Information-Rich Manufacturing Metrology

Abstract

Information-rich metrology (IRM) is a new term that refers to an approach, where the conventional paradigm of measurement is transcended, thanks to the introduction and active role of multiple novel sources of information. The overarching goal of IRM is to encompass and homogenise all those measurement scenarios where information available from heterogeneous sources, for example, from the object being measured, the manufacturing process that was used to fabricate it, the workings of the measurement instrument itself, as well as from any previous measurements carried with any other instrument, is gathered and somewhat incorporated with an active role into the measurement pipeline in order to ultimately achieve a higher-quality measurement result (better metrological performance, shorter measurement times, smaller consumption of resources). Examples of IRM in action in precision and additive manufacturing will be presented, including the measurement of form and texture.

Smart optical coordinate and surface metrology

“…Such solutions can involve running intuitive functions and feedback mechanisms for performing automated corrective actions, changes in the measuring parameters or adjustments of tolerance limits before initiating production [3,6]. Particularly, available pre-existing information (for example, a priori knowledge of the manufacturing process, knowledge about the measured object, knowledge of the measurement technology principles) can help guide instruments in the inspection and verification of parts and be used to monitor the manufacturing process [13][14][15]. The IRM paradigm is discussed in depth in section 3.1, along with other examples of current advanced software and hardware measurement solutions.…”

“…Leach et al [15] introduced the term 'information-rich metrology' (IRM) to indicate the enhancement of any measurement process through the use of pre-existing (i.e. a priori) information.…”

“…The IRM paradigm workflow is shown in schematic form in figure 1. In the IRM approach, information comes from knowledge of an object/surface being measured, knowledge of the measuring instruments employed and knowledge of the physical interactions/principles underlying the measurement technology itself, with respect to the object being measured [13][14][15]. The use of additional sources of information contributes to the enhancement of the performance of a measurement system, and thus increases the final quality of its measurement output.…”

“…A fuzzy logic model was used to define the best positions and orientations for the measurements. Other example applications of the IRM paradigm are presented elsewhere [14].…”

Prediction of Extreme Value Areal Parameters in Laser Powder Bed Fusion of Nickel Superalloy 625

“…Zanini et al [26,27] compared optical and x-ray computed tomography (XCT) surface measurements against two-dimensional (2D) cross-sectional profiles, noting that the re-entrant features of AM surfaces significantly affect the accuracy of optical measurements. Fox et al [28,29] investigated the registration of optical surface measurements and three-dimensional (3D) XCT measurements for more information-rich metrology (a term coined by Leach et al [30]). While findings were too limited to provide insight into a relationship between surface texture and subsurface porosity, a key finding was that users should be cautious of sub-voxel rescaling of XCT measurements as it provided little to no improvement over the original voxel size data in comparison to laser confocal measurements.…”