Abstract
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).
Highlights
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
The designers responsible for these components expect a high degree of precision in the surface properties; it is critical to be able to accurately predict surface integrity characteristics
Residual stresses are of particular interest, since they may lead to undesirable warping, or reduced fatigue life
Summary
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. 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). The International Academy of Production Sciences (CIRP) has devoted significant efforts to the study of machininginduced surface integrity, and its relation with functional performance Despite these efforts, there remains a lack of efficient and robust models to relate machining processes parameters, such as feeds and speeds, with damage, such as grain deformation, strain hardening, and tensile residual stresses. More recent analytical models of residual stress formation were developed by Jacobus et al [8], and Liang and co-workers [9,10]
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