In-Situ Calibrated Modeling of Residual Stresses Induced in Machining under Various Cooling and Lubricating Environments
Although many functional characteristics, such as fatigue life and damage resistance depend on residual stresses, there are currently no industrially viable ‘Digital Process Twin’ models (DPTs) capable of efficiently and quickly predicting machining-induced stresses. By leveraging advances in ultra-high-speed in-situ experimental characterization of machining and finishing processes under plane strain (orthogonal/2D) conditions, we have developed a set of physics-based semi-analytical models to predict residual stress evolution in light of the extreme gradients of stress, strain and temperature, which are unique to these thermo-mechanical processes. Initial validation trials of this novel paradigm were carried out in Ti-6Al4V and AISI 4340 alloy steel. A variety dry, cryogenically cooled and oil lubricated conditions were evaluated to determine the model’s ability to capture the tribological changes induced due to lubrication and cooling. The preliminarily calibrated and validated model exhibited an average correlation of better than 20% between the predicted stresses and experimental data, with calculation times of less than a second. Based on such fast-acting DPTs, the authors envision future capabilities in pro-active surface engineering of advanced structural components (e.g., turbine blades).
- Research Organization:
- Univ. of Kentucky, Lexington, KY (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Manufacturing Office
- Grant/Contract Number:
- EE0009121/0000; EE0009121; FOA-0001980
- OSTI ID:
- 1769178
- Alternate ID(s):
- OSTI ID: 1770586
- Journal Information:
- Lubricants, Journal Name: Lubricants Vol. 9 Journal Issue: 3; ISSN 2075-4442
- Publisher:
- MDPICopyright Statement
- Country of Publication:
- Switzerland
- Language:
- English
Similar Records
AI-enabled Dynamic Finish Machining Optimization for Sustained Surface Integrity
In-Situ Calibrated Digital Process Twin Models for Resource Efficient Manufacturing