Turning hardened component with PCBN and ceramic inserts have been extensively used recently and replaces traditional grinding operation. The use of inexpensive multilayer coated carbide insert in hard turning is lacking and hence there is a need to investigate the potential and applicability of such tools in turning hardened steels. An attempt has been made in this paper to have a study on turning hardened AISI 4340 steel (47 ± 1 HRC) using coated carbide inserts (TiN/TiCN/Al2O3/ZrCN) under dry environment. The aim is to assess the tool life of inserts and evolution of flank wear with successive machining time. From experimental investigations, the gradual growth of flank wear for multilayer coated insert indicates steady machining without any premature tool failure by chipping or fracturing. Abrasion is found to be the dominant wear mechanisms in hard turning. Tool life of multilayer coated carbide inserts has been found to be 31 minute and machining cost per part is Rs.3.64 only under parametric conditions chosen i.e. v = 90 m/min, f = 0.05 mm/rev and d = 0.5 mm. The mathematical model shows high determination coefficient, R2 (99%) and fits the actual data well. The predicted flank wear has been found to lie very close to the experimental value at 95% confidence level. This shows the potential and effectiveness of multilayer coated carbide insert used in hard turning applications.

The bullwhip effect is a phenomenon in forecast-driven distribution channels, which is associated with a trend of larger and larger swings in inventory in response to changes in demand, as we look at firm further back in the supply chain for a particular product. This paper deals with the bullwhip effect in a multi-echelon multiple products supply chain with correlated market demands. The downstream retailer procures products from an un-capacitated upstream supplier to meet the correlated market demands of multiple products. This paper proposes a new method based on demand forecasting technique and uses a simple moving average to eliminate the bullwhip effect, which is proved to be effective under some circumstances.

A finite time horizon inventory problem for a deteriorating item having two separate warehouses, one with an own warehouse (OW) of finite dimension and the other with a rented warehouse (RW), is developed in fuzzy environment. Due to different preserving facilities and storage environment, inventory holding cost is considered to be different in different warehouses. The demand rate of item depends on the selling price and is displayed stocked level in own warehouse. Shortages are allowed and backlogged them partially, which depends on the duration of stock out. The stocks of RW are transported to OW in continuous release pattern. Due to impreciseness in nature, total revenue and shortage cost are assumed to be imprecise in nature too. By using the fuzzy goal programming method, multi-objective problem is converted into single objective function. The model is illustrated numerically and the results are presented in tabular form.

In this paper, an inventory model of two-warehouse is considered, which evaluates the impact of a reduction rate in the selling price with volume flexibility. In real life, there are many products, which may decay or deteriorate or become obsolete. Therefore, one alternative is to clear the stock by selling a large amount of items at reduced prices. Taking this concept into account, this paper considers a fixed demand rate at the beginning of planning until the certain time point occurs, while demand is assumed to follow the pattern of nonlinear and non-decreasing power function of the reduction rate. The total cost function includes warehouse and rented warehouse holding costs, set up cost and the production cost. Numerical illustrations are given to exemplify the model and the proposed model is solved using a Genetic Algorithm (GA) with sensitivity analysis.

The performance of a product is generally characterized by more than one response variable. Hence the management often faces the problem of simultaneous optimization of many response variables. This study was undertaken to simultaneously optimize the surface hardness and case depth of carbonitrided bushes. Even though lots of literature has been published on various methodologies for tackling the multi-response optimization problem, the simultaneous optimization of heat treated properties of carbonitrided bushes are not reported yet. In this research the effect of four factors and two interactions on surface hardness and case depth of carbontirded bushes were studied using design of experiments. Based on the experimental results, the expected values of the heat treated properties of the bushes were estimated for all possible combination of factors. Then the best combination which, simultaneously optimized the response variables, was arrived at using desirability function. The study showed that the optimum combination obtained through desirability function approach not only minimized the variation around the targets of surface hardness and case depth but also was superior to the ones obtained by optimizing the response variables separately. Moreover this study provides a useful and effective approach to design the production process to manufacture bushes with customer specified surface hardness and case depth targets.

The design of supply chain networks has attracted more attention in recent years according to business and environmental factors.in this paper a multi objective supply chain network design model aims to minimize network costs while satisfying the desired service level, is presented. A fuzzy goal programming (FGP) solution approach based on fuzzy membership function concept is developed to minimize costs and amount of investment while obtain maximum service level. Numerical experiments are conducted to test the efficiency of proposed solution method.

Cutting force and chip reduction coefficient is the important index of machinability as it determines the power consumption and amount of energy invested in machining actions. It is primarily influenced by process parameters like cutting speed, feed and depth of cut. This paper presents the application of Taguchi’s parameter design to optimize the parameters for individual responses. For multi-response optimization, Taguchi’s quality loss function approach is proposed. In the present investigation, optimal values of cutting speed, feed and depth of cut are determined to minimize cutting force and chip reduction coefficient during orthogonal turning. The effectiveness of the proposed methodology is illustrated through an experimental investigation in turning mild steel workpiece using high speed steel tool.

The use of saturated two-level designs is very popular, especially in industrial applications where the cost of experiments is too high. Standard classical approaches are not appropriate to analyze data from saturated designs, since we could only get the estimates of the main factor effects and we would not have degrees of freedom to estimate the variance of the error. In this paper, we propose the use of empirical Bayesian procedures to get inferences for data obtained from saturated designs. The proposed methodology is illustrated assuming a simulated data set.

Flow-shop problems, as a typical manufacturing challenge, have become an interesting area of research. The primary concern is that the solution space is huge and, therefore, the set of feasible solutions cannot be enumerated one by one. In this paper, we present an efficient solution strategy based on a genetic algorithm (GA) to minimize the makespan, total waiting time and total tardiness in a flow shop consisting of n jobs and m machines. The primary objective is to minimize the job waiting time before performing the related operations. This is a major concern for some industries such as food and chemical for planning and production scheduling. In these industries, there is a probability of the decay and deterioration of the products prior to accomplishment of operations in workstation, due to the increase in the waiting time. We develop a model for a flowshop scheduling problem, which uses the planner-specified weights for handling a multi-objective optimization problem. These weights represent the priority of planning objectives given by managers. The results of the proposed GA and classic GA are analyzed by the analysis of variance (ANOVA) method and the results are discussed.

Selection of industrial robots for the present day’s manufacturing organizations is one of the most difficult assignments due to the presence of a wide range of feasible alternatives. Robot manufacturers are providing advanced features in their products to sustain in the globally competitive environment. For this reason, selection the most suitable robot for a given industrial application now becomes a more complicated task. In this paper, four models of data envelopment analysis (DEA), i.e. Charnes, Cooper and Rhodes (CCR), Banker, Charnes and Cooper (BCC), additive, and cone-ratio models are applied to identify the feasible robots having the optimal performance measures, simultaneously satisfying the organizational objectives with respect to cost and process optimization. Furthermore, the weighted overall efficiency ranking method of multi-attribute decision-making theory is also employed for arriving at the best robot selection decision from the short-listed competent alternatives. In order to demonstrate the relevancy and distinctiveness of the adopted DEA-based approach, two real time industrial robot selection problems are solved.