Experimental and prediction of sintered Cu–W composite by using artificial neural networks

High-energy mechanical milling was used to mix Cu and W powders. Cylindrical preforms with initial preform density of 85% were prepared using a die and punch assembly. The preforms were sintered in an electric muffle furnace at 750 °C, 800 °C, 850 °C, and subsequently furnace cooled and then the specimens are hot extruded to get 92% preform density. Scanning Electron Microscope and X-ray diffraction observations used to evaluate the characteristics. The pore size reduction during extrusion was studied using Auto CAD 2010. Neural networks are employed to study the tribological behavior of sintered Cu–W composites. The proposed neural network model has used the measured parameters namely the weight percentage of tungsten, sintering temperature, load and sliding distance to predict multiple material characteristics, hardness, specific wear rate, and coefficient of friction. The predicted values from the proposed networks coincide with the experimental values. In addition, a relative study between the regression analysis and the networks revealed that the artificial neural networks can predict the tribological characteristics of sintered Cu and W composites better than regression polynomials within a very few percent error.     

Optimization of mechanical property and shape recovery behavior of Ti-(∼49 at.%) Ni alloy using artificial neural network and genetic algorithm

Multi-objective genetic algorithm based searching is used for designing the process schedule of Ti-(∼49 at.%) Ni alloy, to achieve optimum mechanical property and shape recovery behavior. Artificial neural network technique based data driven models are developed to empirically describe the relationship between the processing conditions and the properties. The models are used as objective functions for the optimization process. The optimization search found to be helpful to design the decision space variables for the improvement in shape recovery behavior without sacrificing the mechanical properties of the alloy. The Pareto solutions have been used as the guideline to find the process schedules, which is validated by suitable experimentation.     

Parameter identification of an elasto-plastic behaviour using artificial neural networks–genetic algorithm method

The simulation of the metal forming processes requires accurate constitutive models to describe the material behaviour at finite strain taking into account several conditions. The choice of a rheological model and the determination of its parameters should be made from a test that generates such conditions. The major difficulty encountered is that there is no experimental test satisfying all these criteria. The use of more than one test seems more and more essential, and it is utilized to characterize the rheological behaviour at operating conditions that correspond to metal forming applications. An Inverse analysis is then considered. Therefore, the difficulty lies within the long computing time taken when an optimization procedure is coupled with a finite element computation (FEC) to identify the material parameters. In order to solve the computing time problem, this paper proposes a hybrid identification method based on finite elements, neural network computations and genetic algorithm (GA) of an elasto-plastic behaviour model. The strategy suggested is then applied to identify the Karafillis and Boyce criterion and the Voce parameters model of the Stainless Steel AISI 304 using two tests (plane tensile test and bulge test with a circular die) at the same time.     

Artificial neural network and constitutive equations to predict the hot deformation behavior of modified 2.25Cr–1Mo steel

Hot compression tests of modified 2.25Cr–1Mo steel were conducted on a Gleeble-3500 thermo-mechanical simulator at the temperatures ranging from 1173 to 1473 K with the strain rate of 0.01–10 s⁻¹ and the height reduction of 60%. Based on the experimental results, an artificial neural network (ANN) model and constitutive equations were developed to predict the hot deformation behavior of modified 2.25Cr–1Mo steel. A comparative evaluation of the constitutive equations and the ANN model was carried out. It was found that the relative errors based on the ANN model varied from −4.63% to 2.23% and those were in the range from −20.48% to 12.11% by using the constitutive equations, and the average root mean square errors were 0.62 MPa and 7.66 MPa corresponding to the ANN model and constitutive equations, respectively. These results showed that the well-trained ANN model was more accurate and efficient in predicting the hot deformation behavior of modified 2.25Cr–1Mo steel than the constitutive equations.     

Understanding and predicting the dissemination of scientific papers on social media: a two-step simultaneous equation modeling–artificial neural network approach

Scientometrics - Social media platforms have had an enormous impact on the dissemination of scientific work and have fared well in covering scientific papers. However, little is known about the...     

Purpose and Application of Standards——Interoperability and Openness

Preface:Nowadays, standardization is playing a more crucial role in marketing, industrial and tech- nical fields. Twenty years ago, standardization was the realm of just a few specialists.Today, compa- nies treat standardization as a technical and com- mercial element of great importance. At the same time, companies have realized they must play an active role in the standardization field,and they must be ready to accept standards made without their agreement.Therefore,it is also increasingly necessary for engineers and decision makers in en- terprises to study the purpose and application of standards and standardization, and to optimize their enterprises’ position by doing so.     

