## 1 Introduction  
Additive Manufacturing (AM), also known as 3D printing, refers to a form of fabricating three-dimensional objects, where materials are deposited layer-by-layer to ensure even complex shapes. Due to this characteristic, AM has become a major source of paradigm shifts, as seen in various industries. From small and simple tools to industries that require huge sizes and high reliability, such as the aerospace, energy, engine, and biomedical industries, 3D printing technology is gradually expanding [1; 2; 3; 4]. Despite the various potentials of 3D printing, some defects, such as thermal deformations and geometrical errors, have been major hindrances in AM processes. Such defects cause unnecessary time and cost increases in many manufacturing processes, crucially reducing the efficiency of the manufacturing industry. Therefore, defect minimization and detection, including early detection, are vital regarding increasing the efficiency and reliability of AM processes. Since it is very important to ensure high reliability and quality in manufacturing, most studies have so far focused on finding optimal process parameters to minimize defects such as thermal deformations and geometrical errors [5; 6; 7; 8; 9; 10; 11]. [PERSON] et al. [5] aimed at optimizing process parameters using fused deposition modeling (FDM). The analysis of variance was also used to study the effect of three main parameters: raster angle, raster width, and raster to raster gap. [PERSON] et al. [7] experimentally documented the influence of some process parameters on the part quality and micro property in selective laser melting. In this research, the effect of each process parameter on the quality and defect formation in manufacturing processes was described in detail. Although optimizing process parameters improves the overall quality of manufacturing, thus reducing the number of defects, it is still important to monitor the remaining defects. That is, in addition to parameter optimization, accurate defect early detection must also be performed. There are many studies regarding in-situ monitoring and early detection, and they have been the focus of conventional statistical methods [12; 13]. [PERSON], [PERSON], and [PERSON] [12] attempted to establish a sensor-based monitoring system for the conditions of machines to increase part quality. Generally, the use of acoustic emission sensors and hidden semi-Markov models serves as a diagnostic tool of FDM processes. Recently, many studies have used machine learning (ML) techniques to detect defects using process parameter information [14; 15; 16; 17; 18]. [PERSON] and [PERSON] [18] presented that the use of artificial neural networks and support vector regression can facilitate the prediction accuracy of many models. In this study, the analysis of the relationship between product geometry, which is affected by defects, and process parameters were described. One of the factors that greatly affect the product geometry is warping deformation, which refers to the phenomenon in which a fabricated part is detached from a built platform. Such deformations usually start from corners, as this defect usually occurs due to thermal residual stresses. Most of these thermal defects, such as warping deformation, determine the final quality and are mainly caused by process parameters. However, it is not easy to forecast warping deformation using only process parameters, even with ML techniques. This is because it is not only difficult to directly identify how each process parameter affects the results but also because many input variables and input data are needed for ML. Unlike prior research that has mainly focused on the relationship between process parameters and defect forecasting, this study aimed at predicting the defects of thermal time series data using in-situ monitoring. In addition, this study has focused on thermal deformation, especially the warping deformation in FDM, by analyzing thermal time-series patterns.