This paper underscores the critical role of integrating common knowledge across the manufacturing industry by introducing and formalizing a new concept called Manufacturing Commonsense Knowledge (MCSK). Although commonsense knowledge is crucial for enhancing AI-driven operational intelligence and decision-making, its structured application within the broader manufacturing sector has been insufficient. To bridge this gap, we present a structured methodology for translating MCSK into first-order logic (FOL), employing standard ontological frameworks such as the Basic Formal Ontology (BFO), Industrial Ontologies Foundry (IOF), Relations Ontology (RO), and Machine Services Description Language (MSDL). This translation process is pivotal, whether the underlying AI systems employ symbolic, sub-symbolic, or hybrid approaches, as it transforms intuitive MCSK into organized semantic rules. Our findings demonstrate that structured MCSK patterns enhance knowledge representation’s clarity and utility and significantly improve the explanatory capabilities of AI decision-making processes across the industry. The broader impacts of our research extend to enhancing machine interoperability, predictive analytics, and advanced manufacturing practices, thus paving the way for a more informed and efficient industrial future.