Simulation of structural features on mechanochemical synthesis of Al2O3–TiB2 nanocomposite by optimized artificial neural network

In this study, structural features of alumina–titanium diboride nanocomposite (Al₂O₃–TiB₂) were simulated from the mixture of titanium dioxide, boric acid and pure aluminum as raw materials via mechanochemical process using the optimized artificial neural network. The phase transformation and structural evolutions during the mechanochemical process were characterized using X-ray powder diffractometry (XRD). For better understanding the refining crystallite size and amorphization phenomena during the milling, XRD data were modeled and simulated by artificial neural network (ANN). An ANN consisting of three layers of neurons was trained using a back-propagation learning rule. Also, the ANN was optimized by Taguchi method. Additionally, the crystallite size, interplanar distance, amorphization degree and lattice strain were compared for the simulated values and experimental results.     

Effect of pH, temperature and Cl− concentration on electrochemical behavior of NiTi shape memory alloy in artificial saliva

The cooperation of pH, temperature and Cl⁻ concentration on electrochemical behavior of NiTi shape memory alloy in artificial saliva was studied using orthogonal test method. The results showed that the pitting potential for NiTi in artificial saliva decreased at low and high pH; at 25^∘C, the pitting potential was the lowest compared to those at 10^∘C, 37^∘C and 50^∘C; when the Cl⁻ concentration was not less than 0.05 mol/L the pitting potential decreased with the increase of Cl⁻ concentration. The free corrosion potential of austenitic NiTi was lower than that of mixture of austenite and martensite.     

Optimal design approach for heating irregular-shaped objects in three-dimensional radiant furnaces using a hybrid genetic algorithm–artificial neural network method

In this article, a new hybrid method based on the combination of the genetic algorithm (GA) and artificial neural network (ANN) is developed to optimize the design of three-dimensional (3-D) radiant furnaces. A 3-D irregular shape design body (DB) heated inside a 3-D radiant furnace is considered as a case study. The uniform thermal conditions on the DB surfaces are obtained by minimizing an objective function. An ANN is developed to predict the objective function value which is trained through the data produced by applying the Monte Carlo method. The trained ANN is used in conjunction with the GA to find the optimal design variables. The results show that the computational time using the GA-ANN approach is significantly less than that of the conventional method. It is concluded that the integration of the ANN with GA is an efficient technique for optimization of the radiant furnaces.     

Proof testing of ceramic materials—an analytical basis for failure prediction

An analysis is presented which permits the accurate prediction of component lifetimes after proof testing. The analysis applies to crack propagation controlled fracture but can be used as a conservative prediction when crack initiation is predominant. The analytical predictions are confirmed in a series of time-to-failure measurements.

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Zusammenfassung Man berichtet von einer Analyse die die genaue Voraussagung der Lebensdauer eines Keramikbestandteiles. nach den Abnahmeversuchen, ermöglicht. Die Analyse wird auf den durch Rißausdehnung kontrolierten Bruch angewandt, kann aber auch als eine konservative Voraussagung benützt werden im Fall wo die Rißeinleitung ausschlaggebend ist.Die analytische Voraussagung wurde durch eine Reihe von Zeit bis zum Bruch Messungen bestätigt.

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Résumé On présente une analyse permettant de prédire avec précision la durée de vie d'un composant céramique après épreuve de réception. L'analyse a trait aux ruptures dont la propagation des fissures est contrôlée, mais est utilisable en tant que prédiction conservative, dans le cas où l'amorçage de la fissure est la phase prédominante.Les prédictions analytiques se voient confirmées par des séries de mesures de durée de vie à rupture.

     

Advance and Application of Thermal Spraying in Machinery Maintenance

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Retention behavior of ionizable isomers in reversed-phase liquid chromatography: a comparative study of porous graphitic carbon and octadecyl bonded silica

The retention behaviors of 36 positional isomers of ionizable substituted benzene compounds have been compared on two different packing materials: porous graphitic carbon (PGC) and octadecyl bonded silica (ODS) using 35% aqueous acetonitrile as the mobile phase. The effect of the mobile phase pH on the solute retention was studied over a range of pH values from pH 2.0 to 7.0. The retention as a function of pH was modeled using equations based on solute ionization. With PGC, the theoretical equations fitted the observed retention data for each class of solute, indicating that the retention mechanism was uniform over the whole pH range. However, with ODS, only the acidic solutes showed agreement with the theoretical model; for the amine-containing compounds, serious deviations from the theory were observed, suggesting that strongly acidic silanols gave added retention at low mobile phase pH. Overall, PGC demonstrated a higher selectivity toward positional isomers than ODS. This was attributed to the greater steric discriminating ability arising from the flat surface of the PGC compared with the more fluid nature of the ODS bonded phase.     

