This paper presents the experimental study, development of mathematical model and parametric optimization for surface roughness in turning D2 steel using TiN coated carbide insert using Taguchi parameter design and response surface methodology. The experimental plan and analysis was based on the Taguchi L27 orthogonal array taking cutting speed (v), feed (f) and depth of cut (d) as important cutting parameters. The influence of the machining parameters on the surface finish has also been investigated and the optimum cutting condition for minimizing the surface roughness is evaluated. The optimal parametric combination for TiN coated cutting insert is found to be v3-f1-d3. The ANOVA result shows that feed the most significant process parameter on surface roughness followed by depth of cut. The cutting speed is found to be insignificant from the study. The RSM model shows good accuracy between predicted values and experimental values with 95% confidence intervals and adequate. It is concluded that the developed RSM model can be effectively utilized to predict the surface roughness in turning D2 steel.

Industrial robots are mainly employed to perform repetitive and hazardous production jobs, multi-shift operations etc. to reduce the delivery time, improve the work environment, lower the production cost and even increase the product range to fulfill the customers’ needs. When a choice is to be made from among several alternative robots for a given industrial application, it is necessary to compare their performance characteristics in a decisive way. As the industrial robot selection problem involves multiple conflicting criteria and a finite set of candidate alternatives, different multi-criteria decision-making (MCDM) methods can be effectively used to solve such type of problem. In this paper, ten most popular MCDM methods are considered and their relative performance are compared with respect to the rankings of the alternative robots as engaged in some industrial pick-n-place operation. It is observed that all these methods give almost the same rankings of the alternative robots, although the performance of WPM, TOPSIS and GRA methods are slightly better than the others. It can be concluded that for a given industrial robot selection problem, more attention is to be paid on the proper selection of the relevant criteria and alternatives, not on choosing the most appropriate MCDM method to be employed.

This study presents optimal ordering policies for retailer when supplier offers cash discount and two progressive payment schemes for paying of purchasing cost. If the retailer pays the outstanding amount before or at first trade credit period M, the supplier provides r_1cash discount and does not charge any interest. If the retailer pays after M but before or at the second trade period N offered by the supplier, the supplier provides r_2 cash discount and charges interest on unpaid balance at the rate ?Ic?_1 . If retailer pays the balance after N, (N & GT; M) then the supplier does not provide any cash discount but charges interest on unpaid balance at the rate ?Ic?_2. The primary objective of this paper is to minimize the total cost of inventory system. This paper develops an algebraic approach to determine the optimal cycle time, optimal order quantity and optimal relevant cost. Numerical example are also presented to illustrate the result of propose model and solution procedure developed.

In today & apos; s global marketplace, individual firms do not compete as independent entities rather as an integral part of a supply chain. Uncertainty is the main attribute in managing the supply chains. Accordingly, we develop a (Q, R) inventory model with service level constraint and variable lead-time in fuzzy-stochastic environment. In addition, the triangular fuzzy numbers counts upon lead-time are used to construct fuzzy-stochastic lead-time demand. Using credibility criterion, the expected shortages are calculated. Without loss of generality, we assume that all the observed values of the fuzzy random variable, representing the demand are triangular fuzzy numbers. Consequently, the value of total expected cost in the fuzzy sense is derived using the expected value criterion or credibility criterion. To determine an optimal policy, a numerical technique is presented and the results are analyzed using scan and zoom for constraint optimization. Finally, in order to demonstrate the accuracy and effectiveness of the proposed model, numerical example and sensitivity analysis are also included.

The recent development of world is being adversely affected by the scarcity of power and energy. To survive in the next generation, it is thus necessary to explore the non-conventional energy sources and efficiently consume the available sources. For efficient exploitation of the existing energy sources, a great scope lies in the use of Rankin cycle-based thermal power plants. Today, the gross efficiency of Rankin cycle-based thermal power plants is less than 28% which has been increased up to 40% with reheating and regenerative cycles. But, it can be further improved up to 47% by using supercritical power plant technology. Supercritical power plants use supercritical boilers which are able to withstand a very high temperature (650-720?C) and pressure (22.1 MPa) while producing superheated steam. The thermal efficiency of a supercritical boiler greatly depends on the material of its different components. The supercritical boiler material should possess high creep rupture strength, high thermal conductivity, low thermal expansion, high specific heat and very high temperature withstandability. This paper considers a list of seven supercritical boiler materials whose performance is evaluated based on seven pivotal criteria. Given the intricacy and difficulty of this supercritical boiler material selection problem having interactions and interdependencies between different criteria, this paper applies fuzzy analytic network process to select the most appropriate material for a supercritical boiler. Rene 41 is the best supercritical boiler material, whereas, Haynes 230 is the worst preferred choice.

