@article{zhang_zeng_starly_2021, title={Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis}, volume={3}, ISSN={["2523-3971"]}, DOI={10.1007/s42452-021-04427-5}, abstractNote={Abstract}, number={4}, journal={SN APPLIED SCIENCES}, author={Zhang, Jianlei and Zeng, Yukun and Starly, Binil}, year={2021}, month={Mar} }
 @article{nordberg_zhang_griffith_frank_starly_loboa_2017, title={Electrical Cell-Substrate Impedance Spectroscopy Can Monitor Age-Grouped Human Adipose Stem Cell Variability During Osteogenic Differentiation}, volume={6}, ISSN={["2157-6580"]}, DOI={10.5966/sctm.2015-0404}, abstractNote={Abstract}, number={2}, journal={STEM CELLS TRANSLATIONAL MEDICINE}, author={Nordberg, Rachel C. and Zhang, Jianlei and Griffith, Emily H. and Frank, Matthew W. and Starly, Binil and Loboa, Elizabeth G.}, year={2017}, month={Feb}, pages={502–511} }
 @article{zhang_starly_cai_cohen_lee_2017, title={Particle learning in online tool wear diagnosis and prognosis}, volume={28}, ISSN={["1526-6125"]}, DOI={10.1016/j.jmapro.2017.04.012}, abstractNote={Automated Tool condition monitoring is critical in intelligent manufacturing to improve both productivity and sustainability of manufacturing operations. Estimation of tool wear in real-time for critical machining operations can improve part quality and reduce scrap rates. This paper proposes a probabilistic method based on a Particle Learning (PL) approach by building a linear system transition function whose parameters are updated through online in-process observations of the machining process. By applying PL, the method helps to avoid developing a complex closed form formulation for a specific tool wear model. It increases the robustness of the algorithm and reduces the time complexity of computation. The application of the PL approach is tested using experiments performed on a milling machine. We have demonstrated one-step and two-step look ahead tool wear state prediction using online indirect measurements obtained from vibration signals. Additionally, the study also estimates remaining useful life (RUL) of the cutting tool inserts.}, journal={JOURNAL OF MANUFACTURING PROCESSES}, author={Zhang, Jianlei and Starly, Binil and Cai, Yi and Cohen, Paul H. and Lee, Yuan-Shin}, year={2017}, month={Aug}, pages={457–463} }