Biokinetics of ²¹⁰Po in rats: excretion via urine and faeces and retention in tissues and organs

Daily excretions of 21?Po from rats via urine and faeces following i.v. administration of polonium citrate, from Day 2 to Day 5, were reported, together with retentions in tissues and organs on Day 5. Emphasis is given to the methods of measurement and data quality rather than to the discussion of the observations. The authors aim to contribute data for developing or refining the biokinetic model for 21?Po metabolism.     

High impedance fault detection in power distribution networks using time?frequency transform and probabilistic neural network

An intelligent approach for high impedance fault (HIF) detection in power distribution feeders using advanced signal-processing techniques such as time-time and time-frequency transforms combined with neural network is presented. As the detection of HIFs is generally difficult by the conventional over-current relays, both time and frequency information are required to be extracted to detect and classify HIF from no fault (NF). In the proposed approach, S- and TT-transforms are used to extract time-frequency and time-time distributions of the HIF and NF signals, respectively. The features extracted using S- and TT-transforms are used to train and test the probabilistic neural network (PNN) for an accurate classification of HIF from NF. A qualitative comparison is made between the HIF classification results obtained from feed forward neural network and PNN with same features as inputs. As the combined signal-processing techniques and PNN take one cycle for HIF identification from the fault inception, the proposed approach was found to be the most suitable for HIF classification in power distribution networks with wide variations in operating conditions.     

Synthesis-microstructure-mechanical properties-wear and corrosion behavior of an Al-Si (12%)—Flyash metal matrix composite

In the present investigation, Aluminium based metal matrix composite containing up to 15% weight percentage of flyash particulates were successfully synthesized using vortex method. The properties like density, hardness, microhardness, ductility and ultimate tensile strength were investigated. The MMC produced was also subjected to corrosion, dry sliding wear and slurry erosive wear test to investigate its behavior under different material wearing conditions. The results of microhardness revealed higher hardness of the matrix material in the immediate vicinity of flyash particle. The addition of flyash particles reduces the density of composite while increasing some of their mechanical properties. The results of wear studies have shown that the resistance to wear increases with increase in percentage of flyash. Corrosion resistance decreases with increase in flyash content. The macrostructural and microstructural characteristics of the MMC were investigated with particular emphasis on the distribution of flyash particles in the matrix. Macrostructural studies have shown near uniform distribution of flyash particles in matrix. Analysis of fractured surface of tensile test specimen is also made which revealed brittle fracture behavior of MMCs.     

The Application of Standardization System in Corporate Management and Administration

This paper briefly discusses three important components of corporate standardization system and their functions and how such a system can be used in corporate management and administration. It also offers certain solutions and suggestions to some of the existing problems in the process of implementing corporate standardization system.     

Progressive damage and failure of mechanically fastened joints in CFRP laminates – Part II: Failure prediction of an industrial junction

In part II of this study, a methodology is presented to compute the failure of large-scale bolted joints in composite structures. This methodology is based both on a multilevel calculation strategy and on virtual testing. At the global level, coarse FE modelling of the structure is used to assess the load distribution between the fasteners. The most loaded fasteners are identified and the loads issued from the global calculation are used as boundary conditions for the local failure analysis, based on fast semi-empirical models. Nevertheless, in this work, instead of using experimental data, prevision of failure is achieved by fully numerical means. The parameters of the semi-empirical models are evaluated by virtual testing, using the refined FE model proposed in part I of this paper.     

Experimental characterization of microscopic damage behavior in carbon/bismaleimide composite—effects of temperature and laminate configuration

Microscopic damage behavior of high temperature CFRP, carbon/BMI (bismaleimide) under tensile loading was investigated experimentally. To clarify effects of laminate configuration and temperature on the microscopic damage behavior, 10 kinds of laminate configurations were tested at both 25 and 180 °C. Damage initiation at the free edge and progress in the width direction were observed using optical microscopy and soft X-ray radiography, respectively. Damage mechanics analysis was used to predict matrix cracking in 90° plies based on both the energy and average stress criteria. It is clarified that the critical energy release rate and critical average stress associated with matrix cracking in BMI based CFRP are larger than in epoxy-based CFRP, which certify the high crack resistance of carbon/BMI composites.