Abrasivity behavior analysis and fuzzy stochastic prediction of weakly cemented sandstones using an improved RBF neural network for quantifying uncertainties. [PDF]
PLoS OneYao Y, Lin J, Li X, Li Y.
europepmc +1 more source
Harnessing Phase Separation for the Development of High‐Performance Hydrogels
Advanced Science, EarlyView.ABSTRACT Hydrogels are indispensable for the development of next‐generation bioelectronics, soft robotics, and biomedical devices, where their mechanical properties determine performance and reliability. Among strategies to enhance hydrogel mechanics, phase separation enables controlled heterogeneity resulting in gel networks that are reinforced by ...
Yue Shao +3 more
wiley +1 more source
A Physics-Informed Benchmarking Framework for Machine Learning and Tree-Based Ensembles in IIoT-Enabled Predictive Maintenance. [PDF]
Sensors (Basel)Su YK, Tseng CJ.
europepmc +1 more source
Tailoring the Surface Integrity of Ti-6Al-4V Alloy by Ultrasonic Surface Rolling Process: A Review. [PDF]
Materials (Basel)Li G +5 more
europepmc +1 more source
Meniscus Morphology-Based Prediction of Backup Roll Eccentricity for Stable Slot-Die Coating on Polymer Films. [PDF]
Polymers (Basel)Kim M, Kim C, Jo J, Park B, Lee C.
europepmc +1 more source
CrayStack: a simplified crayfish optimization driven stacking ensemble for prediction of machining quality characteristics under data scarcity. [PDF]
Sci RepEmonena I +7 more
europepmc +1 more source
AI-Based Predictive Maintenance Framework for Industrial Saw Blade Wear Monitoring Using Low-Cost Vibration Sensors. [PDF]
Sensors (Basel)Alfaris H +3 more
europepmc +1 more source
Related searches:
On Prediction of Wear Coefficients in Sliding Wear
A S L E Transactions, 1983The wear rates and wear coefficients of metals are analytically predicted based on the delamination theory of wear when the wear rates are controlled by the subsurface crack propagation rate. The wear rate and the wear coefficient are predicted to be directly proportional to the depth of crack location and the crack growth rate. The numerical values of
N. P. Suh, H.-C. Sin
openaire +1 more source
In spite of the large number of wear models found in the literature, no model can predict metal wear a priori based only on materials property data and contact information. The complexity of wear and the large number of parameters affecting the outcome are the primary reasons for this situation.
S.M. Hsu, M.C. Shen, A.W. Ruff
openaire +1 more source
Abstract Advanced ceramics are increasingly being used for wear applications. Wear prediction of ceramics has become an important subject in these arenas. Ceramic wear is a complex function of microstructure, grain size and shape, grain boundary toughness, and the operating conditions.
S.M. Hsu, Ming Shen
openaire +1 more source