A Digital Process Twin for Manufacturing - Bringing the Process Into Focus

This study is currently in progress and will be finished by the end of the year!

The image features a futuristic digital interface titled "Digital Process Twin," displaying various manufacturing processes. In the center, a metallic component, resembling a precision tool, is highlighted against a soft blue background layered with schematic diagrams and icons. On the right side, a CNC milling machine is visually depicted, emitting a sparkling blue aura of digital particles, suggesting active operation. The machine's structure is metallic gray with detailed mechanical elements. The background features blurred lights, adding to the high-tech ambiance. Small icons illustrate steps such as "Milling," "Data Modeling," and "Prototyping," interconnected by arrows and lines in a sleek, modern style. Overall, the composition conveys a sense of advanced manufacturing technology.

A Digital Process Twin for Manufacturing - Bringing the Process Into Focus

Summary

Digital process twins (DPTs) extend asset twins to mirror entire workflows, combining process mining, AI/ML, and simulation for end-to-end optimization. A CNC milling demonstrator integrates CAD/CAM data, real-time sensor streams, and physics models to run live parallel simulations, enabling virtual sensing and proactive control. Key benefits include predictive tool-wear maintenance, real-time chatter mitigation, deformation compensation, higher quality, and reduced time and cost. Adoption is limited by interoperability, computational load, investment, skills, and cybersecurity, but phased pilots, a single digital master, upskilling, strong data governance, and open standards can scale DPTs toward autonomous, self-optimizing operations.


Topic Fields	Digital Twin
Published	2025
Involved Institutes	IPT
Project Type	ICNAP Community Study
Result Type	Report
Responsibles	André Gilerson

Description

The digital process twin (DPT) for CNC milling serves to mirror and optimize end-to-end machining workflows by combining process mining, physics-based simulation, AI/ML analytics, and what-if experimentation. Its core functionality includes continuous ingestion of CAD/CAM plans and real-time IoT telemetry, execution of live parallel simulations for virtual sensing of difficult-to-measure variables such as cutting forces and tool–chip temperatures, anomaly prediction and detection, chatter mitigation via real-time parameter recommendations, and deformation compensation using FEM-based path correction. A modular, microservices architecture supports an event-driven pipeline: CAM toolpaths (ISO 6983 G-code or ISO 14649 STEP-NC) and design data (ISO 10303 STEP/AP242) populate a digital master; machine and process events arrive via OPC UA or MTConnect and MQTT/AMQP; a physics engine (e.g., Dystamill with reduced-order FEM and surrogate models) calibrates parameters online; AI services perform diagnostics and RUL estimation; and decision outputs are exposed to MES and CNC controllers through OPC UA, REST, or gRPC.

Deployment follows a hybrid edge–cloud model. Latency-critical services (stream preprocessing, control recommendations, safety interlocks) run on an industrial edge IPC; batch analytics, model training, and historical process mining run in the cloud. Containerization and orchestration (Docker/Kubernetes) enable horizontal scaling across machines and lines. Target users include manufacturing engineers, NC programmers, operators, and maintenance planners, with role-based interfaces and 3D visualization for situational awareness. Performance considerations include sub-second closed-loop inference (<250 ms), GPU-accelerated simulation where required, time-series storage (e.g., InfluxDB/TimescaleDB), and schema governance via a shared information model or Asset Administration Shell.

Security and compliance adopt TLS/mTLS, RBAC, secrets management, audit logging, and IEC 62443-aligned hardening. Constraints include brownfield interoperability, high computational demand for high-fidelity models, data quality requirements, and controller write-back limits. The system scales by partitioning by asset, using a message backbone (e.g., Kafka), and a schema registry. External integrations cover MES/ERP/PLM/CMMS, controller APIs, and standards-based connectors to support virtual commissioning and in-process control.

Get in touch