A review on the development and properties of continuous fiber/epoxy/aluminum hybrid composites for aircraft structures

Weight reduction and improved damage tolerance characteristics were the prime drivers to develop new family of materials for the aerospace/ aeronautical industry. Aiming this objective, a new lightweight Fiber/ Metal Laminate (FML) has been developed. The combination of metal and polymer composite laminates can create a synergistic effect on many properties. The mechanical properties of FML shows improvements over the properties of both aluminum alloys and composite materials individually. Due to their excellent properties, FML are being used as fuselage skin structures of the next generation commercial aircrafts. One of the advantages of FML when compared with conventional carbon fiber/epoxy composites is the low moisture absorption. The moisture absorption in FML composites is slower when compared with polymer composites, even under the relatively harsh conditions, due to the barrier of the aluminum outer layers. Due to this favorable atmosphere, recently big companies such as EMBRAER, Aerospatiale, Boing, Airbus, and so one, starting to work with this kind of materials as an alternative to save money and to guarantee the security of their aircrafts.

Application of ARX neural networks to model the rate of penetration of petroleum wells drilling

Bit performance prediction has been a challenging problem for the petroleum industry. It is essential in cost reduction associated with well planning and drilling performance prediction, especially when rigs leasing rates tend to follow the projects-demand and barrel-price rises. A methodology to model and predict one of the drilling bit performance evaluator, the Rate of Penetration (ROP), is presented herein. As the parameters affecting the ROP are complex and their relationship not easily modeled, the application of a Neural Network is suggested. In the present work, a dynamic neural network, based on the Auto-Regressive with Extra Input Signals model, or ARX model, is used to approach the ROP modeling problem. The network was applied to a real oil offshore field data set, consisted of information from seven wells drilled with an equal-diameter bit.

In vitro evaluation of surface roughness of 4 resin composites after the toothbrushing process and methods to recover superficial smoothness.

OBJECTIVE: This study evaluated the efficiency of repolishing, sealing with surface sealant, and the joining of both in decreasing the surface roughness of resin-based composites after a toothbrushing process. METHOD AND MATERIALS: Ten specimens of each composite (Alert, Z100, Definite, and Prodigy Condensable), measuring 2 mm in thickness and 4 mm in diameter, were made and submitted to finishing and polishing processes on both sides of the specimens using the Sof-Lex system. The specimens were then subjected to toothbrushing (30,000 cycles), and surface roughness (Ra) was analyzed with a Surfcorder SE 1700 profilometer. The upper surface of each composite was etched with 37% phosphoric acid, and the surface-penetrating sealant Protect-it was applied on 1 surface. The roughness of these surfaces was again measured. On the other side, the surface of the specimen was repolished, and the efficiency of this procedure was measured using the profilometer. The surface roughness resulting from the joining of the 2 methods was verified by applying, in the final stage, the surface-penetrating sealant on the repolished surface. Data were analyzed with analysis of variance and Tukey test (P <.05). RESULTS: Results showed that the lowest surface roughness values were obtained for Definite, Z100, and Prodigy Condensable after the repolishing process and after the repolishing plus sealing. For Alert, the joining of repolishing plus sealing promoted the lowest values of surface roughness. CONCLUSION: Of the resin-based composites, Alert demonstrated the highest values of surface roughness in all the techniques tested.

Applying a genetic neuro-model reference adaptive controller in drilling optimization

Motivated by rising drilling operation costs, the oil industry has shown a trend toward real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated with parameters modeling. One of the drillbit performance evaluators, the Rate Of Penetration (ROP), has been used as a drilling control parameter. However, relationships between operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on an auto-regressive with extra input signals, or ARX model and on a Genetic Algorithm (GA) to control the ROP. © [2006] IEEE.

Classification of petroleum well drilling operations using Support Vector Machine (SVM)

During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 2006 IEEE.

A genetic neuro-model reference adaptive controller for petroleum wells drilling operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 2006 IEEE.

