Parts produced by laser powder bed fusion (LPBF) are susceptible to residual stress, deformation, and other defects that are strongly associated with non-uniform temperature distribution during the printing process. The authors, in their prior work, have proposed SmartScan, an intelligent scan sequence optimization approach that uses purely thermal models to achieve uniform temperature distribution, as an indirect means for achieving reduced residual stress and deformation in parts produced via LPBF. SmartScan was shown to outperform state-of-the-art heuristic scan sequences in reducing residual stress, distortion, and geometric errors in 3D printed parts. However, the thermal-model-only approach to SmartScan fails to account for the complex thermomechanical interactions that map non-uniform temperature distribution to residual stress and distortion in LPBF. This paper presents an initial (2D) investigation into a new version of SmartScan, called SmartScan 2.0, that integrates both thermal and mechanical models to better optimize scan sequences. To achieve this, local temperature gradients are extracted from a thermal model and combined with local compliance information extracted from a mechanical model to create a composite objective function that is optimized using an efficient control-theoretic approach. The effectiveness of SmartScan 2.0 in comparison with the thermal-only version of SmartScan (i.e., SmartScan 1.0) is evaluated in simulations and experiments involving laser marking of thin stainless steel 316L plates clamped in different configurations, as a proxy for 2D layers of parts built using LPBF. While SmartScan 2.0 demonstrated worse thermal uniformity than SmartScan 1.0, it consistently achieved lower maximum deformation of the marked plates (by up to 23.7%) compared to SmartScan 1.0. This supports the well-known fact that better thermal uniformity does not necessarily imply lower maximum deformation in LPBF, and motivates further research into SmartScan 2.0. We also show that, despite its use of thermomechanical models, SmartScan 2.0 is computationally efficient thus facilitating its usefulness in practice.