SUBJECT: Ph.D. Proposal Presentation
BY: Zhipeng Pan
TIME: Wednesday, July 20, 2016, 2:00 p.m.
PLACE: MARC Building, 201
TITLE: Predictive Modeling for Material Microstructure Affected Machining
COMMITTEE: Steven Y. Liang, Chair (ME)
Hamid Garmestani (MSE)
Donald S. Shih (Boeing Research & Technology)
Christopher Saldana (ME)
Thomas Kurfess (ME)


The surface quality, structural integrity of the end product strongly depends on the manufacturing process. Appropriate selection of the manufacturing process parameters could help to improve the overall service functionality of the end product. In a typical machining process, the machined workpiece surface residual stress profile, microhardness and strength are the key factors to be investigated. The material mechanical properties in the machining process strongly depends on the thermal mechanical loading and material microstructural states. The microstructural evolution path is influenced by thermal-mechanical loading history. Previous research mainly focuses on the thermal mechanical interactions. How the material microstructural states influence the machining process stays largely unknown. Therefore, it is of essential importance to develop a comprehensive model that correlates the machining configuration, initial material microstructural and mechanical properties to the residual stress, microhardness outcomes. The goal of the current study is to model the microstructural evolution phenomenon affected machining process. This model helps to understand the correlations among the machining process parameters, initial material microstructural attributes and mechanical properties.
For the start of the research, the Johnson-Mehl-Avrami-Kolmogorov (JMAK) is proposed for the dynamic recrystallization and grain size evolution. The Avrami model and time-temperature-transformation (TTT) curve are proposed for the calculation of phase transformation in the machining. A microstructure sensitive flow stress model by including the microstructural attributes, such as grain size, volume fraction of different phases, and thermal mechanical loading conditions is proposed.

Additionally, the proposed model will be extended to a more advanced laser assisted milling (LAM) process. A 3D model is developed for the temperature distribution prediction induced by laser preheating. Taking the preheating temperature field as initial input, an analytical model is proposed for the end milling process. The analytical model is validated by the milling forces comparison with experimental measurement in the application of IN718 material. The microstructural evolution will be investigated in the LAM process. Analytical grain size evolution and phase transformation model will be proposed.