Wire-arc additive manufacturing (WAAM) simulations are critical for predicting material properties, layer morphology, and defect formation, thereby optimizing production while reducing the need for costly experiments. However, the complex multiphysics and multi-scale nature of WAAM make the entire process simulation computationally expensive [1]. Consequently, existing studies focus either on simplified thermo-fluidic behavior for layer morphology or simplified thermo-mechanical analysis for residual stress and distortion, but neither captures both interlinked aspects simultaneously [2]. Moreover, most models assume planar deposition, whereas multi-axis WAAM allows for nonplanar deposition, which remains largely unexplored. To address these limitations, we propose a part-scale novel additive manufacturing (AM) simulation using the hybrid particle and grid-based material discretization, Material Point Method (MPM), which efficiently handles large deformations [3]. In our modeling framework, material deposition through particles carries state-dependent properties. A transient thermal simulation captures heating and cooling cycles, providing the thermal history of the particles. The thermal history determines the material state and the constitutive material behavior and induces thermal stresses within layers. Shear and bulk moduli in the constitutive material model are dynamically modified based on temperature, enabling viscous flow during heating and restoring elastic solid behavior during cooling. The background grid in MPM computes gradients of temperature, rate of deformation, stress, and strain fields that are interpolated from the particles, facilitating smooth phase transitions and stable particle advection. This AM simulation approach is used to simulate different deposition strategies on thin-walled structures. A parametric study examines the effects of temperature and material properties on layer morphology and residual stress development for various deposition strategies and process parameters. The simplified multiphysics and hybrid material discretization method strikes a practical balance between accuracy and computational efficiency.