The role of surface texture on friction and transfer layer formation during repeated sliding of Al-4Mg against steel

In the present investigation, various kinds of textures, namely, unidirectional, 8-ground, and random were attained on the die surfaces. Roughness of the textures was varied using different grits of emery papers or polishing powders. Then pins made of Al-4Mg alloys were slid against steel plates at various numbers of cycles, namely, 1, 3, 5, 10 and 20 using pin-on-plate reciprocating sliding tester. Tests were conducted at a sliding velocity of 2 minis in ambient conditions under both dry and lubricated conditions. A constant normal load of 35 N was applied in the tests. The morphologies of the worn surfaces of the pins and the formation of transfer layer on the counter surfaces were observed using a scanning electron microscope. Surface roughness parameters of the plates were measured using an optical profilometer. In the experiments, it was observed that the coefficient of friction and formation of the transfer layer depend on the die surface textures under both dry and lubricated conditions. More specifically, the coefficient of friction decreases for unidirectional and 8-ground surfaces while for random surfaces it increases with number of cycles. However, the coefficient of friction is highest for the sliding perpendicular to the unidirectional textures and least for the random textures under both dry and lubricated conditions. The difference in friction values between these two surfaces decreases with increasing number of cycles. The variation in the coefficient of friction under both dry and lubrication conditions is attributed to the change in texture of the surfaces during sliding. (C) 2011 Elsevier B.V. All rights reserved.

Friction and transfer layer formation in polymer-steel tribo-system: Role of surface texture and roughness parameters

In the present investigation, various kinds of surface textures were attained on the steel plates. Roughness of the textures was varied using various grinding or polishing methods. The surface textures were characterized in terms of roughness parameters using an optical profilometer. Then experiments were conducted using an inclined pin-on-plate sliding apparatus to identify the role of surface texture and its roughness parameters on coefficient of friction and transfer layer formation. In the experiments, a soft polymer (polypropylene) was used for the pin and hardened steel was used for the plate. Experiments were conducted at a sliding velocity of 2 minis in ambient conditions under both dry and lubricated conditions. The normal load was varied from 1 to 120 N during the tests. The morphologies of the worn surfaces of the pins and the formation of a transfer layer on the steel plate surfaces were observed using a scanning electron microscope. Based on the experimental results, it was observed that the transfer layer formation and the coefficient of friction along with its two components, namely adhesion and plowing, were controlled by the surface texture of the harder mating surfaces and were less dependent of surface roughness (R(a)) of the harder mating surfaces. The effect of surface texture on the friction was attributed to the variation of the plowing component of friction for different surfaces. Among the various surface roughness parameters studied, the mean slope of the profile, Delta(a), was found to most accurately characterize variations in the friction and wear behavior. (C) 2011 Elsevier B.V. All rights reserved.

Microstructure and texture evolution during accumulative roll bonding of aluminium alloy AA5086

In the present investigation, a strongly bonded strip of an aluminium-magnesium based alloy AA5086 is successfully produced through accumulative roll bonding (ARB). A maximum of up to eight passes has been used for the purpose. Microstructural characterization using electron backscatter diffraction (EBSD) technique indicates the formation of submicron sized (similar to 200-300 nm) subgrains inside the layered microstructure. The material is strongly textured where individual layers possess typical FCC rolling texture components. More than three times enhancement in 0.2% proof stress (PS) has been obtained after 8 passes due to grain refinement and strain hardening. (C) 2011 Elsevier B.V. All rights reserved.

Online Handwriting Recognition of Tamil Script Using Fractal Geometry

We present a fractal coding method to recognize online handwritten Tamil characters and propose a novel technique to increase the efficiency in terms of time while coding and decoding. This technique exploits the redundancy in data, thereby achieving better compression and usage of lesser memory. It also reduces the encoding time and causes little distortion during reconstruction. Experiments have been conducted to use these fractal codes to classify the online handwritten Tamil characters from the IWFHR 2006 competition dataset. In one approach, we use fractal coding and decoding process. A recognition accuracy of 90% has been achieved by using DTW for distortion evaluation during classification and encoding processes as compared to 78% using nearest neighbor classifier. In other experiments, we use the fractal code, fractal dimensions and features derived from fractal codes as features in separate classifiers. While the fractal code is successful as a feature, the other two features are not able to capture the wide within-class variations.

Unrestricted Kannada online handwritten akshara recognition using SDTW

In this paper, we present an unrestricted Kannada online handwritten character recognizer which is viable for real time applications. It handles Kannada and Indo-Arabic numerals, punctuation marks and special symbols like $, &, # etc, apart from all the aksharas of the Kannada script. The dataset used has handwriting of 69 people from four different locations, making the recognition writer independent. It was found that for the DTW classifier, using smoothed first derivatives as features, enhanced the performance to 89% as compared to preprocessed co-ordinates which gave 85%, but was too inefficient in terms of time. To overcome this, we used Statistical Dynamic Time Warping (SDTW) and achieved 46 times faster classification with comparable accuracy i.e. 88%, making it fast enough for practical applications. The accuracies reported are raw symbol recognition results from the classifier. Thus, there is good scope of improvement in actual applications. Where domain constraints such as fixed vocabulary, language models and post processing can be employed. A working demo is also available on tablet PC for recognition of Kannada words.

