New Method for Evaluating Surface Roughness Parameters Acquired by Laser Scanning

Development of a Method for Soil Tilth Quality Evaluation from Crumbling Roller Baskets Using Deep Machine Learning Models

A combination tillage with disks, rippers, and roller baskets allows the loosening of compacted soils and the crumbling of soil clods. Statistical methods for evaluating the soil tilth quality of combination tillage are limited. Light Detection and Ranging (LiDAR) data and machine learning models (Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN)) are proposed to investigate roller basket pressure settings on soil tilth quality. Soil profiles were measured using LiDAR (stop and go and on-the-go) and RGB visual images from a Completely Randomized Design (CRD) tillage experiment on clay loam soil with treatments of roller basket down, roller basket up, and no-till in three replicates. Utilizing RF, SVM, and NN methods on the LiDAR data set identified median, mean, maximum, and standard deviation as the top features of importance variables that were statistically affected by the roller settings. Applying multivariate discriminatory analysis on the four statistical measures, three soil tilth classes were predicted with mean prediction rates of 77% (Roller-basket down), 64% (Roller-basket up), and 90% (No till). The LiDAR data analytics-inspired soil tilth classes correlated well with the RGB image discriminatory analysis. Soil tilth machine learning models were shown to be successful in classifying soil tilth with regard to onboard operator pressure control settings on the roller basket of the combination tillage implement.

Sorting lung tumor volumes from 4D-MRI data using an automatic tumor-based signal reduces stitching artifacts

PURPOSE

To investigate whether a novel signal derived from tumor motion allows more precise sorting of 4D-magnetic resonance (4D-MR) image data than do signals based on normal anatomy, reducing levels of stitching artifacts within sorted lung tumor volumes.

METHODS

(4D-MRI) scans were collected for 10 lung cancer patients using a 2D T2-weighted single-shot turbo spin echo sequence, obtaining 25 repeat frames per image slice. For each slice, a tumor-motion signal was generated using the first principal component of movement in the tumor neighborhood (TumorPC1). Signals were also generated from displacements of the diaphragm (DIA) and upper and lower chest wall (UCW/LCW) and from slice body area changes (BA). Pearson r coefficients of correlations between observed tumor movement and respiratory signals were determined. TumorPC1, DIA, and UCW signals were used to compile image stacks showing each patient's tumor volume in a respiratory phase. Unsorted image stacks were also built for comparison. For each image stack, the presence of stitching artifacts was assessed by measuring the roughness of the compiled tumor surface according to a roughness metric (Rg). Statistical differences in weighted means of Rg between any two signals were determined using an exact permutation test.

RESULTS

The TumorPC1 signal was most strongly correlated with superior-inferior tumor motion, and had significantly higher Pearson r values (median 0.86) than those determined for correlations of UCW, LCW, and BA with superior-inferior tumor motion (p < 0.05). Weighted means of ratios of Rg values in TumorPC1 image stacks to those in unsorted, UCW, and DIA stacks were 0.67, 0.69, and 0.71, all significantly favoring TumorPC1 (p = 0.02-0.05). For other pairs of signals, weighted mean ratios did not differ significantly from one.

CONCLUSION

Tumor volumes were smoother in 3D image stacks compiled using the first principal component of tumor motion than in stacks compiled with signals based on normal anatomy.

The Surface Roughness of Contemporary Indirect CAD/CAM Restorative Materials That Are Glazed and Chair-Side-Finished/Polished

