filament [11]. One of the notable printing parameters is the extrusion temperature whereas due to unstable or extreme temperatures, it drives a disproportionate curing and melting rate of the quasi-solid material that results in poor layer bonding between adjoining layers, prominently reducing the mechanical strength of fabricated products [12; 13], leads to physical deformation [14] and porosity [15; 16]. These mentioned degradation on product quality are still in need of monitoring system [17]. Additionally, aside from affecting the rheological properties of the material, the nozzle temperature also influences the velocity of filament extrusion, a critical factor attributing on nozzle clogging that can cause a printing failure [18; 19]. Aware of this, researchers focus on building monitoring systems and adaptive controls aiming to prevent degradation in product quality or printing failure before it happens. Developments in monitoring system includes techniques like modelling the mechanical aspects of the extruder [20; 21; 22; 23] to further build a digital twin of the printing process adopted by [24]. To predict the filament's temperature, the researchers implemented an algorithmic resolution procedure, and thresholding method, respectively. In predicting the interfacial temperature interaction between the melted PLA and the previous layer a linear Kalman filter estimation is used [25]. A different approach is done in [26] where the thermal state of the nozzle has been estimated using a multiphysics simulation software. These techniques provide precise nozzle temperature predictions however, the mentioned procedures are relatively slow for real-time applications. To reduce these gap, recent studies focus on machine learning-based monitoring system have been developed. There are studies that classifies faulty fabricated products using support vector machines (SVM) like in [27] where a thermal imaging camera are utilized. A similar approach is adopted in [24] as infrared thermal imager acquired the nozzle temperature, and a qualitative trend analysis is used to extract sensitive feature parameters and based on the extracted values, warping on the product is identified using thresholding method.