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Full-Text Articles in Engineering
Characterization Of Microstructural And Mechanical Properties Of 17-4 Ph Stainless Steel By Cold Rolled And Machining Vs. Dmls Additive Manufacturing, Pablo Moreno-Garibaldi, Melvyn Alvarez-Vera, Juan Alfonso Beltrán-Fernández, Rafael Carrera-Espinoza, Héctor Manuel Hdz-García, J. C. Díaz-Guillen, Rita Muñoz-Arroyo, Javier A. Ortega, Paul Molenda
Characterization Of Microstructural And Mechanical Properties Of 17-4 Ph Stainless Steel By Cold Rolled And Machining Vs. Dmls Additive Manufacturing, Pablo Moreno-Garibaldi, Melvyn Alvarez-Vera, Juan Alfonso Beltrán-Fernández, Rafael Carrera-Espinoza, Héctor Manuel Hdz-García, J. C. Díaz-Guillen, Rita Muñoz-Arroyo, Javier A. Ortega, Paul Molenda
Mechanical Engineering Faculty Publications and Presentations
The 17-4 PH stainless steel is widely used in the aerospace, petrochemical, chemical, food, and general metallurgical industries. The present study was conducted to analyze the mechanical properties of two types of 17-4 PH stainless steel—commercial cold-rolled and direct metal laser sintering (DMLS) manufactured. This study employed linear and nonlinear tensile FEM simulations, combined with various materials characterization techniques such as tensile testing and nanoindentation. Moreover, microstructural analysis was performed using metallographic techniques, optical microscopy, scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS), and X-ray diffraction (XRD). The results on the microstructure for 17-4 PH DMLS stainless steel reveal …
Identifying Sick Cells From High-Resolution Solid-State Micropore Data, Abdul Hafeez, Azhar Ilyas, Ali R. Butt, Samir M. Iqbal
Identifying Sick Cells From High-Resolution Solid-State Micropore Data, Abdul Hafeez, Azhar Ilyas, Ali R. Butt, Samir M. Iqbal
Mechanical Engineering Faculty Publications and Presentations
Early detection of diseases such as cancer can drastically improve prognosis and treatment. To this end, solid-state micropores can measure distinct mechanical properties of diseased cells from their translocation behavior — detected as pulses in the temporal data stream of ionic current — and help diagnose diseases at early stages. However, the obstacle in such approaches is that the accuracy of the sensor is affected by noise, making the pulse detection task too subjective. This is inefficient especially when the disease-relevant data is only a fraction of the total acquired data. Thus, it is important to intelligently automate the detection …