The ability to objectively specify surface finish to ensure consistent visual appearance addresses a vital need in surface coating engineering. This work demonstrates how a computational framework, called surface quality and inspection descriptors (SQuID™), can be leveraged to effectively rank different grades of surface finish appearances. ISO 25178-2 areal surface metrics extracted from bandpass-filtered measurements of a set of ten visual smoothness standards taken on a coherent scanning interferometer are used to quantify different grades of powder-coated surface finish. The ability to automatically classify the standard tiles using multi-scale areal texture parameters is compared to parameters obtained from a hand-held gloss meter. The results indicate that the ten different surface finishes can be automatically classified with accuracies as low as 65% and as high as 99%, depending on the filtering and parameters used to quantify the surfaces. The highest classification accuracy is achieved using only five multi-scale topography descriptions of the surface.

Numerical simulation models for laser powder bed fusion of metals (PBF-LB/M) vary in complexity and fidelity, ranging from high-fidelity models that capture melt pool dynamics to simplified models suited for part-scale temperature predictions and process optimizations. Validation against experimental data is essential to build confidence in their predictive capabilities. However, for in-situ thermographic measurements, a direct comparison is challenging due to the data’s size and the multi-scale nature of the process. Similarities must be analyzed at different spatial and temporal scales based on the model’s fidelity and its intended application. For example, agreement between a thermal simulation and measurement in a steady-state scenario does not guarantee accuracy during transient phases. Statistical similarity measures provide a quantitative means to assess model-measurement agreement, highlighting regions of high and low similarity. In this work, we validate a thermal simulation model, discretized using the space-time finite element method, against thermographic camera measurements using various similarity measures and evaluate their applicability to PBF-LB/M. We also propose a multi-scale similarity assessment approach tailored to model fidelity and application.

This paper explores the block-based singular-value-decomposition (SVD) with applications to image compression and processing in both static and dynamic cases. Results include a comprehensive performance study of rank-1 asymmetric block sizes in comparison to conventional uniform square blocks for static image compression. In addition, a method of movement detection for video streaming applications are presented and discussed in this paper. We show that by tracking the Frobenius norm of the derivatives of sequential images, represented as a time series matrix, allows one to infer change points in varying lighting conditions and foreground movement in video sequences. We show that by combining SVD background subtraction we can create clearer distinction between movements and changes in lighting intensity. It is also demonstrated that one can maintain the original derivative feature after re-scaling the images of the video stream to a fraction of their original resolution via local averaging.