The need for machining advanced materials has increased exponentially in recent years. Ni-based alloys, Ti-based alloys or some steel grades are commonly used in transport, energy generation or biomedicine industries due to their excellent properties that combine hardness, high temperature strength and corrosion resistance. These desirable properties make such alloys extremely difficult to machine, inducing a quick cutting tool wear that must be overcome. In the last decade, cryogenic machining has emerged in order to improve the machining of these materials. By means of cryogenic fluids such as cutting coolants, significant improvements in the life of cutting tools are obtained. However, most studies on this new technology are focused on turning processes, because of the difficulty of introducing cryogenic fluids through a rotary tool in processes such as drilling and milling. In this study, a cryogenic milling system integrated within the tool holder is used for milling Gamma TiAl, Inconel 718 and grade EA1N steel using carbon dioxide as a coolant. This system has been compared with the traditional cooling method (emulsion) in terms of tool life to check if it is possible to improve the machining operation in terms of efficiency by supplying the cryogenic coolant directly to the cutting zone. The results show that by replacing traditional pollutant cooling fluids with other more ecologically-friendly alternatives, it is possible to improve tool life by 100% and 175% in the cases of Gamma TiAl and grade EA1N steel, respectively, when using the new delivery system for the coolant.

Zero-Defect Manufacturing (ZDM) is a promising strategy for reducing errors in industrial processes, aligned with Industry 4.0 and digitalization, aiming to carry out processes correctly the first time. ZDM relies on digital tools, notably Artificial Intelligence (AI), to predict and prevent issues at both product and process levels. This study’s goal is to significantly reduce errors in machining large parts. It utilizes data from process models and in situ monitoring for AI-driven predictions. AI algorithms anticipate part deformation based on manufacturing data. Mechanistic models simulate milling processes, calculating tool deflection from cutting forces and assessing geometric and dimensional errors. Process monitoring provides real-time data to the models during execution. The research focuses on a high-value component from the oil and gas industry, serving as a test piece to predict geometric errors in machining based on the deviation of cutting forces using AI techniques. Specifically, an AISI 1095 steel forged flange, intentionally misaligned to introduce error, undergoes multiple milling operations, including 3-axis roughing and 5-axis finishing, with 3D scans after each stage to monitor progress and deviations. The work concludes that Support Vector Machine algorithms provide accurate results for the estimation of geometric errors from the machining forces.