Microstructure-sensitive thermomechanical modelling enables predictive control of residual stress in manufacturing processes and deformation mechanisms in advanced alloys such as high-entropy alloys (HEAs). This allows mechanistic optimization rather than empirical trial-and-error approaches. We review mechanisms and results obtained using microstructure-sensitive thermomechanical models for manufacturing optimization, focusing on linking manufacturing routes, microstructure, and material properties. Residual stress and microstructure evolution arise from coupled thermal and mechanical histories governed by parameters such as heat input and welding speed for welding and additive manufacturing. At lower length scales, controlling strength and ductility requires bottom-up models sensitive to multiscale plasticity mechanisms. These include temperature-dependent dislocation motion and stacking fault energy (SFE), which plays a key role in deformation behavior. Multi-principal compositions in HEAs, such as the popular FCC Cantor alloy (CrMnFeCoNi), exhibit high strength and ductility required in extreme environments in nuclear and aerospace. HEAs enable stable solid solutions with distorted crystal lattices, hindering dislocation motion. HEAs can exhibit low hydrogen diffusivity, retaining most of their initial ductility after hydrogen exposure compared to steels [1]. In some cases, hydrogen can even enhance both strength [2] and ductility [3]. The resistance to hydrogen embrittlement in HEAs is attributed to deformation mechanisms at the microscale, including twinning, stacking faults, and the activity of Shockley partial dislocations [4]. Unlike traditional alloys, HEAs allow for tunable SFE via composition, with lower SFE increasing deformation twin density. Our recent physically-based multiscale models, SFE-sensitive, have shown potential for predicting both stress–strain response and dislocation density evolution [5]. [1] Cavaliere et al. Hydrogen Embrittlement in Metals and Alloys, 681–728. Springer, 2025. [2] Mengkai Cui et al. International Journal of Hydrogen Energy, 106:1275–1284, 2025. [3] Hong Luo etal. Scientific reports, 7(1):9892, 2017. [4] ZiJiao Zhang et al. Nature communications, 6(1):10143, 2015. [5] Gonzalez et al. Computational Materials Science, 249:113565, 2025.