Academic literature on the topic 'Inspection of dimensions'

Ensuring inspection performance is not a trivial design problem, because inspection is a complex and difficult task that tends to be error-prone, whether performed by human or by automated machines. Due to economical or technological reasons, human inspectors are responsible for inspection functions in many cases. Humans, however, are rarely perfect. A system of manual inspection was found to be approximately 80-90% effective, thus allowing non-confirming parts to be processed (Harris & Chaney, 1969; Drury, 1975). As the attributes of interest or the variety of products increases, the complexity of an inspection task increases. The inspection system becomes less effective because of the sensory and cognitive limitations of human inspectors. Any means that can support or aid the human inspectors is necessary to compensate for inspection difficulty. <P> Augmented reality offers a new approach in designing an inspection system as a means to augment the cognitive capability of inspectors. To realize the potential benefits of AR, however the design of AR-aided inspection requires a through understanding of the inspection process as well as AR technology. The cognitive demands of inspection and the capabilities of AR to aid inspectors need to be evaluated to decide when and how to use AR for a dimensional inspection. <P>The objectives of this study are to improve the performance of a dimensional inspection task by using AR and to develop guidelines in designing an AR-aided inspection system. The performance of four inspection methods (i.e., manual, 2D-aided, 3D-aided, and AR-aided inspections) was compared in terms of inspection time and measurement accuracy. The results suggest that AR might be an effective tool that reduces inspection time. However, the measuring accuracy was basically the same across all inspection methods. The questionnaire results showed that the AR and 3D-aided inspection conditions are preferred over the manual and 2D-aided inspection. Based on the results, four design guidelines were formed in using AR technology for a dimensional inspection.<br>Ph. D.

Coordinate measuring machines are widely used to generate data points from an actual surface. The generated measurement data must be analyzed to yield critical geometric deviations of the measured part according to the requirements specified by the designer. However, ANSI standards do not specify the methods that should be used to evaluate the tolerances. The coordinate measuring machines employ different verification algorithms which may yield different results. Functional requirements or assembly conditions on a manufactured part are normally translated into geometric constraints to which the part must conform. Minimum zone evaluation technique is used when the measured data is regarded as an exact copy of the actual surface and the tolerance zone is represented as geometric constraints on the data. In the present study, a new zone-fitting algorithm is proposed. The algorithm evaluates the minimum zone that encompasses the set of measured points from the actual surface. The search for the rigid body transformation that places the set of points in the zone is modeled as a nonlinear optimization problem. The algorithm is employed to find the form tolerance of 2-D (line, circle) as well as 3-D geometries (cylinder). It is also used to propose an inspection methodology for turbine blades. By constraining the transformation parameters, the proposed methodology determines whether the points measured at the 2-D cross-sections fit in the corresponding tolerance zones simultaneously.