Nickel-based superalloys are commonly used in applications which require high strength and resistance to creep and oxidation in extreme conditions. All nickel-based superalloys are considered difficult to machine; however, cast gamma-prime-strengthened nickel-based superalloys are more difficult to machine than common nickel-based superalloys. Machining comprises a significant portion of manufacturing processes and with advancements in technology and material properties, the methods and models used must be adapted in order to keep pace.;In this research, correlations are made, using fundamental principles, between measurements made with on-machine touch probes and the cutting tool's wear state, those correlations are used in an adaptive algorithm to estimate the size of the tool wear, and the estimates are used in an updated mechanistic cutting force model to predict the progression of cutting forces in gamma-prime-strengthened Nickel-based superalloys.;This work impacts machining operations on advanced and common materials by developing a tool wear estimation method with readily available equipment and a computationally tractable force model. It influences knowledge in the field through the fundamental relationships, robust adaptive approach, and modifications to the mechanistic force model.;This research shows that on-machine touch probes are able to measure changes in the geometry of a cutting tool as it wears; however, measurement uncertainty results in 20 micrometers of variation in the wear estimation. The wear estimation was improved through the use of a Kalman filter. The average error from 24 estimations was 8 micrometers. Addressing the geometric changes in the tool due to wear, the mechanistic cutting force model estimated the progression of cutting forces with 30% more accuracy than without addressing the tool changes.
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