## 1 Introduction
### Literature Review  
To optimize sheet metal forming under the initial ideal conditions, numerous studies have been performed to develop different approaches and determine the optimal conditions by combining finite element analysis (FEA) and design of experiments (DOEs). [PERSON] et al. [5] predicted the optimized process parameter values based on the friction coefficient and material properties using a surrogate model to predict the material and process parameters in deep drawing. [PERSON] et al. [6] analyzed the effects of blank holding force and drawbacks on the forming quality of parts with complex geometry through FEA and presented optimal forming conditions that can minimize the defects, such as wrinkling and tearing. [PERSON] et al. [7] determined the design parameters of drawbacks that control and minimize internal wrinkles using DOE to optimize drawbacks in the hydrodynamic deep drawing process. However, previous studies have limitations in that, in case of constraint changes at the initial stage, the parameters have to be optimized again, and such changes cannot be reflected in real time during the process.  
Therefore, machine learning models were introduced to address these limitations, and approaches were developed for real-time learning and prediction of complex process parameters based on analysis and experimental data. [PERSON] et al. [8] predicted and optimized the effects of drawbacks and friction conditions on the forming quality in sheet metal forming using artificial neural networks (ANNs). [PERSON] et al. [9] realized a more precise formability assessment based on the sheet metal thickness, material properties, and forming conditions in deep drawing through classification and regression models. [PERSON] et al. [10] developed a method to learn the forming quality based on the parameters of the segmented blank holder in the stamping process through ANN and genetic algorithms and optimized the forming process by exploring optimal process conditions.  
Although these studies optimized the forming process parameters (such as drawbacks) using AI, they cannot reflect parameter changes in the prediction of results in real time because they use deterministic optimization methods, which assumes that all the design variables and parameters are specific. As such, research has been performed to predict the results in real time according to the changes in variables based on a digital twin (DT). [PERSON] et al. [11] applied task incremental learning to predict the final geometry, physical properties, and quality evaluation of the product according to the process conditions and material properties of the customized metal forming product based on DT and reduced the time required for re-learning using the existing model as a benchmark to update the model when new tasks or data are added. [PERSON] et al. [12] predicted the condition of the machine, the maintenance time, and the possibility of failure using machine sensor data and production-related data in the sheet metal bending process. They performed DT using ANNs and optimized the machine operation time and reduced failures. [PERSON] et al. [13] developed a model that can predict the quality indicators and friction condition through the data collected in real time during the sheet metal forming process. They also implemented a system that performs real-time monitoring through DT and maintains quality by adjusting the friction condition using a Random Forest model. DT technology, however, has limited applications, owing to the difficulty in data collection and processing, as well as various limitations, such as high initial costs.