Estimation of number-area-frequency dimensions of surface water bodies

This paper proposes a technique that includes a set of mathematical morphological transformations to estimate the frequency dimension. The dimension computed through a power law relationship is tallied with the dimension computed through a correlational plot. This technique is demonstrated on a two-dimensional section embodying a large number of surface water bodies, extracted from remotely sensed data, situated randomly, and the frequency dimension (D) for surface water bodies yields straight-line dependence of lnC(r) (correlational integral) on ln(r) (radius of structuring template). The correlational integral is computed for two aspects by considering the number of water bodies and their corresponding occupied areas. The number-frequency dimension and the area frequency dimension computed through correlational plots yield straight-line dependencies with slopes that are greater than unity but less than 2.0 (1.3 and 1.7, respectively).     

Neural networks and their applications in component design data retrieval

Neural networks have gained increased importance in the past few years. One of the basic characteristics of neural networks is the property of associative memory. In this paper we study the possibility of using the ideas of neural networks and associative memory in the manufacturing domain, with specific reference to design data retrieval in group technology. A two-layer feed-forward perceptron with backpropagation is simulated on a Vax-8550 to train example parts. The complete scheme along with the simulation results are explained and future directions indicated.     

Optimization of fused filament fabrication process parameters for mechanical responses of weather-resistant polymer (acrylonitrile styrene acrylate)

Additively manufactured polymeric products for automotive, aerospace, and biomedical applications are usually intended for service in an outdoor environment with high mechanical loading conditions. The strength and sustainability of the products can be significantly degraded due to the outdoor environmental conditions such as UV light, moisture, heat, and so forth. In this research work, a novel weather-resistant polymer (WRP) material, that is, acrylonitrile styrene acrylate (ASA), has been studied. Furthermore, this work aims to study the effect of process parameters and enhance the strength of WRP (ASA) specimens using the FFF process. The optimized process parameters, that is, build orientation (BO), extrusion temperature (ET), layer thickness (LT), and printing speed (PS), were identified based on the tensile and flexural strength using the Taguchi technique and statistical analysis. The best tensile and flexural strengths for the specimen were achieved at both orientations (XYZ and ZXY) TS: 255°C ET, 0.14 mm LT, 50 mm/s PS; and FS: 245°C ET, 0.28 mm LT, 50 mm/s PS, respectively. Regression model was developed to investigate the correlation between the process parameters with tensile and flexural strength. A validation test confirmed the findings, and the error between the actual and predicted values is less than ±10%.     

Non-destructive evaluation of mechanical properties of poly (vinyl) alcohol-hydroxyapatite nanocomposites

Hydroxyapatite-poly (vinyl) alcohol nanocomposite powder was synthesized using varying poly (vinyl) alcohol concentrations. The dried powder was compacted into micro-porous disks at a load of 4 tons. The disks were sintered at 1200°C to evolve porous nanocomposites. Size and shape of the pores observed in the scanning electron micrographs were quantified by using image processing software. Ultrasound velocity measurements were done to evaluate mechanical properties non-destructively.     

Combining predicate and numeric abstraction for software model checking

Predicate (PA) and numeric (NA) abstractions are the two principal techniques for software analysis. In this paper, we develop an approach to couple the two techniques tightly into a unified framework via a single abstract domain called NumPredDom. In particular, we develop and evaluate four data structures that implement NumPredDom but differ in their expressivity and internal representation and algorithms. All our data structures combine BDDs (for efficient propositional reasoning) with data structures for representing numerical constraints. Our technique is distinguished by its support for complex transfer functions that allow two-way interaction between predicate and numeric information during state transformation. We have implemented a general framework for reachability analysis of C programs on top of our four data structures. Our experiments on non-trivial examples show that our proposed combination of PA and NA is more powerful and more efficient than either technique alone.     

Molecular weight (M n) and functionality effects on CUP formation and stability

The formation of colloidal unimolecular polymer (CUP) particles from single polymer strands was investigated as a function of molecular weight. The CUP particle size was correlated with the absolute molecular weight and its distribution. The characteristics of the particles were evaluated with respect to viscosity, acid number, size distribution, and stability. The particle size varied from less than 3 nm to above 8 nm representing polymers with molecular weight in the range of 3000–153,000. Lower molecular weight polymers were found to be unstable. Particle size measurements using dynamic light scattering technique indicated a normal distribution which corresponded to the molecular weight distribution of the copolymer. The statistical distribution of the acid groups in the polymer chains played a significant role in the stability of low molecular weight polymers.     

Ductile fracture in thin sheet metals: a FEM study of the Sandia fracture challenge problem based on the Gurson–Tvergaard–Needleman fracture model

In this paper, the Gurson–Tvergaard–Needleman (GTN) fracture model has been implemented in a 3D nonlinear finite element framework to investigate ductile failure. The simulation consists of both model parameter calibrations and predictions based on the experimental configurations that were described in the Sandia fracture challenge in 2012. The goal is to test the robustness of the GTN model in predicting crack initiation and propagation in a ductile structural stainless steel (15-5 PH). It has been observed that the predicted load drops at crack initiation agree well with the experimental data. Both the simulation and experiments reveal that cracks always initiate in the subsurface. The observed crack path in the experiments, however, is not unique and is strongly influenced by the geometric variations in the specimen introduced in the fabrication process. The crack path is quite sensitive to the relative location of pre-existing features (notch and holes) in this challenge problem, and a very small variation can potentially lead to different path, hence affecting the ductile failure pattern significantly. Variations in the crack path are also observed in the simulation with different choices of GTN model parameters that control the void nucleation and growth behavior.     

The structure and analysis of nanotechnology co-author and citation networks

Research activities and collaborations in nanoscale science and engineering have major implications for advancing technological frontiers in many fields including medicine, electronics, energy, and communication. The National Nanotechnology Initiative (NNI) promotes efforts to cultivate effective research and collaborations among nano scientists and engineers to accelerate the advancement of nanotechnology and its commercialization. As of August 2008, there have been over 800 products considered to benefit from nanotechnology directly or indirectly. However, today’s accomplishments in nanotechnology cannot be transformed into commercial products without productive collaborations among experts from disparate research areas such as chemistry, physics, math, biology, engineering, manufacturing, environmental sciences, and social sciences. To study the patterns of collaboration, we build and analyze the collaboration network of scientists and engineers who conduct research in nanotechnology. We study the structure of information flow through citation network of papers authored by nano area scientists. We believe that the study of nano area co-author and paper citation networks improve our understanding of patterns and trends of the current research efforts in this field. We construct these networks based on the publication data collected for years ranging 1993 through 2008 from the scientific literature database “Web of Science”. We explore those networks to find out whether they follow power-law degree distributions and/or if they have a signature of hierarchy. We investigate the small-world characteristics and the existence of possible community structures in those networks. We estimate the statistical properties of the networks and interpret their significance with respect to the nano field.