The surface roughness (Ra) of indirect computer-aided design/computer-aided manufacturing (CAD/CAM)-fabricated dental restorations is crucial for their long-term durability. This study intended to evaluate the Ra of five different types of contemporary indirect CAD/CAM restorative materials with varying compositions that were glazed and finished/polished. A total of 75 specimens, disc-shaped (10 mm × 2 mm), were obtained from five materials (n = 15) (Tetric CAD, IPS e.max CAD, IPS e.max ZirCAD, CELTRA Duo, and Vita Enamic) and fabricated by CAD/CAM. One of the two surfaces for each specimen was subjected to glazing, while the other surface was subjected to finishing/polishing. The Ra of the two surfaces in micrometers (μm) was evaluated using a Profilometer, while the surface topography was examined using a scanning electron microscope. Using SPSS, the Kruskal-Wallis, post hoc Conover, and Mann-Whitney tests were used to statistically evaluate the data. A comparison of the Ra for the finished/polished surfaces of the five test materials showed significant differences (p < 0.0001). Among the finished/polished surfaces, the mean rank values of Vita Enamic were significantly higher than the other four test materials (p < 0.0001). A comparison of the Ra of glazed surfaces among the five study materials revealed significant differences (p < 0.0001). The Ra for the IPS e.max ZirCAD material was significantly higher than the rest of the four materials (p < 0.001). A comparison of the Ra for two types of surface conditioning within each of the five test materials showed a significant difference (p < 0.05). Only for IPS e.max ZirCAD was the Ra of the glazed surface significantly higher than the finished/polished surface (p < 0.0001). Significant variations in the surface roughness (Ra) were exhibited between the finished/polished and glazed surfaces of the five test materials. Hybrid ceramics showed the highest Ra values for the finished/polished surfaces, and zirconia exhibited the highest Ra values among the glazed surfaces among the tested materials. The Ra values of either finished/polished or glazed surfaces of the test materials were within the clinically acceptable range (0.2-0.5 μm), except for the glazed surface of the zirconia ceramics (0.84 μm).

Experimental Investigation of the Peel Strength of Artificial Leather and Polypropylene Specimens

This study investigates the surface properties and adhesive strength of polypropylene (PP) in order to enhance the bond between PP injection-molded specimens and polyvinyl chloride (PVC) synthetic artificial leather. Plasma, primer, and flame treatments were applied to the surface of each specimen prepared using the two types of injection molds. The surface morphology, surface roughness, and contact angle were analyzed, and peel-strength analyses and a morphological inspections of the peeled specimens were performed. The peeling strength of the PP injection molding was measured, followed by a morphological examination of the peeled specimens. The plasma and flame treatments improved the peel strength, and the plasma and flame treatments changed the rough exterior to a hydrophilic surface, improving the peel strength. In addition, the primer treatment exhibited a lower peel strength than did the other treatments. This confirmed the low adhesion of the primer to the hydrophobic PP surface. The outcomes of this study can be employed across a multitude of industries that require improved adhesion for PP injection molded products.

Amendment the surface structure and optical properties of Makrofol LT by low energy oxygen ion bombardment

Ion beam bombardment is a powerful technique to improve the surface properties of polymeric materials without changing the bulk properties. Herein, Makrofol LT films were bombarded with low energy of oxygen ions at different fluences ranging from 11 × 10¹⁷ to 44 × 10¹⁷ ions/cm². X-ray diffraction, FTIR spectroscopy, surface-roughness tester, ultraviolet-visible spectroscopy, and Fluorescence spectroscopy were used to examine the change in the structure, chemical functional groups, alteration in surface roughness parameters, and photo-physical properties. The obtained results evidenced that the ordering and disordering structure of bombarded Makrofol LT films were influenced by ion beam irradiation according to the ion fluence. The FTIR spectroscopy of functional group examination revealed the possibility of the presence of sp²-carbon clusterization and amorphization. The surface roughness parameters increase as the ion fluences increase. Optical measurements exhibit a shift of absorption-edge towards the visible zone that correlated to the surface damage and the creation of CC bonds. The fluorescence spectra exhibit that the yield intensities decrease with increasing ion fluences. This refers to improvement in the radiative-recombination rate relative to non-radiative recombination. This is attributable to the growth of clusters, the increase of density states of the surface, and the increase in the surface roughness of the bombarded samples.

Determination of roughness coefficient in 3D digital representations of rocks

The roughness property of rocks is significant in engineering studies due to their mechanical and hydraulic performance and the possibility of quantifying flow velocity and predicting the performance of wells and rock mass structures. However, the study of roughness in rocks is usually carried out through 2D linear measurements (through mechanical profilometer equipment), obtaining a coefficient that may not represent the entire rock surface. Thus, based on the hypothesis that it is possible to quantify the roughness coefficient in rock plugs reconstructed three-dimensionally by the computer vision technique, this research aims to an alternative method to determine the roughness coefficient in rock plugs. The point cloud generated from the 3D model of the photogrammetry process was used to measure the distance between each point and a calculated fit plane over the entire rock surface. The roughness was quantified using roughness parameters ([Formula: see text]) calculated in hierarchically organized regions. In this hierarchical division, the greater the quantity of division analyzed, the greater the detail of the roughness. The main results show that obtaining the roughness coefficient over the entire surface of the three-dimensional model has peculiarities that would not be observed in the two-dimensional reading. From the 2D measurements, mean roughness values ([Formula: see text]) of [Formula: see text] and [Formula: see text] were obtained for samples 1 and 2, respectively. By the same method, the results of the [Formula: see text] coefficient applied three-dimensionally over the entire rocky surface were at most [Formula: see text] and [Formula: see text], respectively, showing the difference in values along the surface and the importance of this approach.

