Object-Specific and Generic Difference Detection for Non-Destructive Testing Methods
Andreas Dietze, Yvonne Jung, Paul Grimm · 2024
In this arcticle, we present a concept of a tool suite that combines a generic and object-specific difference detection between 3D measurment and 3D planning data (cp. Figure 1). In a Generic Difference Detection (GDD) between measurement and planning data, the objects of interest to be compared are represented by the acquired 3D data (often the solid of a 3D reconstruction) and the 3D planning data in their entirety. An example of this is a shape analysis used as a non-destructive testing method for products from an additive (e.g. 3D printing) or subtractive (e.g. CNC milling) manufacturing process, in which a 3D reconstruction of the produced object is compared with its orignial 3D planning data [2]. The range of applications in this area is very broad and includes quality controls, plagiarism checks and measuring wear and tear. On the other hand, in an Object-Specific Difference Detection (OSDD) the objects of interest are located within the measurement and planning data and are represented by specific components that have to be compared. Here, the sector of digital construction monitoring can be mentioned as an example, in which the construction process and associated specific construction components, such as walls, passages and window openings, are checked against the planning data based on acquired measurement data [1]. Both difference detection techniques work, regardless of whether the object of interest is present in both the measurement and planning data or only in the measurement or planning data (e.g. after a building refurbishment). Besides the benefit of quality assurance following a production or construction process that is covered by both difference detection concepts, an early detection of errors is of great advantage. For example, follow-up costs after a construction process (e.g. building) can be reduced or avoided by identifying errors during the construction process. In addition, this can also help to ensure that the schedule is adhered to. Depending on the application, there is also the option of feeding identified deviations or errors back into the planning data in order to achieve synchronization between the planning data and the actual state of the object, in case the differences are intentional. While our approach for GDD is limited to a shape similarity analysis of two 3D objects and a subsequent real-time visualization of detected differences, our method for OSDD can already feed detected deviations and errors back into the 3D planning data and allows a collaborative result visualization based on a multi-codal presentation (e.g. tabular data or 3D rendering).