Predictive modeling for material microstructure affected machining
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The precision machining has long been focused on the machined part geometrical consideration, such as dimensional accuracy and surface roughness. The machined part dimensional accuracy is strongly influenced by the tool wear and tool deflection. The surface roughness is a function of process parameters, such as cutting speed, feed rate, and tool geometry. With the rapid development of precision machining technology, the manufacturing end-product functionality requires the precision machining capability beyond machined part geometrical properties. In additional to the mechanical states, the material microstructure attributes are closely related to the surface functionalities. The microstructure consideration in the machining process covers phase transformation, dynamic recrystallization, grain morphology and dislocation density. The material microstructure evolution in the machining process is a combined effect from the thermal-mechanical interactions. In addition, the material microstructure properties would inversely affect the material mechanical properties and heat generation in the machining processes. The current work aims to bring out a computational framework to assist the machining process design and optimization, which outputs the machined end-product microstructure states related surface integrity properties. The model would need a material microstructure structural evolution model, explicit correlation of the material mechanical properties with material microstructural states. This thesis concludes from the current state of the art research in machining with a consideration of the material microstructure properties, brings out the material microstructure affected machining framework. Alternatively, the precision machining also includes the precise control the machined part dimensional accuracy, improved residual stress states and machined parts surface integrity. For the hard to machine material, especially Nickel based super alloy, advanced machining process would be required to solve the state of art challenges. An novel thermally enhanced machining system is developed with improved machining efficiency and reduce energy cost. A novel co-axial laser-assisted milling system is developed, specifically for the hard-to-machine material with high energy efficiency and large material removal rate. A comprehensive numerical based laser-assisted milling model is proposed for the process simulation and optimization.