Intelligent product manufacturing cost estimation framework driven by semantic technologies and knowledge-based systems

Accurate cost estimation in the early stages of new product development (NPD) is increasingly challenged by the complexity of aerospace parts and the rapid expansion of manufacturing process knowledge. Traditional cost estimation approaches, often reliant on static models and expert judgment, struggle to adapt and integrate diverse, evolving process data. This paper introduces a novel Knowledge-Based System for Product Manufacturing Cost Estimation (KBS-PMCE) that leverages semantic technologies and natural language processing (NLP) to deliver explainable, extensible cost estimation capabilities. The proposed framework formalizes manufacturing knowledge into structured ontologies, enabling semantic interoperability and dynamic alignment of data sources, including the incorporation of newly defined manufacturing processes. Validation in a case study involving complex aerospace sheet metal (ASM) parts illustrates the framework’s potential to support cost estimation grounded in formalized process equations and reasoning logic. The results highlight the system’s potential to enhance decision-making and interoperability in smart manufacturing environments. Future research will aim to broaden the framework’s applicability across diverse manufacturing sectors and improve semantic alignment automation through the integration of large language models (LLMs).