Although laser-based additive manufacturing (AM) has enabled unprecedented fabrication of complex parts directly from digital models, broader adoption of the technology remains challenged by insufficient reliability and in-process variations. In pursuit of assuring quality in the selective laser sintering (SLS) AM, this paper builds a modeling and control framework of the key thermodynamic interactions between the laser source and the materials to be processed. First, we develop a three-dimensional finite element simulation to understand the important features of the melt-pool evolution for designing sensing and feedback algorithms. We explore how the temperature field is affected by hatch spacing and thermal properties that are temperature-dependent. Based on high-performance computer simulation and experimentation , we then validate the existence and effect of periodic disturbances induced by the repetitive in-and cross-layer thermomechanical interactions. From there, we identify the system model from the laser power to the melt pool width and build a repetitive control algorithm to greatly attenuate variations of the melt pool geometry. 1 Introduction Different from conventional subtractive machining, additive manufacturing (AM, also called 3D printing) builds up a part from its digital model by adding together materials layer by layer. This paper studies laser-based AM technologies , with a focus on the selective laser sintering (SLS) subcategory. This AM technology applies laser beams as the energy source to melt and join powder materials. A typical workpiece is built from many thousands of thin layers. Within each layer, the laser beam is controlled to follow tra-jectories predefined by the part geometry in a slicing process. After the sintering of one layer is finished, a new thin layer of powder is spread on top, and then another cycle begins. SLS accommodates a broad range of materials (e.g., metals , polymers, and ceramics) and can build customized parts with complex features and high accuracy requirements. Despite the advantages and continuously emerging applications, * Corresponding author broader adoption of the technology remains challenged by insufficient reliability and in-process variations. These variations are induced by, for example, environmental vibrations, powder recycling, imperfect laser-material interactions, and mechanical wears [1-3]. Predictive modeling and process control have thus been key for mitigating the variations and enhancing the energy deposition in SLS. Several existing strategies employ numerical and control-oriented modeling to understand SLS and other laser-based AM processes such as laser metal deposition. In numerical modeling, most researchers adopt finite element analysis (FEA) to investigate thermal fields of the powder bed and substrate, melt pool geometries, and mechanical properties of the printed parts in response of various scanning patterns, scan speeds, number of lasers, and over-hanging structures [4-6]. In control-oriented modeling, current researches often implement low-order system models obtained from system identification techniques, taking laser power or scan speed as the input and melt pool temperature or geometry as the output [2, 7-9]. Furthermore, [8, 10] connect a nonlinear memoryless submodel in series with the linear system model to account for nonlinearities. [9] builds a spatial-domain Hammerstein model to identify the coupled repetitive in-and cross-layer dynamics. The Rosenthal equations give the analytical solutions for a moving laser source in thick and thin plates and have been used to predict the temperature distribution of the powder bed [11-14]. Based on the reduced-order models, existing researches [2, 15, 16] apply PID control to regulate the process parameters and reduce the in-process errors. From there, [17] adds a feedforward path for tracking improvement. Other controllers have also been shown capable in improving the dimensional accuracy of the printed parts, including but not limited to the sliding mode controller [10], predictive controller [7], and iterative learning controller [18]. Note that except for [2], which was developed for SLS, all the other reviewed controllers were tailored for laser metal deposition. Stepping beyond current architectures, this study builds 1 Copyright c by ASME