The research presented in this dissertation focuses on a two-stage methodology of virtual machining of parts produced on a three-axis turning center and virtual inspection of the produced parts using a bridge-type CMM.
The virtual machining system focuses on a priori predicting the surface profile of the turned part. The surface profile is generated by modeling the effects of the static errors inherent in the turning center, the error in the spindle motion, machine vibrations, tool geometry, process parameters, and tool wear. The model so developed is used to calculate various geometric dimensioning and tolerancing (GD and T) parameters of interest (form error, size, runout, and orientation tolerances). The effect of the various error factors on the GD and T parameters is examined. It is observed that the static errors, spindle motion errors, and tool wear play a significant role on the final profile of the part. The results from the inspection are used to develop quantitative relations between the values of the GD and T parameters and the machining process parameters. These regression equations are used to develop a machining advisor that optimizes the process parameters so as to maximize the adherence of the part to the design specifications. In order to be used in a mass production scenario, a weighted optimization function is used where the machining time is optimized simultaneously with the GD and T parameter. The analysis enables the identification of “sweet spots” on the machine, which, through a particular choice of process parameters and other variables, could yield more accurate products. The optimized process parameters are tested using the virtual machining system, and the results indicate a close match with the estimated values from the regression equations.
The virtual inspection system focuses on using the virtual profile generated to analyze the effectiveness of various inspection strategies. The inspection strategy includes the number of sample points, sampling method, and the location of the part on the CMM table for inspection. In addition to these factors, the uncertainty of the CMM due to effects such as probe pre-travel and hysteresis is also considered. The results obtained from this stage are used to study the effect of the various inspection parameters on the accuracy of the inspection results. It is observed that the sample size, location on the CMM table, and the level of CMM uncertainty significantly influence the accuracy of the inspection process. Based on these results, quantitative relations are established between the deviation of the inspection results from the true value of the GD and T parameter and the inspection parameters. These relationships, along with relationships for inspection time, are used to develop an inspection advisor to optimize the inspection parameters with the objective of increasing the inspection accuracy and precision, reducing the inspection time, or a combination of both objectives. The optimized inspection parameters obtained from the inspection advisor are used to inspect a test part for validation. The results obtained indicate a close match with the expected accuracy and precision levels.
Finally, a discussion of the procedure to implement these methodologies on the shop floor is presented. The discussion focuses on various scenarios ranging from a single turning center and single CMM to multiple turning centers and CMMs. Overall, the results from the research presented in this dissertation are expected to enable a substantial decrease in the need for physical prototyping for deciding optimal turning and inspection parameters, thereby reducing developmental costs and increasing the profitability of the manufacturing industry.