Author
C. Richard Liu
Other affiliations: University of North Dakota
Bio: C. Richard Liu is an academic researcher from Purdue University. The author has contributed to research in topics: Machining & Residual stress. The author has an hindex of 12, co-authored 30 publications receiving 976 citations. Previous affiliations of C. Richard Liu include University of North Dakota.
Papers
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TL;DR: In this paper, a new scheme for the prediction of a ball bearing's remaining useful life based on self-organizing map (SOM) and back propagation neural network methods is presented.
502 citations
TL;DR: In this article, the authors compared four models, namely, Litonski-Batra, power law, Johnson-Cook, and Bodner-Partom, in finite element modeling of orthogonal machining of HY-100 steel.
Abstract: In literature, four models incorporating strain rate and temperature effects are able to generalize material test results of HY-100 steel. This study compares the four models, namely, Litonski-Batra, power law, Johnson-Cook, and Bodner-Partom, in finite element modeling of orthogonal machining of this material. Consistency is found in cutting forces, as well as in stress and temperature patterns in all but the Litonski-Batra model. However, the predicted chip curls are inconsistent among the four models. Furthermore, the predicted residual stresses are substantially sensitive to the selection of material models. The magnitudes, and even the sign of the residual stresses in machined surfaces, vary with different models.
105 citations
TL;DR: In this paper, a new stress-based polynomial model of friction behavior in machining is proposed to address the issue from the perspective of predicting machining induced residual stresses, and the feasibility of this methodology is demonstrated by performing finite element analyses.
99 citations
TL;DR: In this paper, a finite element model is developed to predict the chip formation and phase transformation in orthogonal machining of hardened AISI 52100 steel (62HRC) using Polycristalline Cubic Boron Nitride (PCBN) tools.
Abstract: A finite element model is developed to predict the chip formation and phase transformation in orthogonal machining of hardened AISI 52100 steel (62HRC) using Polycristalline Cubic Boron Nitride (PCBN) tools. The model mainly includes a chip separation criterion based on critical equivalent plastic strain; a Coulomb’s law for the friction at the tool/chip interface; a material constitutive relation of velocity-modified temperature; a thermal analysis incorporating the heat dissipated from inelastic deformation energy and friction; and an annealing effect model, in which the work hardening effect may be lost or re-accumulate depending on material temperature. This fully coupled thermal-mechanical finite element analysis accurately simulates the formation of segmental chips and predicts the phase transformation on the chips, as verified by experiment. It is found that high temperatures around the secondary shear zone causes fast re-austenitization and martensite transformation, while other parts of the chips retain the original tempered martensitic structure.
64 citations
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6,278 citations
TL;DR: A comprehensive review of the PHM field is provided, followed by an introduction of a systematic PHM design methodology, 5S methodology, for converting data to prognostics information, to enable rapid customization and integration of PHM systems for diverse applications.
1,164 citations
TL;DR: A review on machinery prognostics following its whole program, i.e., from data acquisition to RUL prediction, which provides discussions on current situation, upcoming challenges as well as possible future trends for researchers in this field.
1,116 citations
TL;DR: In this article, the authors synthesize and place these individual pieces of information in context, while identifying their merits and weaknesses, and discuss the identified challenges, and in doing so, alerts researchers to opportunities for conducting advanced research in the field.
953 citations
TL;DR: A new data-driven approach for prognostics using deep convolution neural networks (DCNN) using time window approach is employed for sample preparation in order for better feature extraction by DCNN.
948 citations