The growing complexity of aerospace products, along with strict demands for quality, cost efficiency, and shorter development cycles, has increased the need for intelligent and knowledge-driven manufacturing planning approaches. In this context, model-based engineering and semantic technologies have become key enablers for enhancing decision-making, interoperability, and automation in manufacturing systems. This paper introduces a knowledge-driven framework for intelligent process planning in aerospace sheet metal manufacturing using the models for manufacturing (MfM) methodology, an ontology-based engineering approach that structures and represents manufacturing knowledge through graphical and semantic models. The framework combines product data extracted from 3D CAD models by automated feature recognition software with formalized manufacturing knowledge captured in ontologies, allowing automated reasoning to identify suitable forming processes and define necessary operations and resources. The proposed architecture is organized into multiple layers supporting product definition, knowledge representation, decision support, interoperability, and manufacturing execution, facilitating integration with industrial systems such as product lifecycle management, enterprise resource planning, and manufacturing execution systems. The framework is demonstrated through two case studies of representative aerospace sheet metal components produced by bending and hydroforming. The results show that the MfM-based approach improves knowledge representation, supports automated process planning, and enhances decision-making. By enabling systematic knowledge capture and reuse, the framework promotes greater efficiency, interoperability, and digital integration in aerospace manufacturing.