CrAlSiN nanocomposite thin films for high-speed machining applications

CrAlSiN nanocomposite thin films with varying film chemistry were developed on tungsten carbide (WC) specimens using cylindrical cathodic arc physical vapor deposition (c-CAPVD) technique. The physical, mechanical, and tribological properties of all the films were comprehensively investigated for arriving at the film chemistry leading to the best properties with respect to mechanical applications. The best tribo-mechanical properties were obtained in films with Cr/(Al+Si) ratio of 1.2. This coating with best properties was translated on to WC drill bits for machining tests. The Al and Si content has shown major influence on the adhesion strength and phase constitution of the films, with a considerable change in residual stress too. The superior properties achieved could be attributed to the formation of a near-perfect nanocomposite structure, with the crystalline CrAlN phase surrounded by an amorphous Si₃N₄ phase. The tool life of the coated CrAlSiN tools was investigated during dry machining of EN 24 material. In comparison to the tool life of an uncoated tool and a TiAlSiN-coated tool, the best CrAlSiN coatings synthesized in this study performed exceedingly well. The present study clearly demonstrates the advantages of CrAlSiN over other existing similar coatings for high-speed machining.     

Flexural bond strength of masonry using various blocks and mortars

This paper deals with an experimental study on flexural bond strength of masonry using various blocks in combination with different mortars. Flexural bond strength of masonry has been determined by testing stack-bonded prisms using a modified bond wrench test set-up. The effect of mortar composition and strength on the masonry's flexural bond strength using three types of masonry units (stabilized mud blocks, stabilized soil-sand blocks and burnt brick) has been examined. The effect of the masonry unit's moisture content on flexural bond strength has also been studied. Increases in mortar strength lead to increased flexural bond strength for cement mortar, irrespective of the type of masonry unit. It has been found that combination mortars, such as soil-cement mortar and cement-lime mortar, lead to better bond strength compared to cement mortars. The moisture content of the masonry unit at the time of casting has displayed significant influence on the flexural bond strength of the masonry. It has been found that for each type of masonry unit, an optimum moisture content exists, beyond which the flexural bond strength falls off quickly.     

Handwritten text separation from annotated machine printed documents using Markov Random Fields

The convenience of search, both on the personal computer hard disk as well as on the web, is still limited mainly to machine printed text documents and images because of the poor accuracy of handwriting recognizers. The focus of research in this paper is the segmentation of handwritten text and machine printed text from annotated documents sometimes referred to as the task of “ink separation” to advance the state-of-art in realizing search of hand-annotated documents. We propose a method which contains two main steps—patch level separation and pixel level separation. In the patch level separation step, the entire document is modeled as a Markov Random Field (MRF). Three different classes (machine printed text, handwritten text and overlapped text) are initially identified using G-means based classification followed by a MRF based relabeling procedure. A MRF based classification approach is then used to separate overlapped text into machine printed text and handwritten text using pixel level features forming the second step of the method. Experimental results on a set of machine-printed documents which have been annotated by multiple writers in an office/collaborative environment show that our method is robust and provides good text separation performance.     

An architecture for handwritten text recognition systems

This paper presents an end-to-end system for reading handwritten page images. Five functional modules included in the system are introduced in this paper: (i) pre-processing, which concerns introducing an image representation for easy manipulation of large page images and image handling procedures using the image representation; (ii) line separation, concerning text line detection and extracting images of lines of text from a page image; (iii) word segmentation, which concerns locating word gaps and isolating words from a line of text image obtained efficiently and in an intelligent manner; (iv) word recognition, concerning handwritten word recognition algorithms; and (v) linguistic post-pro- cessing, which concerns the use of linguistic constraints to intelligently parse and recognize text. Key ideas employed in each functional module, which have been developed for dealing with the diversity of handwriting in its various aspects with a goal of system reliability and robustness, are described in this paper. Preliminary experiments show promising results in terms of speed and accuracy. Received October 30, 1998 / Revised January 15, 1999     

Online tool condition monitoring in turning titanium (grade 5) using acoustic emission: modeling

This paper presents an online prediction of tool wear using acoustic emission (AE) in turning titanium (grade 5) with PVD-coated carbide tools. In the present work, the root mean square value of AE at the chip–tool contact was used to detect the progression of flank wear in carbide tools. In particular, the effect of cutting speed, feed, and depth of cut on tool wear has been investigated. The flank surface of the cutting tools used for machining tests was analyzed using energy-dispersive X-ray spectroscopy technique to determine the nature of wear. A mathematical model for the prediction of AE signal was developed using process parameters such as speed, feed, and depth of cut along with the progressive flank wear. A confirmation test was also conducted in order to verify the correctness of the model. Experimental results have shown that the AE signal in turning titanium alloy can be predicted with a reasonable accuracy within the range of process parameters considered in this study.     

Morphological operators to extract channel networks from digital elevation models

The automated extraction of drainage networks includes generation and processing of digital elevation models (DEMs) obtained from the remotely sensed data having stereo viewing capability. The latter aspect generally aims to extract terrain features such as elevation contours and channel networks. In this technical note, the application of morphological operators to extract channel networks from the digital elevation model is described. The methodology is illustrated using a transcendentally generated DEM that bears the spatially distributed regions in grey levels, assumed as the regions of topographic reliefs and the V-shaped crenulations in successive elevation contours. The authors conclude that the adaptation of this approach to extract channel networks from DEM data is straightforward and is simple both algorithmically and computationally.     

Robustness of multimodal biometric fusion methods against spoof attacks

In this paper, we address the security of multimodal biometric systems when one of the modes is successfully spoofed. We propose two novel fusion schemes that can increase the security of multimodal biometric systems. The first is an extension of the likelihood ratio based fusion scheme and the other uses fuzzy logic. Besides the matching score and sample quality score, our proposed fusion schemes also take into account the intrinsic security of each biometric system being fused. Experimental results have shown that the proposed methods are more robust against spoof attacks when compared with traditional fusion methods.     

Information fusion for text classification — an experimental comparison

This article reports on our experiments and results on the effectiveness of different feature sets and information fusion from some combinations of them in classifying free text documents into a given number of categories. We use different feature sets and integrate neural network learning into the method. The feature sets are based on the “latent semantics” of a reference library — a collection of documents adequately representing the desired concepts. We found that a larger reference library is not necessarily better. Information fusion almost always gives better results than the individual constituent feature sets, with certain combinations doing better than the others.