The world is facing another revolution in the industry. The connection of the cyber-physics systems considers the digital twin as one part of the concept. As residual stress state is induced by the manufacturing chain, it is crucial to have a method to comprehend its real effect for the manufactured component. Incipient qualitative studies were conducted with the main goal of optimizing the manufacturing chain to achieve ideal residual stress (RS) state at the end of the production. To expand the state-of-the-art towards a quantitative result, the objective of this thesis is the prediction of manufacturing process' interaction on a whole tooth of a spur gear. This oriented development of the manufacturing chain for a specific residual stress state consolidates the bases of the concept of Design of Residual Stress (DRS). Gear is one of the mechanical components mostly influenced by residual stresses that come from the combination of a complex load stress state generated by the involute profile and a manufacturing chain with a wide range of processes. The focus of interest of this thesis was the interaction between the shot peening and grinding processes. The approach proposed to achieve the numerical prediction of the interaction was divided into three parts: guaranteeing whether the stress state generated by the manufacturing process is indeed residual, the union of the manufacturing processes in the same predictive model, and the investigation of how to observe the interaction predictively. As a key aspect, the strain energy instability was found as the parameter which represents directly the interaction among the manufacturing processes. The endorsement of this final observation was made by comparison with experimental investigations done with the simulated manufacturing chain. The expansion from the experimental observation to the prediction made this method reliable to be applied as a part of a digital twin for a mechanical component.