Peeling of heterogeneous thin films: Effect of bending stiffness,adhesion energy,and level of heterogeneity

The peeling behaviour of a heterogeneous thin film bonded to a rigid substrate was investigated by using both experiments and finite element modeling. The enhancement in peel force was studied specifically for heterogeneous thin films with periodic stiff and compliant portions along the length. Peel tests with homogeneous thin films (uniform film thickness) showed that the maximum peel force can be observed before the onset of steady state peeling process. Moreover, this maximum peel force was observed to be a function of the bending stiffness of the film and adhesion energy at the film-substrate interface. For the heterogeneous thin films, maximum peel force can be observed either before the onset of steady state or when the peel front traverses from compliant to the stiff portion of the film. The three-dimensional finite element model, based on cohesive zone technique was developed, which provided further insight into the enhancement in peel force. The maximum force was shown to be dependent on the level of heterogeneity in addition to adhesion energy and bending stiffness as was observed with homogeneous films. The improvement in peel force was found to be prevalent at relatively low adhesion energy. This study may be helpful for the better design of homogeneous and heterogeneous thin film-substrate systems having improved bonding strength.     

Evaluation of the performance of backpropagation and radial basis function neural networks in predicting the drill flank wear

This study compares the performance of backpropagation neural network (BPNN) and radial basis function network (RBFN) in predicting the flank wear of high speed steel drill bits for drilling holes on mild steel and copper work pieces. The validation of the methodology is carried out following a series of experiments performed over a wide range of cutting conditions in which the effect of various process parameters, such as drill diameter, feed-rate, spindle speed, etc. on drill wear has been considered. Subsequently, the data, divided suitably into training and testing samples, have been used to effectively train both the backpropagation and radial basis function neural networks, and the individual performance of the two networks is then analyzed. It is observed that the performance of the RBFN fails to match that of the BPNN when the network complexity and the amount of data available are the constraining factors. However, when a simpler training procedure and reduced computational times are required, then RBFN is the preferred choice.     

VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change

International Journal of Computer Vision - Visual place recognition (VPR) is the process of recognising a previously visited place using visual information, often under varying appearance...     

Survey of denoising,segmentation and classification of magnetic resonance imaging for prostate cancer

Multimedia Tools and Applications - Prostate cancer (PCa) has become the second most dreadful cancer in men after lung cancer. Traditional approaches used for treatment of PCa were manual, time...     

Investigation of multiple heterogeneous relationships using a q-rung orthopair fuzzy multi-criteria decision algorithm

Neural Computing and Applications - Q-rung orthopair fuzzy (q-ROF) set is one of the powerful tools for handling the uncertain multi-criteria decision-making (MCDM) problems, various MCDM methods...     

Auto-Discovering Configurations for Service Management

Determining service configurations is essential for effective service management. In this paper we describe a model-driven approach for service configuration auto-discovery. We develop metrics for performance and scalability analysis of such auto-discovery mechanisms. Our approach addresses several problems in auto-discovery: specification of what services to discover, how to efficiently distribute service discovery, and how to match instances of services into related groups. We use object-oriented models for discovery specifications, a flexible bus-based architecture for distribution and communication, and a novel multi-phased, instance matching approach. We have applied our approach to typical e-commerce services, Enterprise Resource Planning applications, like SAP, and Microsoft Exchange services running on a mixture of Windows and Unix platforms. The main contribution of this work is the flexibility of our models, architecture and algorithms to address discovery of a multitude of services.     

Using the causal domain to specify and verify distributed programs

A system for specification and proof of distributed programs is presented. The method is based directly on the partial order of local states (poset) and avoids the notions of time and simultaneity. Programs are specified by documenting the relationship between local states which are adjacent to each other in the poset. Program properties are defined by stating properties of the poset. Many program properties can be expressed succinctly and elegantly using this method because poset properties inherently account for varying processor execution speeds. The system utilizes a proof technique which uses induction on the complement of the causally precedes relation and is shown to be useful in proving poset properties. We demonstrate the system on three example algorithms: vector clocks, mutual exclusion, and direct dependency clocks. Received 25 July 1995 / 16 September 1996