CFD Analysis and Aerodynamic Effects of Add-On Devices on an Audi TT Vehicle Model
Dynamic Response Analysis of Linear Motion Stage with Track Roller Guides
Design and Development of Manual and Voice Assisted Turn Indicator for Automobiles
Vehicle Health Inspection with Enhanced Auto Diagnostics and Maintenance
Dynamic Response Characteristics of Linear Translation Stage: A Review
A Review on Mechanical and Tribological Characteristics of Hybrid Composites
Design of Oil-Ammonia Separator for Refrigeration Systems
Design and Experimental Investigation of a Natural Draft Improved Biomass Cookstove
Progressive Development of Various Production and Refining Process of Biodiesel
Optimization of Wire-ED Turning Process Parameters by Taguchi-Grey Relational Analysis
Evaluation Of Mechanical Behavior Of Al-Alloy/SiC Metal Matrix Composites With Respect To Their Constituents Using Taguchi Techniques
Multistage Extractive Desulfurization of Liquid Fuel by Ionic Liquids
Isomorphism Identification of Compound Kinematic Chain and Their Mechanism
Development of Electroplating Setup for Plating Abs Plastics
A Comprehensive Review of Biodiesel Application in IDI Engines with Property Improving Additives
Micro ultrasonic machining is being explored of late as a mechanical micromachining method to fabricate micro channels on hard and brittle materials. A significant advantage of the process is that the process can be used to machine materials irrespective of their electrical conducting unlike many other popular micro fabrication processes. In the present paper, influences of process parameters were investigated while fabricating microchannels on silicon wafers. Response Surface Methodology (RSM) approach was used to analyze the results. Abrasive mesh size, static load, slurry concentration and power rating were considered as input process parameters while surface roughness and Material Removal Rate (MRR) were considered as process responses. The response curves illustrating the individual effect and also the combined effect of process parameters have been presented.
This paper presents a new method to identify the distinct mechanisms (shortened as DM) from a given kinematic chain. The kinematic chains (shortened as KC) are represented in the form of the Weighted Physical Connectivity [WPCM] Matrix. Two structural invariants derived from the characteristic polynomials of the [WPCM] matrix of the KC are the sum of absolute characteristic polynomial coefficients (shortened as ∑ WPCM) and maximum absolute value of the characteristic polynomial coefficient (shortened as MWPCM). These invariants are used as the composite identification number of a KC and mechanisms. It is capable of detecting DM in all types of simple jointed planer KC with same or different kinematic pairs (shortened as KP). This study will help the designer to select the best KC and mechanisms to perform the specified task at the conceptual stage of design. The application of this study is in research and development industries. The proposed method has been explained with the help of examples and does not require any test for isomorphism separately.
Selection and evaluation of suppliers is a burning issue in the context of modern manufacturing environment to increase organizational competitiveness due to an extensive variety of customer demands. It has become more and more complicated to meet the challenges of international competitiveness, and as the decision makers need to assess a wide range of alternative suppliers based on a set of conflicting criteria. Because of these reasons, supplier selection has got considerable attention by the academicians and researchers. Although, a huge number of mathematical approaches is now available for supplier selection under discrete manufacturing environment, this paper explores the applicability of almost a new multi-criteria decision-making approach, i.e. range of value method (ROVM) for supplier selection. The proposed method is used to rank the alternative suppliers, for which several ordinal and cardinal requirements are considered simultaneously. Two illustrative examples are cited which prove that ROVM method can be very useful to solve real time supplier selection problems. In each example, a list of all the possible choices from the best to the worst suitable suppliers is obtained, which almost match with the rankings as derived by the past researchers.
The objective of the work is to find out microstructure and properties of two zirconium diboride based ceramic composites which were obtained from ?ne commercial zirconium diboride powders and the addition of zirconium carbide, Silicon carbide, Silicon nitride as secondary phases and correlation between microstructure-thermo mechanical properties of two Ultra High Temperature Ceramic Composites (UHTCC) based upon the different secondary phases composition. After that characterization, the influence of composition in microstructure on elevated temperature behavior of zirconium diboride based ultra high temperature ceramic composites are analysed. Micro - structural analyses by Optical and SEM showed oxidation-induced surface modifications of ZrB /SiC materials with oxide 2 layers composed of silica, boron and zirconia (at higher temperature). These composites were prepared from zirconium diboride, zirconium carbide, Silicon carbide and Silicon nitride powders by ball milling and hot pressing. These two UHTCCs were fabricated by hot pressing into 25 mm diameter and 4 mm thickness discs. The measurement of the mechanical properties of two ceramic composite were done by pulse-echo ultrasonic testing. Indentation water quenched method was used for measuring thermal stresses in UHTCCs during characterizing thermo mechanical properties and thermal shock behavior.
Multi-Response optimization of response variables having opposite nature is cumbersome to obtain without use of optimization techniques. In this research work, Grey relational analysis and Taguchi Design approach are used for multi response optimization of metal removal rate and surface roughness during turning of . Taguchi orthogonal array L is used to design the experiment. Signal to Noise (S/N) ratio of both responses i.e. metal removal rate 9 and surface roughness are used to calculate grey relational grade. AISI 1040 MS bars Analysis of Variance (ANOVA) is used to identify the significance of process parameters on grey relational grade and shows that feed rate and spindle speed are most significant process parameters for both the responses. The confirmation experiments show that Taguchi-Grey relation analysis can be successfully utilized to obtain the multi-response optimization.