The global push toward sustainable manufacturing has intensified pressure on composite industries to reduce energy consumption, minimize material waste, and enable circular economy practices. Composite materials, while critical for lightweight applications in aerospace and automotive sectors, face inherent sustainability challenges due to energy-intensive curing processes, limited recyclability, and fragmented lifecycle data management. Digital twin technology has emerged as a strategic enabler to address these challenges by bridging physical processes with virtual simulations. However, existing digital twin platforms remain inadequate for composite manufacturing due to three systemic gaps: the tension between industrial standardization requirements and material-specific customization needs, the lack of cross-system interoperability frameworks reconciling real-time production data with sustainability metrics, and the absence of secure mechanisms to track lifecycle impacts while preserving commercial data sovereignty. Current digital twin implementations face limitations in adapting to dynamic material properties and hybrid manufacturing approaches such as additive-injection molding combinations. Many industrial deployments rely on fixed behavioral models that do not adequately address process variations caused by equipment degradation, particularly in resin flow dynamics during tooling operations. The integration of multi-vendor systems is further complicated by incompatible IoT communication standards and disconnected data repositories, creating redundant infrastructure requirements that disproportionately burden enterprises with constrained technical capabilities. Regulatory initiatives like the EU Digital Product Passport (DPP) compound these technical barriers by mandating auditable environmental impact documentation for composite materials, while standardized implementation frameworks remain undefined. To address these gaps, this research proposes a unified digital twin framework integrating three innovations: adaptive modeling combining OPC UA-based core ontologies with low-code configurators and Auto Machine Learning-assisted tuning, enabling rapid prototyping of material-aware twins; a RAMI4.0-aligned interoperability engine using Ignition’s User-Defined Types to dynamically translate equipment protocols into Catena-X sustainability indicators; and a blockchain-anchored DPP protocol integrating physics-informed lifecycle assessments and reinforcement learning agents to optimize production under thermodynamic constraints. Anticipated outcomes focus on streamlining digital twin deployment through low-code customization, enhancing cross-system interoperability via semantic data integration, and improving energy efficiency in thermoset curing processes. The framework aims to reduce unplanned downtime by leveraging real-time feedback loops that align process parameters with sustainability objectives. Blockchain-based Digital Product Passports are designed to ensure robust traceability of sustainability data across supply chains while maintaining data integrity. Validation will involve thermodynamic simulations of injection molding processes, pilot deployments on industrial-scale equipment at the Clemson Composites Center, and cross-factory interoperability tests using Catena-X infrastructure. By open-sourcing adaptive Asset Administration Shell templates and cross-platform development tools, the project seeks to lower barriers for SMEs adopting Industry 4.0 technologies, offering cost-effective alternatives to proprietary systems. The framework provides a foundational approach for integrating precision process control with circular economy principles. Potential follow-up topics that will be explored include developing a network of federated digital twins to model the entire composites manufacturing chain, creating semantically enhanced digital twins to bridge design and manufacturing simulation interfaces, and advancing techniques for integrating semantic models with diverse AI-driven workflows. These extensions aim to strengthen connectivity across product lifecycle stages while enabling adaptive intelligence in sustainable manufacturing systems.