Adherence of Streptococcus mutans to uncoated and saliva-coated glass-ceramics and composites

This study sought to investigate the surface roughness and the adherence of Streptococcus mutans (in the presence and absence of saliva) to ceramics and composites. The early dental biofilms formed in situ on the materials were illustrated, using scanning electron microscopy (SEM). Feldspathic and leucite/feldspathic ceramics and microhybrid and microfilled composites were evaluated. Human dental enamel was used as the control. Standardized specimens of the materials were produced and surface roughness was analyzed. The adhesion tests were carried out in 24-well plates and colony forming units (CFU/mL) were evaluated. Values of roughness (μm) and adherence (CFU/mL) were analyzed statistically. Of all the surfaces tested, enamel was the roughest. Leucite/feldspathic ceramics were rougher than the feldspathic ceramic, while composites were similar statistically. Enamel offered the highest level of adherence to uncoated and saliva-coated specimens, while the leucite/feldspathic ceramic demonstrated greater adherence than the feldspathic ceramic and the composites were similar statically. The rougher restorative materials increased the adherence of S, mutans on the material surfaces.

Microhardness assessment of different commercial brands of resin composites with different degrees of translucence

Owing to improvements in its mechanical properties and to the availability of shade and translucence resources, resin composite has become one of the most widely used restorative materials in present day Dentistry. The aim of this study was to assess the relation between the surface hardness of seven different commercial brands of resin composites (Charisma, Fill Magic, Master Fill, Natural Look, Opallis, Tetric Ceram, and Z250) and the different degrees of translucence (translucid, enamel and dentin). Vickers microhardness testing revealed significant differences among the groups. Z250 was the commercial brand that showed the best performance in the hardness test. When comparing the three groups assessed within the same brand, only Master Fill and Fill Magic presented statistically significant differences among all of the different translucencies. Natural Look was the only one that showed no significant difference among any of the three groups. Charisma, Opallis, Tetric Ceram and Z250 showed significant differences among some of the tested groups. Based on the results found in this study, it was not possible to establish a relation between translucence and the microhardness of the resin composites assessed. Depending on the material assessed, however, translucence variation did affect the microhardness values of the resin composites.

Fatigue behaviour study on repaired aramid fiber/epoxy composites

Aramid fiber reinforced polymer composites have been used in a wide variety of applications, such as aerospace, marine, sporting equipment and in the defense sector, due to their outstanding properties at low density. The most widely adopted procedure to investigate the repair of composites has been by repairing damages simulated in composite specimens. This work presents the structural repair influence on tensile and fatigue properties of a typical aramid fiber/epoxy composite used in the aerospace industry. According to this work, the aramid/epoxy composites with and without repair present tensile strength values of 618 and 680MPa, respectively, and tensile modulus of 26.5 and 30.1 GPa, respectively. Therefore, the fatigue results show that in loads higher than 170 MPa, both composites present a low life cycle (lower than 200,000 cycles) and the repaired aramid/epoxy composite presented low fatigue resistance in low and high cycle when compared with non-repaired composite. With these results, it is possible to observe a decrease of the measured mechanical properties of the repaired composites.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.

Analysis of mechanical behavior in bending of polymer composites using the finite elements method

The use of composite materials has increased in the recent decades, mainly in the aeronautics and automotives industries. In the present study is elaborated a computational simulation program of the bending test using the finite elements method, in the commercial software ANSYS. This simulation has the objective of analyze the mechanical behavior in bending of two composites with polymeric matrix reinforced with carbon fibers. Also are realized bending tests of the 3 points to obtain the resistances of the materials. Data from simulation and tests are used to make a comparison between two failures criteria, Tsai-Wu and Hashin criterion. Copyright © 2009 SAE International.

An intelligent system to detect drilling problems through drilled cuttings return analysis

Cuttings return analysis is an important tool to detect and prevent problems during the petroleum well drilling process. Several measurements and tools have been developed for drilling problems detection, including mud logging, PWD and downhole torque information. Cuttings flow meters were developed in the past to provide information regarding cuttings return at the shale shakers. Their use, however, significantly impact the operation including rig space issues, interferences in geological analysis besides, additional personel required. This article proposes a non intrusive system to analyze the cuttings concentration at the shale shakers, which can indicate problems during drilling process, such as landslide, the collapse of the well borehole walls. Cuttings images are acquired by a high definition camera installed above the shakers and sent to a computer coupled with a data analysis system which aims the quantification and closure of a cuttings material balance in the well surface system domain. No additional people at the rigsite are required to operate the system. Modern Artificial intelligence techniques are used for pattern recognition and data analysis. Techniques include the Optimum-Path Forest (OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC). Field test results conducted on offshore floating vessels are presented. Results show the robustness of the proposed system, which can be also integrated with other data to improve the efficiency of drilling problems detection. Copyright 2010, IADC/SPE Drilling Conference and Exhibition.