Estimating the Potential Differential Settlement of a Tailings Deposit Based on Consolidation Properties Heterogeneity

Processing of extracted oil sands generates substantial volumes of tailings slurries. Due to the scale and inherent variability of the tailings properties, consolidation settlement is expected to occur at different rates and magnitudes across the tailings deposit. Estimating potential differential settlement of the consolidated deposit surface is an essential input for closure design. This paper presents a three-step methodology that generates multiple realizations of quasi-three-dimensional (3D) surfaces of the consolidated deposit based on the adjacent points. Each point is based on a stochastic one-dimensional (1D) large strain consolidation model developed with Monte Carlo techniques in GoldSim. The simulated surfaces provide early estimates of differential settlement based on the variability of consolidation properties expected in the tailings deposit. Comprehensive sensitivity analyses are performed for differently treated tailings material through 28 distinct scenarios to evaluate the sensitivity of the developed 1D and 3D models to consolidation input parameters over a 40-year time period. The analysis demonstrated that differential settlement is highly sensitive to tailings compressibility and hydraulic conductivity governed by the constitutive relationship parameters, and less sensitive to the solids content, specific gravity or thickness of a surcharge load. Tailings that underwent steady continuous settlement exhibited the largest degree of differential settlement.

Skin-Inspired Thermometer Enabling Contact-Independent Temperature Sensation via a Seebeck-Resistive Bimodal System

Tactile sensation is a powerful method for probing the temperature of an arbitrary object due to its intuitive operating mechanism. However, the disruptive interface commonly formed between the thermometer and the object gives rise to thermal contact resistance, which is the primary source of measurement inaccuracy. Here, we develop a bioinspired bimodal temperature sensor exhibiting robust measurement accuracy by precisely decoupling contact resistance from the associated thermal circuit. In our sensors, a micropatterned resistive thermometer is placed underneath a thermoelectric heat fluxmeter, which resembles thermoreceptors located in human biomembranes. The object temperature is probed by modulating the thermometer temperature within the sensor system and precisely extrapolating the zero-heat flux point of the Seebeck voltage developed across the fluxmeter. At this zero-heat flux point, the object and thermometer temperatures coincide with each other regardless of the contact resistance formed at the fluxmeter-object interface. An experimental study shows that our sensors display excellent measurement accuracy within ∼0.5 K over a wide range of contact resistance values. Our work opens up new avenues for highly sensitive tactile thermal sensation in thermal haptics, medical devices, and robotics if combined with flexible devices.

Extending conventional surface roughness ISO parameters using topological data analysis for shot peened surfaces

The roughness of material surfaces is of greatest relevance for applications. These include wear, friction, fatigue, cytocompatibility, or corrosion resistance. Today’s descriptors of the International Organization for Standardization show varying performance in discriminating surface roughness patterns. We introduce here a set of surface parameters which are extracted from the appropriate persistence diagram with enhanced discrimination power. Using the finite element method implemented in Abaqus Explicit 2019, we modelled American Rolling Mill Company pure iron specimens (volume 1.5 × 1.5 × 1.0 mm³) exposed to a shot peening procedure. Surface roughness evaluation after each shot impact and single indents were controlled numerically. Conventional and persistence-based evaluation is implemented in Python code and available as open access supplement. Topological techniques prove helpful in the comparison of different shot peened surface samples. Conventional surface area roughness parameters might struggle in distinguishing different shot peening surface topographies, in particular for coverage values > 69%. Above that range, the calculation of conventional parameters leads to overlapping descriptor values. In contrast, lifetime entropy of persistence diagrams and Betti curves provide novel, discriminative one-dimensional descriptors at all coverage ranges. We compare how conventional parameters and persistence parameters describe surface roughness. Conventional parameters are outperformed. These results highlight how topological techniques might be a promising extension of surface roughness methods.

