Optimization of A-TIG process parameters using response surface methodology

In the present work, the influence of process parameters such as welding current (I), welding speed (S), and flux coating density (F) on different aspects of weld bead geometry for example depth of penetration (DOP), bead width (BW), depth to width ratio (D/W), and weld fusion zone area (WA) were investigated by using the central composite design (CCD). 9–12% Cr ferritic stainless steel (FSS) plates were welded using A-TIG welding. It was observed that all input variables have a direct influence on the DOP, BW, and D/W. However, flux coating density has no significant effect on WA. Mathematical models were generated from the obtained responses to predict the weld bead geometry. An optimized DOP, BW, D/W, and WA of 6.95?mm, 8.76?mm, 0.80, and 41.99?mm², respectively, were predicted at the welding current of 213.78 A, the welding speed of 96.22?mm/min, and the flux coating density of 1.99?mg/cm². Conformity test was done to check the practicability of the developed models. The conformity test results were in good agreement with the predicted values. Arc constriction and reversal in Marangoni convection were considered as major mechanisms for the deep and narrow weld bead during A-TIG welding.     

Cutting force and hole quality analysis in micro-drilling of CFRP

Micro-drilling in carbon fiber reinforced plastic (CFRP) composite material is challenging because this material machining is difficult due to anisotropic, abrasive and non-homogeneous properties and also downscaling of cutting process parameters affect the cutting forces and micro-drilled hole quality extensively. In this work, experimental results based statistical analysis is applied to investigate feed and cutting speed effect on cutting force components and hole quality. Analysis of variance based regression equation is used to predict cutting forces and hole quality and their trend are described by response surface methodology. Results show that roundness error and delamination factor have similar trends to those of radial forces and thrust force, respectively. Non-linear trends of cutting forces and hole quality errors are observed during downscaling of the micro-drill feed value. Optimization results show that cutting forces and hole quality errors are minimum at a feed value which is almost equal to the tool edge radius rather than at the lowest feed value. Therefore, the presented results clearly show the influences of size effects on cutting forces and hole quality parameters in micro-drilling of CFRP composite material.     

Influence of pH on particle size and sensing response of chemically synthesized chromium oxide nanoparticles to alcohols

Nanoparticles of chromium oxide have been synthesized by following a co-precipitation route at various pH values of the precursor solution. Structural and morphological analysis were carried out by using XRD and TEM techniques which revealed that the size of nanoparticles synthesized at pH 9 was smaller as compared to those synthesized at other pH values. The thick films of synthesized samples were deposited on alumina substrate and their sensing response to methanol, ethanol and isopropanol was investigated at different operating temperatures. It was observed that all the sensors gave optimum response at 250 °C. It has been observed that sample prepared at pH 9, being a collection of smallest particles as compared to other samples, exhibited high sensing response to alcohol vapour. Sensor response of all the samples tested was significantly higher towards isopropanol vapour than towards methanol or ethanol.In the present study the effect of particle size on intergranular activation energy has been studied as well. It was found that smaller particles possess high activation energy and exhibit higher sensing response as compared to that of larger particles. This type of study may help in the selection of particle suitable for gas sensing.     

An imperfect production process for time varying demand with inflation and time value of money – An EMQ model

The paper deals with an economic manufacturing quantity (EMQ) model for time-dependent (quadratic) demand pattern. Every manufacturing sector wants to produce perfect quality items. But in long run process, there may arise different types of difficulties like labor problem, machinery capabilities problems, etc., due to that the machinery systems shift from in-control state to out-of-control state as a result the manufacturing systems produce imperfect quality items. The imperfect items are reworked at a cost to become the perfect one. The rework cost may be reduced by improvements in product reliability i.e., the production process depend on time and also the reliability parameter. We want to determine the optimal product reliability and production rate that achieves the biggest total integrated profit for an imperfect manufacturing process using Euler–Lagrange theory to build up the necessary and sufficient conditions for optimality of the dynamic variables. Finally, a numerical example is discussed to test the model which is illustrated graphically also.     