Comparison of HMM and SDTW for Tamil handwritten character recognition

In this paper, we compare the experimental results for Tamil online handwritten character recognition using HMM and Statistical Dynamic Time Warping (SDTW) as classifiers. HMM was used for a 156-class problem. Different feature sets and values for the HMM states & mixtures were tried and the best combination was found to be 16 states & 14 mixtures, giving an accuracy of 85%. The features used in this combination were retained and a SDTW model with 20 states and single Gaussian was used as classifier. Also, the symbol set was increased to include numerals, punctuation marks and special symbols like $, & and #, taking the number of classes to 188. It was found that, with a small addition to the feature set, this simple SDTW classifier performed on par with the more complicated HMM model, giving an accuracy of 84%. Mixture density estimation computations was reduced by 11 times. The recognition is writer independent, as the dataset used is quite large, with a variety of handwriting styles.

Evolution of sub-micron grain size and weak texture in magnesium alloy Mg-3Al-0.4Mn by a modified multi-axial forging process

This is the first successful attempt to produce simultaneously ultrafine grain size and weak texture in a single-phase magnesium alloy Mg-3Al-0.4Mn through an optimal choice of processing parameters in a modified multi-axial forging (MAF) process. An average grain size of similar to 0.4 mu m and a weak texture could be achieved. This has led to an increase in the strength as well as room-temperature ductility (55%). The plot of the yield loci shows a decrease in anisotropy after MAF. (C) 2011 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Evolution of texture and grain boundary microstructure in two-phase (α + β) brass during recrystallization

The evolution of texture and microstructure during recrystallization is studied for two-phase copper alloy (Cu–40Zn) with a variation of the initial texture and microstructure (hot rolled and solution treated) as well as the mode of rolling (deformation path: uni-directional rolling and cross rolling). The results of bulk texture have been supported by micro-texture and microstructure studies carried out using electron back scatter diffraction (EBSD). The initial microstructural condition as well as the mode of rolling has been found to alter the recrystallization texture and microstructure. The uni-directionally rolled samples showed a strong Goss and BR {236}385 component while a weaker texture similar to that of rolling evolved for the cross-rolled samples in the α phase on recrystallization. The recrystallization texture of the β phase was similar to that of the rolling texture with discontinuous 101 α and {111} γ fiber with high intensity at {111}101. For a given microstructure, the cross-rolled samples showed a higher fraction of coincident site lattice Σ3 twin boundaries in the α phase. The higher fraction of Σ3 boundaries is explained on the basis of the higher propensity of growth accidents during annealing of the cross-rolled samples. The present investigation demonstrates that change in strain path, as introduced during cross-rolling, could be a viable tool for grain boundary engineering of low SFE fcc materials.

Self assembly of bivalent glycolipids on single walled carbon nanotubes and their specific molecular recognition properties

Solubilization of single walled carbon nanotubes (SWNTs) in aqueous milieu by self assembly of bivalent glycolipids is described. Thorough analysis of the resulting composites involving Vis/near-IR spectroscopy, surface plasmon resonance, confocal Raman and atomic force microscopy reveals that glycolipid-coated SWNTs possess specific molecular recognition properties towards lectins.

Prototype learning methods for online handwriting recognition

In this paper, we study different methods for prototype selection for recognizing handwritten characters of Tamil script. In the first method, cumulative pairwise- distances of the training samples of a given class are used to select prototypes. In the second method, cumulative distance to allographs of different orientation is used as a criterion to decide if the sample is representative of the group. The latter method is presumed to offset the possible orientation effect. This method still uses fixed number of prototypes for each of the classes. Finally, a prototype set growing algorithm is proposed, with a view to better model the differences in complexity of different character classes. The proposed algorithms are tested and compared for both writer independent and writer adaptation scenarios.

Automatic Recognition of Facial Features and Landmarking of Digital Human Head

Ergonomic design of products demands accurate human dimensions-anthropometric data. Manual measurement over live subjects, has several limitations like long time, required presence of subjects for every new measurement, physical contact etc. Hence the data currently available is limited and anthropometric data related to facial features is difficult to obtain. In this paper, we discuss a methodology to automatically detect facial features and landmarks from scanned human head models. Segmentation of face into meaningful patches corresponding to facial features is achieved by Watershed algorithms and Mathematical Morphology tools. Many Important physiognomical landmarks are identified heuristically.