Validation of a roughness parameters for defining surface roughness of prosthetic polyethylene Pe-Lite liner

The Biosculptor's CNC milling machine, the Biomill, offered four different surfaces machined on positive models. This study aims to adopt the surface topography method in characterizing the four different surface roughness of polyethylene Pe-Lite liner as a product of the Biomill. Three surface parameters chosen were the arithmetic average (Ra), root mean square roughness (Rq), and ten-point height (Rz). The surface parameters were used to define the four different surfaces (STANDARD, FINE, COARSE, and FAST) and then compared with the same liner material from a conventionally fabricated socket. The Ra values of the conventional liner, 8.43 μm, were determined to be in-between the Ra values of STANDARD and FAST surfaces which were 8.33 μm and 8.58 μm respectively. STANDARD surface required 43.2 min to be carved while FAST surface took almost only a third of the time compared to STANDARD surface (conventional socket takes 2–3 days). The results of this study would be one of the guidelines to the prosthetists using the Biosculptor in socket fabrication to produce sockets according to the suitable surface to cater to different requirements and levels of activity of each amputee.

Method for evaluating roughness and valley areas coefficients of surfaces acquired by laser scanner

The quantitative determination of average roughness parameters, from the determination of height variations of the surface points, is frequently used to estimate the adhesion between an adhesive and the surface of a substrate. However, to determine the interaction between an adhesive and a surface of a heterogeneous material, such as a red ceramic, it is essential to define other roughness parameters. This work proposes a method for determining the roughness of red ceramic blocks from a three-dimensional evaluation, with the objective of estimating the contact area that the ceramic substrate can provide for a cementitious matrix. The study determines the average surface roughness from multiple planes and proposes the adoption of 2 more roughness parameters, the valley area index and the average valley area. The results demonstrate that there are advantages in using the proposed multiple plane method for roughness computation and that the valley area parameters are efficient to estimate the extent of adhesion between the materials involved.

Changes in Crystal Phase, Morphology, and Flexural Strength of As-Sintered Translucent Monolithic Zirconia Ceramic Modified by Femtosecond Laser

Conventional bonding technology suitable for silica-based ceramics is not applicable to zirconia, due to its polycrystalline phase composition, chemical stability, and acid corrosion resistance. The development of an effective treatment to improve its surface roughness and mechanical properties remains an unresolved problem. Therefore, to solve this problem, this in vitro study evaluated the changes in surface morphology and flexural strength of translucent monolithic zirconia surfaces treated with femtosecond laser technology. As-sintered translucent zirconia specimens were subjected to airborne particle abrasion and femtosecond laser treatments, while control group specimens received no treatment. After treatment, the roughness and morphology of the treated zirconia surfaces were examined. The flexural strength and X-ray diffraction of the treated specimens were measured and analyzed. Statistical inferential analysis included one-way analysis of variance at a set significance level of 5%. The surface roughness after femtosecond laser treatment was significantly improved when compared with the control group and the group that received the airborne particle abrasion treatment (p < 0.05). In comparison with the airborne particle abrasion group, the flexural strength of the group that received the femtosecond laser treatment was significantly improved (p < 0.05). The femtosecond laser approach using appropriate parameters enhanced the roughness of the zirconia without reducing its flexural strength; therefore, this approach offers potential for the treatment of zirconia surfaces.

(Macro)Molecular Imprinting of Proteins on PCL Electrospun Scaffolds

Biological recognition sites are very useful for biomedical purposes and, more specifically, for polymeric scaffolds. However, synthetic polymers are not capable of providing specific biological recognition sites. To solve this inconvenience, functionalization of biological moieties is typically performed, oftentimes via peptide binding. In this sense, the main task is capturing the biological complexity of a protein. This study proposes a possible alternative solution to this challenge. Our approach is based on the combination of molecular imprinting (MI) and electrospinning processes. We propose here an alternative MI approach with polymeric structures, instead of using cross-linkers and monomers as conventionally performed. Different PCL-protein scaffolds were produced via electrospinning before performing MI. Gelatin, collagen, and elastin were used as proteins. Results evidenced that the MI process conducted with PCL electrospun membranes was carried out with ionic interactions between the desired molecules and the recognition sites formed. In addition, it has been proved that MI was more efficient when using gelatin as a template. This approach opens a new stage in the development of recognition sites in scaffolds obtained with synthetic polymers and their application for biomedical purposes.

