Indentation and scratch behavior of functionalized MWCNT–PMMA composites at the micro/nanoscale

Polymethylmethacrylate (PMMA) and functionalized multiwalled carbon nanotube (F‐MWCNT) based composite films were prepared using solution casting method. Nanoindentation and scratch measurements were carried out to study the influence of F‐MWCNT as the reinforcement on the mechanical properties of the composite at the sub‐micron scale. The composites were prepared with varying weight percentages of F‐MWCNT in the PMMA matrix. The composites containing an adequate amount (0.25 wt%) of F‐MWCNT was found to demonstrate the maximum nanomechanical properties, viz. hardness, elastic modulus, recovery index. Scratch resistance measured in terms of coefficient of friction, also showed maximum value for the PMMA composite reinforced with 0.25 wt% of F‐MWCNT. POLYM. COMPOS., 35:948–955, 2014. © 2013 Society of Plastics Engineers     

Quality of rainbow trout chilled-stored after post-catch holding

The shelf-life of headed and gutted rainbow trout (Salmo gairdneri) was evaluated over period of storage at 0°C. There were three groups of samples: (i) fish held at 10°C for 6h and iced, (ii) fish held at 20°C for 6h and iced and (iii) fish held at 30°C for 6h and iced. Quality was assessed over the period of iced storage by measurements of total volatile bases (TVB), hypoxanthine, thiobarbituric acid, pH, water holding capacity (WHC) and tissue compressibility (TC). The results indicated that holding at various time/temperature periods before icing influences quality over the period of iced storage. Deterioration in quality was most pronounced when the fish had been held at high ambient temperatures of 30°C. Hypoxanthine values appeared to give an accurate indication of quality of freshwater fish. There was a linear increase in hypoxanthine values over the period of iced storage for all samples. However, for trout held at 30°C for 6 h before icing the values appeared to be highest. The coefficients of linear regression for fish iced immediately on receipt were lowest per day. It is suggested that hypoxanthine values may be used as chemical indicators of quality of freshwater trout.     

Materials selection using complex proportional assessment and evaluation of mixed data methods

Material selection is a very fast growing multi-criteria decision-making (MCDM) problem involving a large number of factors influencing the selection process. Proper choice of material is a critical issue for the success and competitiveness of the manufacturing organizations in the global market. Selection of the most appropriate material for a particular engineering application is a time consuming and expensive process where several candidate materials available in the market are taken into consideration as the tentative alternatives. Although a large number of mathematical approaches is now available to evaluate, select and rank the alternative materials for a given engineering application, this paper explores the applicability and capability of two almost new MCDM methods, i.e. complex proportional assessment (COPRAS) and evaluation of mixed data (EVAMIX) methods for materials selection. These two methods are used to rank the alternative materials, for which several requirements are considered simultaneously. Two illustrative examples are cited which prove that these two MCDM methods can be effectively applied to solve the real time material selection problems. In each example, a list of all the possible choices from the best to the worst suitable materials is obtained which almost match with the rankings as derived by the past researchers.     

Mechanisms of interdiffusion in Pd-Cu thin film diffusion couples

Interdiffusion in sputter-deposited polycrystalline Pd-Cu bilayers (thickness of each sublayer: 50 nm) was studied in the temperature range 175 °C-250 °C by sputter-depth profiling in combination with Auger electron spectroscopy. X-ray diffraction and transmission electron microscopy investigations revealed that the layers are polycrystalline, consisting of columnar grains separated by grain boundaries oriented more or less perpendicularly to the film surface. Considerable diffusional intermixing occurred in the studied temperature range, which was accompanied by the sequential formation of (ordered) phases Cu₃Pd and CuPd. Volume interdiffusion coefficients were determined using the so-called ‘centre-gradient’ and ‘plateau-rise’ methods. Grain-boundary diffusion coefficients of Pd through Cu grain boundaries were determined by the Whipple-Le Claire method and grain-boundary diffusion coefficients of Cu through Pd grain boundaries were determined by the Hwang-Balluffi method. It was found that both volume and grain-boundary diffusion coefficients decreased roughly exponentially with annealing time. Activation energies were determined which pertain to the same (defect) microstructure at each temperature. The differences with literature results for macroscopic diffusion couples were discussed.     

