Manuscript Title: Characterization of Microannuli at the Cement-Casing Interface: Development of Methodology

Formation of microannuli at the interface of cement-casing can create well integrity issues. X-ray CT and Optical microscopy are technological trends that may have potential for direct visualization of microannuli. CT has an advantage of providing non-destructive visualization of microannuli, but its resolution suffers with increase in casing thickness. Conversely, Optical microscopy has the potential of providing higher resolution needed to detect smaller sized microannuli; however, information about microannuli is limited to only a few sections where samples have been sliced. The objective of the current article is to describe a methodology to examine the interface of cement-casing. Experimental work was combined with literature review. This includes both direct visualization methods, evaluation of current trends to better understand the characteristics and geometric variation of relevant leakage paths. We generate test specimens consisting of cement plugs, various steel casing thickness and nano-coated aluminium casings. Hydraulic sealability tests were conducted by injecting water at the cement-casing interface. Flow rates are then interpreted in terms of microannuli aperture and direct visualization of the cement plug-casing interface by CT and Optical microscopy was implemented. The experimental findings of this article will form a basis for studying geometry and size of microannuli as well as modelling of fluid migration.

Numerical Analysis of Microchannels Designed for Heat Sinks

Conjugate heat transfer is numerically investigated using a three-dimensional computational fluid dynamics approach in various microchannel geometries to identify a high-performance cooling method for piezoelectric ceramic stacks and spindle units in high-precision machines. Straight microchannels with rectangular cross sections are first considered, showing the performance limitations of decreasing the size of the microchannels, so other solutions are needed for high applied heat fluxes. Next, many microchannel designs, focusing on streamwise geometric variation, are compared to straight channels to assess their performances. Sinusoidally varying channels produce the highest heat transfer rates of those studied. Thus, their optimization is considered at a channel width and height of 35 and 100 μm, respectively. Heat transfer increases as the amplitude and spatial frequencies of the channels increase due to increased interfacial surface area and enhanced Dean flow. The highest performance efficiencies are observed at intermediate levels of amplitude and frequency, with efficiency decreasing as these geometric parameters are increased further at the onset of flow separation. The sinusoidal channel geometries are then optimized with respect to minimizing the system’s pressure drop for all applied heat fluxes between 5690 and 6510 kW/m2. Doing so created an optimal geometry curve and showed that all geometries in this region had amplitudes close to 40 μm. Therefore, imposing a fixed heat flux requirement for a case study of cooling piezoelectric ceramics, the optimized sinusoidal geometry decreases the system pressure drop by 79% relative to a straight channel while maintaining a larger minimum feature size.

A data-centric approach to generative modelling for 3D-printed steel

The emergence of additive manufacture (AM) for metallic material enables components of near arbitrary complexity to be produced. This has potential to disrupt traditional engineering approaches. However, metallic AM components exhibit greater levels of variation in their geometric and mechanical properties compared to standard components, which is not yet well understood. This uncertainty poses a fundamental barrier to potential users of the material, since extensive post-manufacture testing is currently required to ensure safety standards are met. Taking an interdisciplinary approach that combines probabilistic mechanics and uncertainty quantification, we demonstrate that intrinsic variation in AM steel can be well described by a generative statistical model that enables the quality of a design to be predicted before manufacture. Specifically, the geometric variation in the material can be described by an anisotropic spatial random field with oscillatory covariance structure, and the mechanical behaviour by a stochastic anisotropic elasto-plastic material model. The fitted generative model is validated on a held-out experimental dataset and our results underscore the need to combine both statistical and physics-based modelling in the characterization of new AM steel products.

Assessing Variability in Segmentation Algorithms for 3D Printing at the Point of Care

Background: 3D printing (3DP) has enabled medical professionals to create patient-specific medical devices to assist in surgical planning. Anatomical models can be generated from patient scans using a wide array of software, but there are limited studies on the geometric variance that is introduced during the digital conversion of images to models. The final accuracy of the 3D printed model is a function of manufacturing hardware quality control and the variability introduced during the multiple digital steps that convert patient scans to a printable format. This study provides a brief summary of common algorithms used for segmentation and their principal features. We also identify critical parameters and steps in the workflow where geometric variation may be introduced. We then provide suggested methods to measure or reduce the variation and mitigate these risks.Methods: Using a clinical head CT scan of a mandible containing a tumor, we performed segmentations in four separate programs using workflows optimized for each. Differences in segmentation were calculated using several techniques.Results: Visual inspection of print-ready models showed distinct differences in the thickness of the medial wall of the mandible adjacent to the tumor. Residual volumes were calculated to generate pairwise agreement and disagreement percentages between each as the program’s model. For the relevant ROIs, statistically significant differences were found globally in the volume and surface area comparisons between final bone and tumor models, as well locally between nerve centroid measurements – major variance introduced due to workflow is highlighted in difference heat maps. As with all clinical use cases, statistically significant results must be weighed against the clinical significance of any deviations found.Conclusions: Statistically significant geometric variations can be introduced to patient-specific models from differences in software applications. The global and local variations should be evaluated for a full understanding of geometric variations. The clinical implications of these variations vary by anatomical location and should be evaluated on a case-by-case basis by certified clinicians. Understanding the basic functions of segmentation and 3D print preparation software is essential for users intending to adopt the use of patient-specific models for clinical intervention or decision making.