The present paper applies Principal Component Analysis (PCA) for grouping of machines and parts so that the part families could be processed in the cells formed by those associated machines. An incidence matrix with binary entries has been chosen to apply this methodology. After performing the eigenanalysis of the principal component and observing the component loading plot of the principal components, the machine groups and part families are identified and arranged to form machine-part cells. Later, the same methodology is extended and it is applied to nine other machine-part matrices collected from literature for the validation of the proposed methodology. The goodness of cell formation is compared using the grouping efficacy and the potential of eigenanalysis in cell formation is established over the best available results using the various established methodologies. The result shows that in 70% of the problems there is an increase in grouping efficacy and in 30% problem, the performance measure of cell formation is as good as the best result from literature.

Information technology induced communications (ICTs) have revolutionized the operational aspects of service sector and have triggered a perceptual shift in service quality as rapid dis-intermediation has changed the access-mode of services on part of the consumers. ICT-enabled services further stimulated the perception of automated service quality with renewed dimensions and there subsequent significance to influence the behavioural outcomes of the consumers. Customer Relationship Management (CRM) has emerged as an offshoot to technological breakthrough as it ensured service-encapsulation by integrating people, process and technology. This paper attempts to explore the relationship between automated service quality and its behavioural consequences in a relatively novel business-philosophy – CRM. The study has been conducted on the largest public sector bank of India - State bank of India (SBI) at Kolkata which has successfully completed its decade-long operational automation in the year 2008. The study used structural equation modeling (SEM) to justify the proposed model construct and causal loop diagramming (CLD) to depict the negative and positive linkages between the variables.

Form deviation of machined components need to be controlled within the required tolerance values for proper assembly and to serve the intended functional requirements. Methods like minimum zone circle (MZC) method, minimum circumscribed circle (MCC) method, maximum inscribed circle (MIC) method and least square circle (LSC) method are used to evaluate roundness error. Roundness error evaluation includes collection of co-ordinate points on the surface of the compo-nent and measurement using any of the above methods. Since, manual measurement of roundness error from these coordinate points is time consuming and less accurate, use of algorithms is highly appreciated. A detailed study of various optimization techniques showed that all evolutionary and swarm intelligence-based optimization algorithms require common control parameters and algorithm specific parameters. Improper tuning of these parameters either increases the computational effort or results in local optimal solution. Teaching Learning Based Optimization (TLBO) algorithm is used in this work as it does not require any algorithm specific parameters for the evaluation of roundness error. The results obtained are then compared with the results of Particle Swarm Optimization (PSO) algorithm to know the merits and demerits of both the algorithms when used for form measurement.

The NanoMQL technique is used to overcome the limitations of wet grinding due to economic and ecological problems. The performance measure is largely influenced by the process parameters such as table speed, depth of cut, air pressure, coolant flow rate and nanofluid concentration. In this paper, the performance of NanoMQL technique in terms of surface roughness was evaluated for hard and soft EN31 steel. The Experiments were conducted by response surface methodology (RSM) using statistical software to develop regression model of surface roughness and optimization was carried out using Jaya algorithm. The result shows that lowest value of surface roughness was obtained for NanoMQL of hard steel in comparison with soft steel under grinding environ-ments such as wet, MQL and NanoMQL. Hence to improve the performance of soft steel, the modeling and optimization of surface roughness were carried out. The significant parameters were considered for model development and validity of model determined through ANOVA (Analysis of variance). Lastly, the optimal values were determined using Jaya algorithm for minimum surface roughness and the percentage error observed to be close with the experimental test.

WEDM has evolved as a well admired technique for machining of difficult to cut materials such as superalloys. WEDM produces intricate shape and profiles of superalloys by thermoelectric erosion process. But as the process is carried out at very high temperature, the formation of heat affected zone, microcracks, recast layer, porosity etc. resulted in decreased surface integrity of machined specimen and becomes a big problem in WEDM. Discharge energy is the most influencing param-eters that affect the surface integrity of WEDmed samples. In this study, Inconel 825, widely used in aerospace industry for making of combustor casing and turbine blades, was machined with WEDM under different discharge energy. The surface topography of the WEDMed specimen was carried cut by using SEM, XRD and EDX techniques. It was observed from the SEM micrograph that the machined surface includes cracks, pockmarks, craters, and pulled out material. The density and sice of craters increase with increase in discharge energy. Surface crack density of 0.0138 μm/μm2 and recast layer thickness of 34.62μm was obtained for the machined sample at high value of discharge energy while at low value surface crack density of 0.0016 μm/μm2 and recast layer thickness of 20.99μm was observed. EDX and XRD analysis of the specimen showed that an ap-preciable amount of elements viz. Fe (Ferrous), Cr (Chromium), Cu (Copper), Ni (Nickel) are mi-grated to the surface of the workpiece at high value of pulse on time.