Fast petroleum well drilling monitoring through optimum-path forest

Automatic inspection of petroleum well drilling has became paramount in the last years, mainly because of the crucial importance of saving time and operations during the drilling process in order to avoid some problems, such as the collapse of the well borehole walls. In this paper, we extended another work by proposing a fast petroleum well drilling monitoring through a modified version of the Optimum-Path Forest classifier. Given that the cutting's volume at the vibrating shale shaker can provide several information about drilling, we used computer vision techniques to extract texture informations from cutting images acquired by a digital camera. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and effciency. We used the Optimum-Path Forest (OPF), EOPF (Efficient OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP) Support Vector Machines (SVM), and a Bayesian Classifier (BC) to assess the robustness of our proposed schema for petroleum well drilling monitoring through cutting image analysis.

Study of ITO@PMMA composites by transmission electron microscopy

ITO nanowires were synthesized by carbothermal reduction process, using a co-evaporation method, and have controlled size, shape, and chemical composition. The electrical measurements of nanowires showed they have a resistance of about 102 Ω. In order to produce nanocomposites films, nanowires were dispersed in toluene using an ultrasonic cleaner, so the PMMA polymer was added, and the system was kept under agitation up to obtain a clear suspension. The PMMA polymer was filled with 1, 2, 5 and 10% in weight of nanowires, and the films were done by tape casting. The results showed that the electrical resistance of nanocomposites changed by over 7 orders of magnitude by increasing the amount of filler, and using 5 wt% of filler the composite resistance decreased from 1010 Ω to about 104 Ω, which means that percolation threshold of wires occurred at this concentration. This is an interesting result once for nanocomposites filled with ITO nanoparticles is necessary about 18% in weight to obtain percolation. The addition of filler up to 10 wt% decreased the resistance of the composite to 103 Ω, which is a value close to the resistance of wires. The composites were also analyzed by transmission electron microscopy (TEM), and the TEM results are in agreement with the electrical ones about percolation of nanowires. These results are promising once indicates that is possible to produce conductive and transparent in the visible range films by the addition of ITO nanowires in a polymeric matrix using a simple route. © 2011 Materials Research Society.

Color stability of composites: effect of immersion media.

Coloring in drinks decreases the color stability of composite restorations, reducing their longevity. The purpose of this in vitro study was to evaluate the effect of immersion media on color stability of seven different composite resins (Solidex - Shofu, Resilab-Wilcos, Signum - Heraeus, Epricord - Tokuyama, Adoro - Ivoclar Vivadent, Admira - Voco and Sinfony - 3MESPE). Seven resin-based composite specimens were prepared using a cylindrical teflon mold 2 mm thick and 10 mm in diameter Fifteen specimens of each resin were light-cured according to manufacturers' instructions and randomized into 3 groups (n= 5) according to immersion media: coffee, cola beverage and water A digital spectrophotometer Easy Shade (VITA) was used to evaluate the color changes at baseline and 7 days after immersion in each solution. Specimens were stored in the different staining media for 24 h/day during one week. The color differences were analyzed by two-way ANOVA and Tukey 's test (p< 0.05). Color change was observed after one week of immersion and there were statistical differences in staining, composite and interaction factors. The least staining was observed in Admira (deltaE= 3.934+/-0.814) and Resilab (deltaE= 3.993+/-0.735), followed by Adoro (deltaE= 4.044+/-1.001), Epri-cord (deltaE= 4.049+/-1.234), Signum (deltaE= 4.260+/-1.785), Solidex (deltaE=5,122+/-0.534) and Sinfony (deltaE=5.126+/-0.838). All of the composites tested except Adoro were susceptible to staining by substances present in coffee and cola, when stored in beverage for seven days. The lowest deltaE means were obtained with Admira.