Object Semantic Segmentation in Point Clouds—Comparison of a Deep Learning and a Knowledge-Based Method

Through the power of new sensing technologies, we are increasingly digitizing the real world. However, instruments produce unstructured data, mainly in the form of point clouds for 3D data and images for 2D data. Nevertheless, many applications (such as navigation, survey, infrastructure analysis) need structured data containing objects and their geometry. Various computer vision approaches have thus been developed to structure the data and identify objects therein. They can be separated into model-driven, data-driven, and knowledge-based approaches. Model-driven approaches mainly use the information on the objects contained in the data and are thus limited to objects and context. Among data-driven approaches, we increasingly find deep learning strategies because of their autonomy in detecting objects. They identify reliable patterns in the data and connect these to the object of interest. Deep learning approaches have to learn these patterns in a training stage. Knowledge-based approaches use characteristic knowledge from different domains allowing the detection and classification of objects. The knowledge must be formalized and substitutes the training for deep learning. Semantic web technologies allow the management of such human knowledge. Deep learning and knowledge-based approaches have already shown good results for semantic segmentation in various examples. The common goal but the different strategies of the two approaches engaged our interest in doing a comparison to get an idea of their strengths and weaknesses. To fill this knowledge gap, we applied two implementations of such approaches to a mobile mapping point cloud. The detected object categories are car, bush, tree, ground, streetlight and building. The deep learning approach uses a convolutional neural network, whereas the knowledge-based approach uses standard semantic web technologies such as SPARQL and OWL2to guide the data processing and the subsequent classification as well. The LiDAR point cloud used was acquired by a mobile mapping system in an urban environment and presents various complex scenes, allowing us to show the advantages and disadvantages of these two types of approaches. The deep learning and knowledge-based approaches produce a semantic segmentation with an average F1 score of 0.66 and 0.78, respectively. Further details are given by analyzing individual object categories allowing us to characterize specific properties of both types of approaches.

Effect of Air-Abraded Versus Laser-Fused Fluorapatite Glass-Ceramics on Shear Bond Strength of Repair Materials to Zirconia

Zirconia repair could be a feasible alternative option to total replacement in fractured zirconia-based restorations. Maximising the bond strength by enriching zirconia with fluorapatite glass-ceramics (FGC) powder has been addressed and compared to other surface treatments. Besides resin composite, other repair materials have been proposed and compared. Zirconia blocks received different surface treatments (A-sandblasting with tribochemical silica-coated alumina (CoJet). B-sandblasting with FGC powder (FGC), C-fluorapatite glass-ceramic coat+ neodymium-doped yttrium aluminum garnet laser irradiation (FGC + Nd: YAG), and D-no surface treatment). The surface roughness, topography, and crystallinity were investigated by a profilometer, scanning electron microscopy (SEM), and X-ray diffraction (XRD) analyses, respectively. For each surface treatment, three repair materials (feldspathic porcelain, lithium disilicate, and resin composite) were bonded to zirconia with 10, Methacryloyloxydecyl dihydrogen phosphate (MDP)-Monobond Plus/ Multilink Automix. Bonded specimens were thermocycled for 10,000 cycles and tested for shear bond strength (SBS) at a speed of 1 mm/min, followed by the analysis of the mode of failure. FGC + Nd: YAG laser group reported the highest surface roughness and monoclinic content compared to CoJet, FGC, and control groups. The highest mean SBS was found in FGC-blasted zirconia, followed by FGC + Nd: YAG laser and CoJet treated groups. However, the lowest SBS was found in control groups regardless of the repair material. Sandblasting zirconia with FGC powder increased SBS of resin to zirconia with lower monoclinic phase transformation compared to FGC + Nd: YAG or CoJet groups.

3D Printed Biomodels for Flow Visualization in Stenotic Vessels: An Experimental and Numerical Study

Atherosclerosis is one of the most serious and common forms of cardiovascular disease and a major cause of death and disability worldwide. It is a multifactorial and complex disease that promoted several hemodynamic studies. Although in vivo studies more accurately represent the physiological conditions, in vitro experiments more reliably control several physiological variables and most adequately validate numerical flow studies. Here, a hemodynamic study in idealized stenotic and healthy coronary arteries is presented by applying both numerical and in vitro approaches through computational fluid dynamics simulations and a high-speed video microscopy technique, respectively. By means of stereolithography 3D printing technology, biomodels with three different resolutions were used to perform experimental flow studies. The results showed that the biomodel printed with a resolution of 50 μm was able to most accurately visualize flow due to its lowest roughness values (Ra = 1.8 μm). The flow experimental results showed a qualitatively good agreement with the blood flow numerical data, providing a clear observation of recirculation regions when the diameter reduction reached 60%.