First-principles study of ground-state properties and phase stability of vanadium nitrides

Using a series of density functional electronic structure total energy calculations, we have systematically studied the ground-state properties and phase stability of vanadium nitrides. Comparison of enthalpy of formation shows that V ₂N is equally stable (polymorphic) in , and Fe₂C phases within a few meV. Formation enthalpy of the various phases considered for perfect stoichiometric V N_1.0 shows that it has enhanced stability in hexagonal WC and NiAs structures in relation to NaCl-type δ-phase. The TiAs phase of VN has nearly same energy as NaCl structure. Comparison of energetics of -type , for x=0 and 0.3333 and of , for x=0, 0.0625, 0.125 and 0.25 shows that vacancies on the nitrogen sublattice lowers the formation enthalpy in relation to respective stoichiometric phases which is in agreement with experiments, as bulk vanadium nitrides are known to be generally non-stoichiometric. The calculated dilute heat of solution for the interstitial nitrogen is found to be in good agreement with experimental values and shows that nitrogen prefers to occupy the octahedral sites in bcc vanadium. The α^′-FeN and martensite structures, considered for the metastable phases of vanadium nitrides, have higher formation enthalpy in relation to equilibrium phases. Analysis of electronic density of states of V ₂N shows that the low energy , and Fe₂C phases are characterized by broad V 3d-N 2p and V 3d bonding bands. Density of states of VN shows that in the low energy WC and NiAs phases some of the antibonding states are made empty, leading to a minimum near the Fermi level. For and , density of states shows that vacancies on the nitrogen sublattice introduce additional filled states in the 3d band below Fermi level enabling enhanced bonding. Comparison between bulk moduli and atomic volumes for the various phases of vanadium nitrides shows that higher bulk moduli are dominated by increased V–N bonds combined with low atomic volumes.     

Implementation of a Gaussian process-based machine learning grasp predictor

With the goal of advancing the state of automatic robotic grasping, we present a novel approach that combines machine learning techniques and physical validation on a robotic platform to develop a comprehensive grasp predictor. After collecting a large grasp sample set (522 grasps), we first conduct a statistical analysis of the predictive ability of grasp quality metrics that are commonly used in the robotics literature. We then apply principal component analysis and Gaussian process (GP) algorithms on the grasp metrics that are discriminative to build a classifier, validate its performance, and compare the results to existing grasp planners. The key findings are as follows: (i) several of the existing grasp metrics are weak predictors of grasp quality when implemented on a robotic platform; (ii) the GP-based classifier significantly improves grasp prediction by combining multiple grasp metrics to increase true positive classification at low false positive rates; (iii) The GP classifier can be used generate new grasps to improve bad grasp samples by performing a local search to find neighboring grasps which have improved contact points and higher success rate.     

Two-echelon fuzzy stochastic supply chain for the manufacturer–buyer integrated production–inventory system

This paper deals with two-echelon integrated procurement production model for the manufacturer and the buyer integrated inventory system. The manufacturer procures raw material from outside suppliers (not a part of supply chain) then proceed to convert it as finished product, and finally delivers to the buyer, who faces imprecise and uncertain, called fuzzy random demand of customers. The manufacturer and the buyer work under joint channel, in which a centralized decision maker makes all decisions to optimize the joint total relevant cost (JTRC) of entire supply chain. In this account, in one production cycle of the manufacturer we determine an optimal multi-ordering policy for the buyer. To be part of this, we first derive the JTRC in stochastic framework, and then extend it in fuzzy stochastic environment. In order to scalarize the fuzzy stochastic JTRC, we use an evaluation method wherein randomness is estimated by probabilistic expectation and fuzziness is estimated by possibilistic mean based on possibility evaluation measure. To derive the optimal policies for both parties, an algorithm is proposed. A numerical illustration addresses the situations of paddy procurement, conversion to rice and fulfillment of uncertain demand of rice. Furthermore, sensitivity of parameters is examined to illustrate the model and algorithm.     

A particle swarm approach for multi-objective optimization of electrical discharge machining process

This paper proposes an experimental investigation and optimization of various machining parameters for the die-sinking electrical discharge machining (EDM) process using a multi-objective particle swarm (MOPSO) algorithm. A Box–Behnken design of response surface methodology has been adopted to estimate the effect of machining parameters on the responses. The responses used in the analysis are material removal rate, electrode wear ratio, surface roughness and radial overcut. The machining parameters considered in the study are open circuit voltage, discharge current, pulse-on-time, duty factor, flushing pressure and tool material. Fifty four experimental runs are conducted using Inconel 718 super alloy as work piece material and the influence of parameters on each response is analysed. It is observed that tool material, discharge current and pulse-on-time have significant effect on machinability characteristics of Inconel 718. Finally, a novel MOPSO algorithm has been proposed for simultaneous optimization of multiple responses. Mutation operator, predominantly used in genetic algorithm, has been introduced in the MOPSO algorithm to avoid premature convergence. The Pareto-optimal solutions obtained through MOPSO have been ranked by the composite scores obtained through maximum deviation theory to avoid subjectiveness and impreciseness in the decision making. The analysis offers useful information for controlling the machining parameters to improve the accuracy of the EDMed components.