AI-Assisted Design for Manufacturability (DFM): A Conceptual Framework for Intelligent Heavy Fabrication Systems in Smart Manufacturing

Abstract

The paper proposes a conceptual framework for the integration of Artificial Intelligence (AI) technologies with Design for Manufacturability (DFM) for heavy fabrication systems with the objective of creating an environment for smart manufacturing. DFM methods and tools have been found to depend significantly on guidelines and expert knowledge based on rules, which are often found to be inadequate for the complexity associated with heavy fabrication systems and assemblies. This paper proposes an AI-based DFM framework for heavy fabrication systems using machine learning and real-time data analytics for improving the process of decision-making regarding manufacturability during the initial stages of the design process. The proposed framework focuses on improving the parameters associated with heavy fabrication processes such as material selection, welding feasibility, structural integrity, costs, and supply chain constraints. In the proposed framework, a multi-layer architecture for DFM integration with AI technologies has been proposed for heavy fabrication systems with feedback for continuous improvement. In addition, the paper explores the challenges associated with the implementation of an AI-based DFM framework for heavy fabrication systems and proposes an interface for bridging the capabilities associated with Industry 4.0 and the human-centric vision for Industry 5.0 for improving the efficiency and productivity of heavy fabrication processes and assemblies through collaborative decision-making involving humans and computers. This paper proposes a framework for improving heavy fabrication systems and assemblies through the integration of AI technologies with DFM for the development of an intelligent heavy fabrication system for improving efficiency and productivity while reducing costs and improving the robustness of designs.