Digital Twin-Based Intelligent Transformation of an Igniter Production Line

Igniter manufacturing is characterized by complex process routes, stringent quality and safety requirements, multi-variety small-batch production, and the coexistence of manual and automated operations. These characteristics often lead to fragmented production information, insufficient process transparency, weak closed-loop scheduling capability, limited visual monitoring, and inadequate quality traceability and risk assessment. To address these challenges, this study proposes a digital twin-based technical framework for the intelligent transformation of an igniter production line. First, a production-line-level digital twin information architecture is established to enable multi-source data integration, virtual-physical mapping, analytical computation, and application-oriented decision support. Second, five interrelated models are developed, namely the information architecture model, production factor model, process layout model, manufacturing process model, and data application model. These models provide the semantic, structural, and operational basis for full-factor perception, autonomous production planning, resource scheduling, dynamic monitoring, and risk analysis. Third, implementation approaches are presented, including comprehensive factor perception, data service middleware, autonomous planning and scheduling algorithms, a dynamic monitoring and risk analysis platform, and a digital twin interaction platform. The proposed framework enables real-time perception, transparent production control, proactive risk identification, and dynamic optimization of production resources. This work provides a practical and transferable technical route for the intelligent upgrading of igniter production lines and other discrete manufacturing systems with high safety and quality constraints.