{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/43754"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/43754","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Integrating vision into a computer integrated manufacturing system","abstract":"An industrial vision system is a useful and often integral part of a computer integrated manufacturing system. Successful integration of vision capabilities into a manufacturing system involves extracting from image data the information which has meaning to the task at hand, and communicating that information to the larger system. The goal of this research was to integrate the activities of a stand-alone vision system into the operation of a manufacturing system; more specifically, the host controller and vision system were expected to work together to determine the status of pallets moving through the system. Pallet status was based on whether the objects on the pallet were correct in shape, location, and orientation, as compared to a pallet model generated using the microcomputer-based CADKEY CAD program. Cadd.c, a C language program developed for this research, extracts object area, perimeter, centroid, and principal angle from the CAD KE Y model for comparison to counterparts generated by the vision system. This off-line approach to supplying known parameters to the vision system was chosen over the traditional \"teach by showing\" method to take advantage of existing CAD data and to avoid disruption of the production system. The actual comparison of model and image data was performed by a program written in VPL, the resident language of the GE Optomation II Vision System. The comparison program relies on another short VPL program to obtain a pixel/inch ratio which equates the disparate units of the two systems. Model parameters are passed to the vision system via hardware and software links developed as part of this research. Three C language programs enable the host computer to communicate commands and parameters, and receive program results from the vision system. Preliminary testing of the system revealed that the object location and surface texture, lighting conditions, and pallet background all affected the image parameter calculations and hence the comparison process.","abstract_html":"An industrial vision system is a useful and often integral part of a computer integrated manufacturing system. Successful integration of vision capabilities into a manufacturing system involves extracting from image data the information which has meaning to the task at hand, and communicating that information to the larger system. The goal of this research was to integrate the activities of a stand-alone vision system into the operation of a manufacturing system; more specifically, the host controller and vision system were expected to work together to determine the status of pallets moving through the system. Pallet status was based on whether the objects on the pallet were correct in shape, location, and orientation, as compared to a pallet model generated using the microcomputer-based CADKEY CAD program. Cadd.c, a C language program developed for this research, extracts object area, perimeter, centroid, and principal angle from the CAD KE Y model for comparison to counterparts generated by the vision system. This off-line approach to supplying known parameters to the vision system was chosen over the traditional "teach by showing" method to take advantage of existing CAD data and to avoid disruption of the production system. The actual comparison of model and image data was performed by a program written in VPL, the resident language of the GE Optomation II Vision System. The comparison program relies on another short VPL program to obtain a pixel/inch ratio which equates the disparate units of the two systems. Model parameters are passed to the vision system via hardware and software links developed as part of this research. Three C language programs enable the host computer to communicate commands and parameters, and receive program results from the vision system. Preliminary testing of the system revealed that the object location and surface texture, lighting conditions, and pallet background all affected the image parameter calculations and hence the comparison process.","abstract_has_math":false,"creators":["Berg, Paula M."],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Industrial Engineering and Operations Research","degree_department":"Industrial Engineering and Operations Research","school":null,"contributors":[],"advisors":[],"committee_chairs":["Freeman, H. 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