Computational Fluid Dynamics Applied to River Boat Hull Optimization

This paper extends the work of Maia and Said (“Analysis for Resistance Reduction of an Amazon School Boat through Hull Shape Modification Utilizing a CFD Tool,” 2019), proposing the optimization of a school boat hull using genetic algorithms and computational fluid dynamics (CDF) simulations. The study examines a school boat used for the transportation of children to schools in riverine communities of the Brazilian Amazon. The optimization was focused on reducing the hydrodynamic hull resistance by modifying the hull lines, using the NSGA-II (non-dominated sorting genetic algorithm II) algorithm in the CAD (computer aided design) CAESES environment. The objective of the study was to reduce the resistance coefficients: C wp (wave profile) and C wp trans (transverse wave profile), thus reducing the total resistance coefficient (C t) and the generated wave amplitude. Pressure distributions and flow lines were then evaluated to obtain an optimal modified hull with reduced wave emission (lower wave resistance) and, consequently, lower forward resistance. The proposed methodology resulted in a maximum reduction of 5% in the total resistance coefficient C t and in the identification of a trend of geometric variation of the hull for investigation in further studies.

Assessing Variability in Segmentation Algorithms for 3D Printing at the Point of Care

Background: 3D Printing (3DP) has enabled medical professionals to create patient specific medical devices to assist in surgical planning. Anatomical models can be generated from patient scans using a wide array of software, but there are limited studies on the geometric variance that is introduced during the digital conversion of images to model. Final accuracy of the 3D printed model is a function of manufacturing hardware quality control and the variability introduced during the multiple digital steps that convert patient scans to a printable format. This study provides a brief summary of common algorithms used for segmentation and their principal features. We also identify critical parameters and steps in the workflow where geometric variation may be introduced. We then provide suggested methods to measure or reduce the variation and mitigate these risks. Methods: Using a clinical head CT scan of a mandible containing a tumor, we performed segmentations in four separate programs using workflows optimized for each. Differences in segmentation were calculated using several techniques.Results: Visual inspection of print-ready models showed distinct differences in the thickness of the medial wall of the mandible adjacent to the tumor. Residual volumes were calculated to generate pairwise agreement and disagreement percentages between each as program’s model. For the relevant ROIs, statistically significant differences were found globally in the volume and surface area comparisons between final bone and tumor models, as well locally between nerve centroid measurements – major variance introduced due to workflow is highlighted in difference heat maps. As with all clinical use cases, statistically significant results must be weighed against the clinical significance of any deviations found. Conclusions: Statistically significant geometric variations can be introduced to patient specific models from differences in software applications. The global and local variations should be evaluated for a full understanding of geometric variations. The clinical implications of these variations vary by anatomical location and should be evaluated on a case-by-case basis by certified clinicians. Understanding the basic functions of segmentation and 3D print preparation software is essential for users intending to adopt the use of patient specific models for clinical intervention or decision making.

Experimental Comparison of Straight Flanging and Rotary Die Bending Based on Springback

Springback in sheet bending is a well-defined phenomenon; however, variation of springback is difficult to control causing quality problems in especially mass-produced goods such as home appliances. As an alternative to straight flanging, the rotary die bending process offers reduced springback as well as reduced geometric variation; however, there is little knowledge in the literature. The effects of process parameters on the springback behavior of straight flanging and rotary die bending as applied to home appliance side panels are investigated experimentally. For each flange bending method, effects of die radius, punch-die clearance, rolling direction, flange length, and material supplier on springback are tested on EN DC01 carbon and SAE 430 stainless steel sheets. A full factorial experimental design was applied to investigate the factor interactions as well as the main effects using ANOVA. In both methods, die radius was the most dominant factor on springback, clearance being the second, and the inevitable material property variations being the third one. Nevertheless, in rotary die bending, springback values were smaller with significantly less scatter compared to straight flanging. Consequently, rotary die bending is a much more preferable process especially in mass production performed with narrow profit margins.

SQUEAK AND RATTLE PREVENTION BY GEOMETRIC VARIATION MANAGEMENT USING A TWO-STAGE EVOLUTIONARY OPTIMISATION APPROACH

Squeak and rattle are annoying sounds that are often regarded as the failure indicators by car users. Geometric variation is a key contributor to the generation of squeak and rattle sounds. Optimisation of the connection configuration in assemblies can be a provision to minimise this risk. However, the optimisation process for large assemblies can be computationally expensive. The focus of this work is to propose a two-stage evolutionary optimisation scheme to find the fittest connection configurations that minimise the risk for squeak and rattle. This was done by defining the objective functions as the measured variation and deviation in the rattle direction and the squeak plane. In the first stage, the location of the fasteners primarily contributing to the rattle direction measures are identified. In the second stage, fasteners primarily contributing to the squeak plane measures are added to the fittest configuration from phase one. It was assumed that the fasteners from the squeak group plane have a lower-order effect on the rattle direction measures, compared to the fasteners from the rattle direction group. This assumption was falsified for a set of simplified geometries. Also, a new uniform space filler algorithm was introduced to efficiently generate an inclusive and feasible starting population for the optimisation process by incorporating the problem constraints in the algorithm. For two industrial cases, it was shown that by using the proposed two-stage optimisation scheme the variation and deviation measures in critical interfaces for squeak and rattle improved compared to the baseline results.