Dynamics of machining

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Machining is the most widely used operation in manufacturing mechanical parts. Costly aerospace parts are machined from solid blocks by removing 90% of the material. Prototyping costs of mass production lines in the automotive industry can reach a few million dollars in order to shorten the machining time while avoiding scrap rates. The industry needs productive machining strategies to stay competitive by predicting the physical performance of manufacturing steps ahead of costly physical trials. This research will contribute to the mathematical modeling of machine tool spindles, control systems, and machining operations. The mathematical models will be used to predict the outcome of the design and manufacturing scenarios, which will enable the engineer to optimize the manufacturing processes. The focus of this research is to model line boring operations, machine tool spindles, and computer controllers for virtual simulation of machining operations. All bearing housings, such as housings for crank, cam, and turbine shafts, are bored using slender bars carrying multiple inserts. If the bar is not well supported and the incorrect speed, the depth of cut and feed are selected, the system experiences severe chatter vibrations leading to tolerance violations and poor surface finish quality. Similarly, if the spindle is not assembled with symmetric stiffness, the machine cannot be used at productive speeds. This research will develop a line-boring design and analysis model which will predict the cutting forces, torque, power, dynamic response, and chatter free cutting conditions of boring long bores. The current knowledge will be further extended to assess the penalty of non-symmetric spindle dynamics on their productivity during the high speed machining of dies, molds, and aerospace parts. The Computer Numerical Control (CNC) of the machine tool will also be modeled in virtual environment, which will allow the manufacturing engineers to select the most optimal CNC parameters and machining strategies before conducting costly trials. The proposed research will enrich the current knowledge in machining to achieve a full, science and physics based virtual machining technology.