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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Effect Of Feedrate, Depth Of Cut, Tool Material, And Toolpath On Dimensional Accuracy And Surface Roughness Of Milled Cfrp, Assem Hesham Almadani
Effect Of Feedrate, Depth Of Cut, Tool Material, And Toolpath On Dimensional Accuracy And Surface Roughness Of Milled Cfrp, Assem Hesham Almadani
Graduate Theses, Dissertations, and Problem Reports
This thesis investigates the effect of different factors on Carbon Fiber Reinforced Polymers (CFRP) milling, like feedrate, tool material, and cutting speed. CFRP offers excellent material properties, which led to the increase of the material in today's manufacturing industry. CFRP offers up to 2.25 times steel's modulus of elasticity at about a fifth of the weight and excellent thermal properties, which allow the use of this material in applications with high heat like automobiles. Many industries have implemented the use of CFRP in their applications, like airplanes and automobiles, which lead to a decrease in weight and increase in strength. …
Prediction Of Tensile Behaviors Of L-Ded 316 Stainless Steel Parts Using Machine Learning, Israt Zarin Era
Prediction Of Tensile Behaviors Of L-Ded 316 Stainless Steel Parts Using Machine Learning, Israt Zarin Era
Graduate Theses, Dissertations, and Problem Reports
Directed energy deposition (DED) is a rising field in the arena of metal additive manufacturing and has extensive applications in aerospace, medical and rapid prototyping. The process parameters, such as laser power, scanning speed and specimen height, play a great deal in controlling and affecting the properties of DED fabricated parts. Nevertheless, both experimental and simulation methods have shown constraints and limited ability to generate accurate and efficient computational predictions on the correlations between the process parameters and the final part quality. In this work, a data driven machine learning model XGBoost has been built and applied to predict the …