ANN modeling of nickel base super alloys for time dependent deformation

Alloys 617 and 276 are nickel-based super alloys with excellent mechanical properties, oxidation, creepresistance, and phase stability at high temperatures. These alloys are used in complex and stochastic applications. Thus, it is difficult to predict their output characteristics mathematically. Therefore, the non-conventional methods for modeling become more effective. These two alloys have been subjected to time-dependent deformation at high temperatures under sustained loading of different values. The creep results have been used to develop the new models. Artificial neural network (ANN) was applied to predict the creep rate and the anelastic elongation for the two alloys. The neural network contains twenty hidden layer with feed forward back propagation hierarchical. The neural network has been designed with MATLAB Neural Network Toolbox. The results show a high correlation between the predicted and the observed results which indicates the validity of the models

A STUDY ON CAPABILITIES OF DIFFERENT ELECTRODE MATERIALS DURING ELECTRICAL DISCHARGE MACHINING (EDM)

Electrode material inelectro discharge machining EDM process plays an important role in terms of material removal rate (MRR), electrode wear rate (EWR) and surface roughness (Ra). The purpose of this research is to investigate the capability of different electrode materials: copper, aluminum and graphite in EDM of AISI 304 stainless steel as a work piece. The research focuses on three current settings: 2.5A, 4.5A and 6.5A using kerosene as dielectric fluid. The experiment is planned and analyzed using full factorial of the experimental design using response surface methodology (RSM). two outputs have been investigated: MRR and EWR. The results indicated that the responses increased with the increase in current. Finally the desirability function method have been used to determine the optimum values. The resulat show that the maximum MRR and  the minimum EWR were achieved by using graphite electrode at current 6.5A

From green remediation to polymer hybrid fabrication with improved optical band gaps

The present work proposed a novel approach for transferring high-risk heavy metals tometal complexes via green chemistry remediation. The method of remediation of heavy metals developed in the present work is a great challenge for global environmental sciences and engineering because it is a totally environmentally friendly procedure in which black tea extract solution is used. The FTIR study indicates that black tea contains enough functional groups (OH and NH), polyphenols and conjugated double bonds. The synthesis of copper complex was confirmed by the UV-vis, XRD and FTIR spectroscopic studies. The XRD and FTIR analysis reveals the formation of complexation between Cu metal complexes and Poly (Vinyl Alcohol) (PVA) host matrix. The study of optical parameters indicates that PVA-based hybrids exhibit a small optical band gap, which is close to inorganic-based materials. It was noted that the absorption edge shifted to lower photon energy. When Cu metal complexes were added to PVA polymer, the refractive index was significantly tuned. The band gap shifts from 6.2 eV to 1.4 eV for PVA incorporated with 45 mL of Cu metal complexes. The nature of the electronic transition in hybrid materials was examined based on the Taucs model, while a close inspection of the optical dielectric loss was also performed in order to estimate the optical band gap. The obtained band gaps of the present work reveal that polymer hybrids with sufficient film-forming capability could be useful to overcome the drawbacks associated with conjugated polymers. Based on the XRD results and band gap values, the structure-property relationships were discussed in detail. © 2019 by the authors. Licensee MDPI, Basel, Switzerland

Minimizing makespan of multimachine production system in flow shop environment by means of mixed integer programming model

To face the challenges of industrial globalization and sustain in the competitive market, the manufacturers have to gratify the customer demand by launching the products on time having variable design and volume at low price. In this regards, the necessity of adopting the epitome of flexible mass production flow shop structure along with the appropriate production planning tools and techniques like scheduling knows no bound. As a consequence, numerous approaches have already proposed for scheduling the production flow shop. However, before the adoption of any of these conventional approaches it is an utmost need for the manufacturer to realize its consequences and the appropriateness. Therefore, in this endeavour, we anticipated mixed integer linear-programming model for machine scheduling in flow shop environment based on multi-machine and multi-product scenario. Real data from industry has been collected by conducting several site visits at a local production system. The model then was analysed using What’s Best Excel Solver. The result shown by adopting the appropriate sequence, it is possible to achieve the minimum completion time compared to other possible sequence combination of products. By minimizing the makespan, the idle times of some of the machine will be reduced meanwhile the utilization of the machine will be maximized consequently

Tool life modeling in high speed turning of AISI 4340 hardened steel with mixed ceramic tools by using face central cubic design

Tool life estimation for the cutting tool before the machining process is important due to economic and quality consideration. Thus, developing a model that can predict the tool life with high accuracy is an important issue. This paper deals with developing a new model of tool life for mixed ceramic tools in turning hardened steel AISI 4340 based on experimental tests. The experiments were planned and implemented using Central Composites Design (CCD) of Response Surface Methodology (RSM) with three input factors: cutting speed, feed rate and negative rake angle. The Face Central Cubic Design has been used as a special case of CCD. The analysis of variance (ANOVA) has been conducted to analyze the influence of process parameters and their interaction during machining. The first and second order models have been developed. It was found that the second order model provide higher accuracy prediction than the first order model. It was observed that the cutting speed is the most significant factor that influences the tool life for the two models, followed by the feed rate then the negative rake angle. The predicted values are confirmed by using validation experiments. Copyright © 2013 Praise Worthy Prize S.r.l. - All rights reserved

Flank wear modeling in high speed hard end milling using integrated approach of Monte Carlo simulation method and Taguchi design

In high speed cutting of hard materials, the wear rate will be very difficult to predict due to the fast and sever changing in the cutting zone. Therefore, using the traditional methods in predicting the output responses will be not the correct options. One of the effective alternatives is by using artificial intelligent approach. The current work presents the simulation of flank wear rate in high-speed hard end milling of AISI H13 hardened steel using an integrated approach of using: Monte Carlo (MC) simulation method based on Taguchi design. An experimental investigation was carried out using coated carbide tools to run a set of experiments using Taguchi design (L9) with three input factors at three levels in the following design boundary: cutting speeds (352-452 m/min), feed rate (0.01-0.05 m/rev), and depth of cut of (0.2-0.5) mm. Each experiment was repeated twice using three inserts. The results was used to create 1000 run simulated from 135 experimental reading. A new model was developed using JMP software. The results were analyzed statistically and indicate that even with the complexity of the process, the neural network technique was found to be adequate in predicting and simulating the flank wear length