Articles written in Sadhana

    • Tolerance analysis and yield estimation using Monte Carlo simulation – case study on linear and nonlinear mechanical systems


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      Dimensions of individual components give rise to a critical dimension in an assembly, called the assembly dimension(s) or the assembly response(s). This concept is applicable to any engineering system. Thus, a variation in the individual dimension/characteristics directly affects the assembly response or the performance of the system. The random assembly of the individual dimensions gives rise to a statistical distribution of assembly response. Tolerance analysis is the estimation of resultant variation of the assembly response, for a given set of tolerances associated with individual dimensions, and the functional relationship between the individual dimensions and the assembly response. Several methods for tolerance analysis have been reported over the decades. The Monte Carlo simulation still remains the benchmark approach for testing of the precisionobtained by any other method. This paper presents two case studies to explore the insight of the methodology for tolerance analysis. The first case study is on a linear assembly while the second one is the nonlinear assembly. Three sub-studies considering (a) uniform distribution, (b) normal distribution, and (c) beta distribution, of individual dimensions have been attempted in each of the two cases. Further, in each sub-study, the tolerance analysis and the yield estimation has been carried out for the worst-case criteria, followed by analysis of theestimated yield due to reduction of assembly tolerance. The results have been presented in the form of histograms for all 2 x 3 x 3 cases

    • Fabrication and mechanical characterization of glass fiber/Al2O3 hybrid-epoxy composite


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      This paper presents the development and mechanical characterization of hybrid composite consisting of glass fiber and aluminum oxide (Al2O3) particles reinforced with epoxy matrix. The hybrid Glass Fiber Reinforced Polymer (GFRP) composite has been fabricated using different weight fractions (20%, 30%, and40%) and different particles size (20 lm, 40 lm and 60 lm) of Al2O3 along with chopped E-glass fiber reinforced with epoxy resin matrix, following the hand lay-up method. Further, the mechanical characterization has been carried out through tensile test, impact test, and bending test. The effect of process parameters on mechanical properties of the composite has been studied using response surface methodology (RSM) and analysis of variance (ANOVA). It has been found that an increase in weight percentage of Al2O3 particlesimproves the mechanical properties. However, the increase in size of Al2O3 particles shows an adverse effect on mechanical properties. The results have been optimized using the desirability function approach, and validated through a confirmation test.

    • Parametric modeling and optimization of electrochemical machining of EN-31 alloy steel


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      Electrochemical Machining (ECM) is an important advanced machining process useful to obtain good quality finish with a stress-free and crack free machined surface. Machining performance of the process is independent of the hardness of the material. Objective of this study is to assess the effect of process parameters of the ECM on material removal rate (MRR) and surface roughness (SR) during machining of EN-31 alloy steel. The EN-31 alloy steel is widely used in the manufacturing industry for making plastic molding dies, gears, automobile parts, etc. The machining experiments have been conducted according to the design of experiments based on Central Composite Face Centered Design (CCFCD) with three independent input parameters – voltage, electrolyte concentration and electrolyte flow rate, and two response parameters – material removal rate (MRR) and surface roughness (SR). Response Surface Methodology (RSM) has been used to develop the empirical relationship between the response parameters and the input process parameters. Optimization has been carried out to maximize the MRR and minimize the SR using desirability function.

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