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Abstract
Intelligent planning for inspection of parts with complex geometric surfaces using contact or non-contact devices is still a major challenge. Contact measurement is widely used in manufacturing owing to its superiority in point accuracy. However, the volumetric accuracy of non-contact measurement techniques is better owing to the large number of points that can be measured in a short time. Consequently, contact measurement is usually used for mechanical parts with prismatic shapes while non-contact measurement methods are mostly used with free-form shapes. Complex parts that include both prismatic and free form shapes may require inspection using both techniques. It may not be possible to fully digitise a part using a single type of sensor owing to occlusion or accessibility issues. This paper proposes a hybrid (contact/non-contact) inspection planning approach that capitalises on the advantages of both inspection techniques. In the beginning, a knowledge-based system has been developed for selecting the most suitable sensor for the inspected features using a proposed inspection-specific features taxonomy. Additionally, a new travel salesperson problem (TSP) formulation has been developed for sequencing of hybrid inspection tasks, where a novel sub-tour elimination constraint has been formulated. The proposed 0–1 integer mathematical model minimises the non-digitisation related time between successive inspection operations. The developed hybrid inspection planning system not only overcomes the incompleteness of information when each sensor type is used separately, but also improves the accuracy of the point cloud obtained by using both sensors. The developed hybrid inspection planner was applied to the inspection of a water pump housing of an automotive engine. The applicability of the developed inspection planning framework and methodology for inspecting complex parts and associated dies and moulds has been demonstrated. The developed system makes it easier to utilise the powerful combination of measurement sensors while automating the development of an optimal sensor-task assignment as well as inspection tasks sequence. It has the potential benefits of reducing the inspection time and cost and increasing the efficiency of the whole inspection process, which have positive effects on the early product development stages and quality assurance activities.