Data-Driven Additive Manufacturing Process Monitoring, Modelling and Optimisation

TF-CC provides a specialised service for the monitoring, modelling and optimisation of Additive Manufacturing (AM) processes through data-driven and physics-informed approaches. The service typically lasts 6–10 weeks, depending on process complexity, data availability, and system maturity. It targets startups, SMEs, research teams and industrial users developing or deploying AM technologies (e.g. powder bed fusion, directed energy deposition) for the industrialization of their novel product, process or service, across sectors such as aerospace, automotive, medical devices and advanced manufacturing. The goal is to enhance process stability, part quality, and production efficiency by integrating in-situ monitoring, predictive modelling and optimisation strategies. The service is delivered in four phases: 1. Initial assessment of AM process, equipment, materials, and available data.  2. Monitoring strategy design, including sensor selection, data acquisition architecture and key process indicators.  3. Process modelling, combining data-driven methods (AI/ML) and physics-based models for defect prediction, quality estimation and process understanding.  4. Optimisation and validation, defining parameter tuning strategies, control approaches and performance metrics.  Activities include expert consultations, data analysis, model development, simulation, and validation through pilot cases or experimental datasets. The expected outcome is a validated framework (pre-industrial set-up) for AM process monitoring and optimisation, including predictive models, improved parameter sets, and a clear implementation roadmap. The service is delivered through a combination of remote collaboration, technical workshops, and optional access to TF-CC’s experimental and digital infrastructure