SGER: An Integrated Approach to Prediction, Assessment and Inspection of Form Errors in Machined Parts

University Of Cincinnati Main Campus, Cincinnati OH

Investigators

Abstract

The objective of this Small Grant for Exploratory Research (SGER) is to characterize and correlate form errors in machined parts with machining process sequences and process variables used for generating the form. This information will be used to: (a) Provide feedback and establish clear guidelines for reducing form errors by optimal selection of process sequences and process parameters, (b) Develop methods for individualized optimal inspection plans for assessing the true value of each form error that are process sequence/ process variables specific, and (c) Develop form process capability indices for processes/machines. For this research, two types of form errors, namely, Circularity and Cylindricity, will be considered. The domain of processes considered will be limited to form features generated by conventional hole making processes and turning processes. These form profiles/surfaces will be modeled using analytical Fourier Models and Generalized Cylinders. Attributes of profiles/surfaces such as general shape, number of lobes, amplitude of lobes, eccentricity, evenness of lobe spacing, form errors, will be extracted by mapping profiles to a novel s-theta and psi-s domains. A Neural network based approach will be used for training the system to predict standardized profiles, ranges of standardized attributes (lobes, amplitude etc.) as well as range of form errors based on process sequence and parameters. For each family of standardized profiles, a Design of Experiments (DOE) based approach will be used for determining the optimum combination of sample size, sampling method, and evaluation algorithm (least square or minimum zone) for form inspection that is process specific for each profile family, and can estimate the true form error with a certain level of confidence. A novel combinatorial optimization formulation in conjunction with Genetic Algorithms (GA) will be used to calculate minimum zones of the form errors. Finally, based on standardized profiles and form errors for each profile family, a form process capability index will be developed for each of the form errors. If successful, this research will provide a basis for predicting form profiles and errors based on process variables and sequence. This could result in selection of optimal process plans/variables for reducing form errors. This research will also provide guidelines for optimal inspection plans for assessing form errors that are specific to the process creating the form. Finally, the form process capability indices developed can be used for optimal manufacturing tolerance allocation, design centering and machine selection. Such a plan can readily be used by designers as well as practitioners on the shop floor.