Cutting force is a fundamental parameter determining productivity and quality ofthe end milling operation. The development of a generic cutting force model forend milling operation requires a large number of machining experimentsfollowed by determining empirical relationships between process parameterswith cutting force. This thesis presents a novel approach combining Mechanisticmodel and supervised ANN model to predict instantaneous cutting force variationduring the end milling operation. The approach proposes training of an ANNmodel using datasets generated from the Mechanistic force model instead ofusing experimental data. The proposed approach can generate a large number ofdatasets for the training of an ANN model without conducting rigorousexperimentation. The thesis work explores different ANN architecturesanddetermines an appropriate network by comparing a set of performanceparameters. A series of end milling experiments are conducted to examine theefficacy of the proposed approach in predicting cutting forces over a wide rangeof cutting conditions. Subsequently, the application of modern machine learningmodels such as Long Short Term Memory (LSTM) is explored to detect processparameters from the experimentally measured cutting force data. The studyuses a multi-layered LSTM to map measured cutting force data (input sequence)to a vector of end milling process parameters (fixed dimensionality labelledoutput vector). The dataset for the training of an LSTM model is generated usingthe Mechanistic force model. The testing of trained LSTM model is carried out toidentify process parameters from experimentally measured cutting force data. Ithas been observed that the proposed model can detect cutting conditions quiteeffectively for CNC end milling.KeywordsMachining; End milling; Cutting forces; Mechanistic Model; Machine Learning