Architectural View of Flexible Aliphatic –OH Group Coordinated Hemi-Directed Pb(II)-Salen Coordination Polymer: Synthesis,Crystal Structure,Spectroscopic Insights,Supramolecular Topographies,and DFT Perspective

Journal of Inorganic and Organometallic Polymers and Materials - We serendipitously designed one novel 1D hetero-binuclear Zn(II)-Pb(II) coordination polymer, namely...     

A production-recycling-inventory model with learning effect

This paper develops an integrated production-recycling system over a finite time horizon. Here, the dynamic demand is satisfied by production and recycling. The used units are bought back and then either recycled or disposed of which are not repairable. The used units are collected continuously from the customers. Recycling products can be used as new products which are sold again. The rate of production and disposal are assumed to be function of time. The setup cost is reduced over time due to “Learning curve” effect. The optimum results are presented both in tabular form and graphically.     

Numerical and experimental study on the three-dimensional extrusion of square section from square billet through a polynomial shaped curved die

The geometry of die profile plays a major role in reducing the extrusion pressure and ensuring the smooth flow of material. In general, the extrusion process is mostly affected by billet geometry, die geometry, and interface frictional force at the die billet geometry. In the present investigation, an analysis using three-dimensional upper bound method using fifth-order die profile function has been carried out for extrusion of square sections from square billet. The extrusion pressure and optimum die length have been computed by multivariable optimization technique. The present die shape profile is found to be superior to many other profiles. The results obtained will help in design of optimum die profile and investigation of its performance.     

A Comprehensive GRNN Model for the Prediction of Cutting Force,Surface Roughness and Tool Wear During Turning of CP-Ti Grade 2

Silicon - Titanium alloys are most widely used in aerospace, military, marine, medical and bio-medical industries because of their unique properties such as high corrosion resistance,...     

Simulation of abrasive flow machining process for 2D and 3D mixture models

Improvement of surface finish and material removal has been quite a challenge in a finishing operation such as abrasive flow machining (AFM). Factors that affect the surface finish and material removal are media viscosity, extrusion pressure, piston velocity, and particle size in abrasive flow machining process. Performing experiments for all the parameters and accurately obtaining an optimized parameter in a short time are difficult to accomplish because the operation requires a precise finish. Computational fluid dynamics (CFD) simulation was employed to accurately determine optimum parameters. In the current work, a 2D model was designed, and the flow analysis, force calculation, and material removal prediction were performed and compared with the available experimental data. Another 3D model for a swaging die finishing using AFM was simulated at different viscosities of the media to study the effects on the controlling parameters. A CFD simulation was performed by using commercially available ANSYS FLUENT. Two phases were considered for the flow analysis, and multiphase mixture model was taken into account. The fluid was considered to be a Newtonian fluid and the flow laminar with no wall slip.     

Effect of Electrode Materials on Different EDM Aspects of Titanium Alloy

Silicon - Electro discharge machining (EDM) is extensively used in modern manufacturing industry because of its advantage over conventional machining method. Titanium alloys are the materials of...     

ANN modelling and Elitist teaching learning approach for multi-objective optimization of $$\upmu $$-EDM

Fabrication of micro-holes has been carried out in Inconel 718 using micro electrical discharge machining operation. Artificial neural network modelling has been carried out to predict Material Removal Rate, Overcut effect and Recast Layer thickness. The training, testing and validation data sets were collected by conducting experiments. It is observed that ANN is a powerful prediction tool. It provides agreeable results when experimental and predicted data are compared. Further optimization of the process variables has been carried out using different meta heuristic approaches like Elitist Teaching learning based optimization, Multi-Objective Differential Evolution and Multi-Objective Optimization using an Artificial Bee Colony algorithm. The comparisons are carried out to improve the accuracy of the model on the basis of Pareto front solutions.     

Investigation of FEM Simulation of Machining of Titanium Alloy Using Microgroove Cutting Insert

In this paper, FEM simulation of machining of titanium alloy using microgroove cutting insert has been carried out using DEFORM-3D software. The cutting insert with microgroove has been made with Solidworks software. The 3D machining simulations of all the different types of microgroove patterns in cutting inserts were done at constant cutting variable, i.e. cutting speed of 125.6 m/min, feed of 0.23 mm/rev and depth of cut of 2 mm respectively. The simulation results were partially validated with experiments. There was good agreement between experiment and simulation. The cutting temperature, effective stress, tool wear rate and temperature at the chip/tool interface has been determined. There was an improvement in the machinability criteria using cutting tool with microgroove pattern.     

A production–inventory model with remanufacturing for defective and usable items in fuzzy-environment

This paper investigates a production–remanufacturing system for a single product over a known-finite time horizon. Here the production system produces some defective units which are continuously transferred to the remanufacturing unit and the constant demand is satisfied by the perfect items from production and remanufactured units. Remanufacturing unit uses the defective items from production unit and the collected used-products from the customers and later items are remanufactured for reuse as fresh items. Some of the used items in the remanufacturing unit are disposed off which are not repairable. The remanufactured units are treated as perfect items. Normally, rate of defectiveness varies in a production system and may be approximated by a constant or fuzzy parameter. Hence, two models are formulated separately with constant and fuzzy defective productions. When defective rate is imprecise, optimistic and pessimistic equivalent of fuzzy objective function is obtained by using credibility measure of fuzzy event by taking fuzzy expectation. Here, it is assumed that remanufacturing system starts from the second production cycle and after that both production and remanufacturing units continue simultaneously. The models are formulated for maximum total profit out of the whole system. Here the decision variables are the total number of cycles in the time horizon, the duration for which the defective items are collected and the cycle length after the first cycle. Genetic Algorithm is developed with Roulette wheel selection, Arithmetic crossover, Random mutation and applied to evaluate the maximum total profit and the corresponding optimum decision variables. The models are illustrated with some numerical data. Results of some particular cases are also presented.