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Ceramic Matrix Composites (CMC) are advanced materials composed of ceramic fibers embedded in a ceramic matrix, resulting in a highly durable and lightweight composite structure offering exceptional high-temperature performance, excellent mechanical properties, and superior resistance to wear and corrosion. CMC find applications in industries such as aerospace, automotive, energy, and defense, where high strength and thermal stability are crucial. Despite their numerous advantages, machining CMC presents unique challenges. The hardness and brittleness of ceramics make them difficult to machine using conventional methods. The abrasive nature of ceramic particles can rapidly wear down cutting tools, leading to decreased tool life and increased costs. Numeric simulations for the machining of CMC are therefore particularly interesting due to their ability to provide insights into tool-material interactions and optimize machining parameters without the need for expensive and time-consuming physical trials. This paper discusses existing methods and approaches from different materials like Carbon Fiber Reinforced Plastics (CFRP) and monolithic ceramics and puts forward an outlook for the numerical simulation of the machining process of CMC.© 2017 Elsevier Inc. All rights reserved.

Ceramic Matrix Composites are an interesting option for high-temperature combustive environments as often encountered in aerospace applications. In the past a lot of research was conducted in order to find the right process parameters for optimal performance of these materials. The mechanical properties of CMCs are vastly dependent on their microstructure. Therefore, a lot of past research focused on finding correlations between process parameters and microstructure of CMCs, most of which was based on empirical trial and error methods.In this paper we use several data-driven, probabilistic machine-learning models to quantify the microstructural composition of C/C–SiC based on the process parameters and choice of raw materials. As a ground truth 123 samples of C/C–SiC with varying process parameters and microstructures were used. The predictive capabilities of the models were demonstrated by the use of the R² metric. By this analysis density in siliconized state as well as open porosity and mass change during siliconization proved to be the parameters with the highest impact on microstructural formation. If siliconization was taken out of the equation the porosity in CFRP state and fiber type were found to be the most influential factors.