## 1 Introduction
Despite the work undertaken on optimized exposure strategies [14; 15; 16], antialiasing, and pixel calibration [17; 18], a trade-off exists between achieving the model's detail and robustness. Currently, two methods are most commonly used in the exposure time calibration process:  
* Working curve experiments. This method directly measures the depth of cure as a function of exposure time to determine the required energy for a given layer thickness [19]. This method quantitatively uses the Jacobs model to set the baseline exposure. However, it may not account for the accuracy of selected features, so it is often combined with test models for the final parameter setting. * Calibration test models. This method is based on conducting tests using a specially designed and manufactured model for calibration. These models usually include several small bars, holes, or other patterns that are sensitive to exposure [20; 21]. The result of 3D printing directly indicates whether the exposure is excessive or insufficient. By iterating exposure times and redoing test models, one can arrive at an optimal value at which all models will perform as designed. This systematic trial-and-errormethod with a standardized test model is widely used because it integrates all effects of vertical curing lateral drop, among others, into a single manufacturing process. Resin shrinkage must be considered when using the mSLA method for 3D printing models. Global scale calibration and offset (surface compensation) are the most commonly referenced strategies for compensating for this issue [22; 23; 24]. Both methods measure a fabricated part of known dimensions to identify manufacturing errors. In the global scale calibration method, if the object is consistently smaller by a certain percentage, a global scale factor can be applied in the slicer to enlarge all models accordingly. However, this method may not adequately address errors related to specific geometric features. For instance, objects with thin walls or similar characteristics tend to lose consistent material on each side. Therefore, the literature also discusses methods that adjust for errors based on a percentage and introduce a specific offset in millimeters. The effectiveness of this type of compensation is not perfect and is influenced by the particular kind of slicer used. Moreover, it is essential to carry out the global scale calibration and offset (surface compensation) processes separately for each resin, as different resin formulations shrink to varying degrees. Consequently, researchers often seek solutions through the resin formulation improvements process [25; 26]. This involves creating resins with low-shrinkage monomers or adding fillers to counterbalance the changes in polymerization volume. By doing so, it is possible to reduce scaling errors in the compensation process. Achieving minimal geometric deviations and accurate linear dimensions in mSLA 3D printing is complex. Mathematical models, such as the working curve, help us understand how exposure time and resin properties influence the geometric accuracy of printed models. If fabrication parameters are not correctly established using the mSLA method, significant geometric distortion can occur in the final manufactured models. Integrating theoretical models with empirical calibration is essential to optimize an mSLA 3D printer for producing parts that closely match design dimensions. Therefore, research is needed to develop a model and methodology that enables the calibration of the following parameters:  
* Resin exposure time: Identifying the optimal exposure time to achieve the best 3D print resolution;
* Shrinkage compensation: Adjusting for shrinkage to ensure that the final dimensions and feature positions on the model are as close as possible to the nominal values;
* Internal and external dimension compensation: to ensure that the final dimensions are as close as possible to the nominal values. This approach will help improve the overall accuracy of mSLA-manufactured models.