Wavelet transformation based multi-time scaling method for crystal plasticity FE simulations under cyclic loading

Microstructure based mechanistic calculations, coupled with physically motivated crack initiation criterion, can provide effective means to predict fatigue cracking in polycrystalline materials. However the accommodation of large number of cycles to failure, as observed in the experiments, could be computationally exhaustive to simulate using conventional single time scale finite element analysis. To meet this challenging requirement, a novel wavelet transformation based multi-time scaling algorithm is proposed for accelerated crystal plasticity finite element simulations in this paper. An advantage over other conventional methods that fail because of assumptions of periodicity etc., is that no assumption of scale separation is needed with this method. The wavelet decomposition naturally retains the high frequency response through the wavelet basis functions and transforms the low frequency material response into a “cycle scale” problem with monotonic evolution. The method significantly enhances the computational efficiency in comparison with conventional single time scale integration methods. Adaptivity conditions are also developed for this algorithm to improve accuracy and efficiency. Numerical examples for validating the multi-scaling algorithm are executed for a one dimensional viscoplastic problem and a 3D crystal plasticity model of polycrystalline Ti alloy under the cyclic loading conditions.     

Influence of melt treatments on sliding wear behavior of Al–7Si and Al–7Si–2.5Cu cast alloys

The microstructures and dry sliding wear behavior of Al–7Si and Al–7Si–2.5Cu cast alloys were studied after various melt treatments like grain refinement and modification. Results indicate that combined grain refined and modified Al–7Si–2.5Cu cast alloys have microstructures consisting of uniformly distributed α-Al grains, eutectic Al– silicon and fine CuAl₂ particles in the interdendritic region. These alloys exhibited better wear resistance in the cast condition compared with the same alloy subjected to only grain refinement or modification. The improved wear resistances of Al–7Si–2.5Cu cast alloys are related to the refinement of the aluminum grain size, uniform distribution of eutectic Al-silicon and fine CuAl₂ particles in the interdendritic region resulting from combined refinement and modification. This paper attempts to investigate the influence of the microstructural changes in the Al–7Si and Al–7Si–2.5Cu cast alloys by grain refinement, modification and combined action of both on the sliding wear behavior.     

Reliability of linear structures with parameter uncertainty under non-stationary earthquake

The present work aims towards the development of a general framework of time varying unconditional reliability evaluation of linear elastic multi degree of freedom structures with uncertain parameter subjected to the generalized earthquake ground motion, a non-stationary process both in amplitude and frequency content. The formulation is developed in double frequency spectrum to derive the generalized power spectral density function of the structural responses. The time varying reliability is evaluated using conditional crossing rate following the Vanmarcke’s modification. The perturbation based stochastic finite element method is utilized in deriving unconditional reliability. An idealized three dimensional dam structure subjected to El Centro (1940) earthquake is taken up to elucidate the proposed unconditional time varying reliability computation procedure based on the maximum top displacement and base shear criteria. The results are presented to compare the change in reliabilities of the uncertain system with that of deterministic system and associated variance of the reliability due to parameter uncertainty.     

QFD-based expert system for non-traditional machining processes selection

Selection of an optimal non-traditional machining (NTM) process for generating a desired feature on a given material requires the consideration of several factors among which the type of the workpiece material and shape to be machined are the most significant ones. This paper presents a quality function deployment (QFD) based methodology to ease out the optimal NTM process selection procedure. It includes the design of a QFD-based expert system that can automate the decision making process with the help of graphical user interfaces and visual aids. The developed expert system employs the use of a house of quality (HOQ) matrix for comparison of the relevant product and process characteristics. The weights obtained for various process characteristics are utilized to estimate an overall score for each of the NTM processes. Finally, if some of the NTM processes satisfy certain critical criteria, they are again compared with each other on the basis of their overall scores and the process having the maximum score is selected as the optimal choice.     

Neural-Network-Based Robust Linearization and Compensation Technique for Sensors Under Nonlinear Environmental Influences

A novel artificial neural network (NN)-based technique is proposed for enabling smart sensors to operate in harsh environments. The NN-based sensor model automatically linearizes and compensates for the adverse effects arising due to nonlinear response characteristics and nonlinear dependency of the sensor characteristics on the environmental variables. To show the potential of the proposed NN-based technique, we have provided results of a smart capacitive pressure sensor (CPS) operating under a wide range of temperature variation. A multilayer perceptron is utilized to transfer the nonlinear CPS characteristics at any operating temperature to a linearized response characteristics. Through extensive simulated experiments, we have shown that the NN-based CPS model can provide pressure readout with a maximum full-scale error of only 1.5% over a temperature range of 50 to 200 with excellent linearized response for all the three forms of nonlinear dependencies considered. Performance of the proposed technique is compared with a recently proposed computationally efficient NN-based extreme learning machine. The proposed multilayer perceptron based model is tested by using experimentally measured real sensor data, and found to have satisfactory performance.