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This paper presents investigation of surface veracity of Wire EDM machined surfaces of Al / ZrO 2 (p) - MMC obtained utilizing the optimal parametric combinations. The effect of process parameters on the desired performance characteristics, i.e., minimum spark gap (SG), maximum material removal rate (MRR) and minimum surface roughness [SR( R a & R t )], is also presented. The second-order numerical expressions developed by response surface methodology (RSM) are used as a function in.M file. Genetic Algorithm and Direct Search Toolbox of MATLAB ® are utilized to identify the multi-objective optimum set of the process parameters for the desired performance characteristics. In the present paper, surface veracity aspects that include surface defects and thickness of recast layer are investigated for the machined surfaces obtained after Wire EDM to achieve the desired optimum performance characteristics. From the investigation, it can safely be concluded that for the Wire EDM of Al / ZrO 2 (p) - MMC s, lower parameter setting values of pulse width and short pulse time along with higher parameter setting value of time between pulses are desirable from the view point of good surface veracity of the machined surfaces as well as to obtain low average recast layer thickness ( Rl tavg ). The Rl tavg decreases as weight fraction of ZrO 2 (p) increases in Al / ZrO 2 (p) - MMC from 5 to 15%. The investigation of surface veracity of the Wire EDM obtained surfaces will provide the significant guidelines to the production engineers to plan the need of subsequent operations to improve the surface veracity and further the life of the products fabricated using these MMCs. [ABSTRACT FROM AUTHOR]

Accurate determination of the bubble point pressure (BPP) is extremely important in several applications in oil industry. In reservoir engineering applications the BPP is an essential input for the reservoir simulation and reservoir management strategies. Also, in production engineering the BPP determines the type of the inflow performance relationship that describes the reservoir production performance. Accurate estimation of the BPP will eliminate the risk of producing in two-phase region. Current correlations can be used to determine the BPP with high errors, and this will lead to poor reservoir management. Artificial intelligent tools used in the previous studies did not disclose the models they developed, and they stated the models as black box. The aim of this research is to develop a new empirical correlation for BPP prediction using artificial intelligent techniques (AI) such as artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM). For the first time we extracted the weights and the biases from AI models and form a new mathematical model for BPP prediction. The results obtained showed that the ANN model was able to estimate the BPP with high accuracy (correlation coefficient of 0.988 and average absolute error percent of 7.5%) based on the specific gravity of gas, the dissolved gas to oil ratio, the oil specific gravity, and the temperature of the reservoir as compared with ANFIS and SVM. The developed mathematical model from the ANN model outperformed the previous AI models and the empirical correlations for BPP prediction. It can be used to predict the BPP with a high accuracy (the average absolute error (3.9%) and the coefficient of determination (R2) of 0.98). [ABSTRACT FROM AUTHOR]

We consider in this paper a reverse-logistics problem chain with three stock categories where the production system is boosted by advertisement campaigns. Newly manufactured items are stored in the first stock category. The second stock category is reserved for remanufactured items, while the third contains items that are returned from the market. We assume that remanufactured items are not necessarily as-good-as-new. We also assume that new and remanufactured items are subject to deterioration and to dynamic demands, that customer return rate is also dynamic, and that the firm adopts a continuous-review policy. We use an optimal control approach to obtain optimal manufacturing rate, remanufacturing, rate, disposal rate and inventory levels in all three stock categories. Numerical examples and sensitivity analyses illustrate the results obtained. [ABSTRACT FROM AUTHOR]