Free-form surfaces are present in key industries such as aerospace, automotive, and energy, fulfilling both aesthetic and functional requirements. Ball-end milling is preferred for manufacturing due to its high efficiency and precision. However, the large number of variables makes the process challenging and not fully understood. The intermittent cutting characteristics, along with changes in tool-workpiece contact and variations in effective cutting speed, are some of the difficult aspects to predict that significantly impact surface quality and part accuracy. Additionally, depending on the tool orientation, the transition between ploughing and shearing as the predominant mechanisms can occur, with the transitional zone not being well understood. This research proposes the development of a robust methodology to integrate predictive mathematical models into commercial CAD/CAM software, enhancing the precision of ball-end milling of free-form geometries. The project will focus on understanding the cutting phenomena, particularly the transition between shearing and ploughing, and implementing cutter workpiece engagement (CWE), and the cutting forces within the CAM software environment for finishing operations with ball-end mills. The expected outcomes include new insights into the milling process, a robust methodology for modelling outputs, and the development of an API capable of predicting machining outcomes. This will provide tools for improving programming efficiency, reducing waste, and aligning with sustainability goals, thereby enhancing the academic understanding of the process and the competitiveness of the industry.

The challenge of climate change poses a significant threat to humanity. It is essential to prioritise renewable and cost-effective energy sources while simultaneously reducing greenhouse gas emissions for the benefit of future generations. One of the primary solutions lies in the new generation of larger, segmented and more efficient wind blades. The REVOLUTION_WIND project aims to enhance the circularity and damage tolerance of the new generation of larger and segmented wind blades by embedding a multifunctional self-sensing 3D printed structure within a reversible adhesive layer. The project will use a combination of supervised machine learning and experimental characterisation to devise a multi-modal monitoring system that can accurately predict the remaining useful life of the reversible adhesively bonded joints. The most cutting-edge outcome of REVOLUTION_WIND is a multifunctional 3D printed self-sensing structure that will work as an embedded reinforcement and generate input data for a machine learning framework for in-situ life assessment of the structural integrity of reversible adhesively bonded joints. Finally, the successful implementation of REVOLUTION_WIND will contribute to achieving the European Green Deal's objective of establishing wind power as Europe's primary energy source while promoting a circular economy.