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Full-Text Articles in Engineering
Milling Stability Map Identification And Machining Parameter Optimization Using Bayesian Inference, Aaron William Cornelius
Milling Stability Map Identification And Machining Parameter Optimization Using Bayesian Inference, Aaron William Cornelius
Doctoral Dissertations
This dissertation describes a physics-guided Bayesian learning approach for statistically modelling and optimizing machining processes under a state of uncertainty. This approach uses a series of automatically-selected cutting tests to refine uncertainties about the machining system's dynamics and cutting force and identify higher productivity cutting parameters. The algorithm is evaluated experimentally and compared to the cutting tool manufacturer’s recommendations, both in laboratory conditions and in an industrial setting to optimize the machining process for a large aluminum component. These results show that the proposed Bayesian model can quickly identify both highly-productive machining parameters and accurate information about the underlying system …
Surface Location Error In Robotic Milling: Modeling And Experiments, Richard Henry Swan Jr.
Surface Location Error In Robotic Milling: Modeling And Experiments, Richard Henry Swan Jr.
Masters Theses
Robotic milling offers new opportunities for discrete part manufacturing as an alternative to milling using large conventional machine tools. The advantage of industrial robots is their large work volume, configurability, and comparatively low cost. However, robots are significantly less stiff than conventional machine tools, which can lead to poor surface finish, low machining accuracy, and low material removal rates. The purpose of this research is to predict the geometric errors, or surface location errors, that occur in a robotic mulling tool path, validate these predictions with machining tests, and compensate these errors by tool path modification. Compared with conventional machine …
Digital Cutting Force Modeling For Milling Operations, Timothy T. No
Digital Cutting Force Modeling For Milling Operations, Timothy T. No
Doctoral Dissertations
Process improvement in milling through improved understanding of machining dynamics is an on-going research endeavor. The objective of this project is to advance digital modeling of the milling process by incorporating tool-specific geometry in the machining analysis. Structured light scanning will be used to perform tool geometry measurements and produce a 3D model. The 3D model data will include the spatial location of the cutting edges, as well as the rake and relief profiles from the tool cross section. The rake and relief profiles will be imported, together with the work material flow stress model, into a finite element analysis …
Displacement-Based Dynamometer For Milling Force Measurement, Michael F. Gomez
Displacement-Based Dynamometer For Milling Force Measurement, Michael F. Gomez
Doctoral Dissertations
This project will study the design and testing of a low-cost dynamometer for milling dynamic force measurement. The monolithic design is based on constrained-motion/flexure-based kinematics, where force is inferred from displacement measured using a low-cost optical interrupter (i.e., a knife edge that partially interrupts the light beam in an emitter-detector pair). The time-dependent displacement of the dynamometer’s moving platform caused by the milling force is converted to the frequency domain, multiplied by the inverse of the dynamometer’s ideally single degree of freedom (SDOF) frequency response function (FRF), and converted back into the time-domain to obtain the time-dependent cutting force. The …