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Nimonic C-263 is predominantly used in the manufacturing of heat susceptible intricate components in the gas turbine, aircraft, and automotive industries. Owing to its high strength, poor thermal conductivity, the superalloy is difficult to machine and causes rapid tool wear during conventional machining mode. Moreover, the unpleasant machining noise produced during machining severely disrupts the tool engineer’s concentration, thereby denying a precise and environment friendly machining operation. Hence, close dimensional accuracy, superior machined surface quality along with production economy, and pleasant work environment for the tool engineers is the need of an hour of the current manufacturing industry. To counter such issues, the present work attempts to compare and explore the machinability of two of the most popular machining strategies like minimum quantity lubrication (MQL) and cryogenic machining process during turning of Nimonic C-263 work piece in order to achieve an ideal machining environment. The machining characteristics are compared in terms of surface roughness (SR), power consumption (P), machining noise (S), nose wear (NW), and cutting forces (CF) to evaluate the impact of machining variables like cutting speed (Vc), feed (f), and depth of cut (ap) with a detailed parametric study and technical justification. Yet again, an investigation is conducted to compare both the machining strategies in terms of qualitative responses like chip morphology, total machining cost, and carbon emissions. The study revealed that cryogenic machining strategy is adequately proficient over MQL machining to deliver energy proficient and gratifying work environment for the tool engineers by reducing the cost of machining and improving their work efficiency.

Nimonic C263, a nickel-based superalloy, finds extensive application in manufacturing of complex parts in gas turbine, aircraft and automotive industries exclusively for exhaust sections. Machining of the alloy through traditional approach is a difficult task owing to its poor thermal conductivity, work hardening and high strength properties. However, the superalloy can be machined up to desired accuracy by non-traditional machining processes like electrical discharge machining (EDM). Yet again, machining of these difficult-to-machine materials not only consumes a significant amount of energy, but also produces unpleasant noise, hindering the sustainable aspect of the process. To meet the requisite of sustainable EDM and explore the machinability, an experimental investigation is proposed in this work, when Nimonic C263 superalloy is machined with three different electrodes, viz. copper, tungsten, and copper–tungsten. Outcomes of important process parameters, viz. voltage (U), discharge current (I), pulse-on-time (Ton), duty factor (τ), and electrode material, are studied on the responses, viz. specific energy consumption, machining noise (N), material removal rate, electrode wear rate, surface roughness (Ra) and radial overcut (C). Analysis of variance is conducted to analyse the influence of each parameter on the responses. A scanning electron microscope investigation is carried out to analyse the pre-machining and post-machining scenarios on the machined surface. The multiple responses are converted into single response by calculating the net outranking flow by application of preference ranking organization method for enrichment evaluation (PROMETHEE) approach. The PROMITHEE-based results are further improved by using a hybrid cuckoo search algorithm (combined PROMITHEE-based cuckoo search). A confirmative test is further conducted on the optimum machining conditions obtained by the hybrid approach (combination of PROMITHEE and cuckoo search) indicating an overall improvement of 6.02% for the responses, validating the proposed work.

The machining feasibility of high chromium tool steel using the EDM process has been studied. An attempt has been made to optimise the EDM process, concerning eco-friendly aspects like relative electrode wear ratio, process energy consumption along with machining parameter, that is process time. The four control factors: Peak current, Pulse duration, Flushing pressure and Dielectric level were selected to minimize the output responses. The Taguchi approach was used to optimise the process parameters. It was observed that the output responses were greatly influenced by the peak current and the dielectric level, but least affected by pulse duration.

The present work proposes a combined experimental methodology and optimization strategy of the process parameters for the turning of Nimonic C-263 superalloy with a multilayer CVD coating using TiN-MT-TiCN-Al2O3 grade KCM25 inserts. Taguchi’s L-27 orthogonal array is used for the experimental design. The outcomes of machining parameters viz. cutting speed (vc), cutting feed (f), depth of cut (ap), cryogenic temperature (T) and holding time (t) are explored on the machining characteristics like surface roughness (SR), nose wear (NW), flank wear (NW) and cutting force (F). The results are further compared with the cryogenic environmental studies that have improved the tool life and led to better machinability of the alloy. The multiple responses of the cryogenic machining environment are converted into the equivalent closeness factor (CF) using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The TOPSIS based results are further improved using a neuro-genetic algorithm (combined ANN-GA). A confirmatory test is further carried out on the optimum machining parameters obtained by the hybrid approach (the combination of TOPSIS and ANN-GA) to validate the proposed work. It is seen that the closeness factor is improved by 6.17 %. The proposed model can be useful to select the ideal process conditions and to enhance the productivity of turning processes.

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Nickel based superalloys finds extensive usage in manufacturing of intricate part shapes in gas turbine, aircraft, submarine, and chemical industries owing their excellent mechanical property and heat resistant abilities. However, machining of such difficult-to-machine alloys up to the desired accuracy and preciseness is a complex task owing to a rapid tool wear and failure. In view of this, present work proposes an experimental investigation and optimization of process parameters of the cryogenic assisted turning process during machining of Nimonic C-263 superalloy with a multilayer CVD insert. Taguchi’s L-27 orthogonal array is used plan the experiments. Effects of input parameters viz. cutting speed (V), cutting feed (F), depth of cut (D) are studied on responses viz. surface roughness (SR), nose wear (NW) and cutting forces (CF) under indirect, direct and hybrid cryogenic (indirect+direct) machining environment. A scanning electron microscope (SEM) analysis is carried out to explore the post- machining outcomes on the performance measures. The multiple responses are converted in to single response and ranked according to Taguchi based grey relational analysis (TGRA). Feed rate (F) is found to be the most influential parameter from the analysis of variance (ANOVA) of grey relational grade (GRG). Further, the means of GRG for each level of process parameters are used to improve the optimal process parameters. Finally, a confirmative experiment is performed on the optimum machining conditions to validate the optimization study results. It is seen that the value of GRG is improved by 9.34% when compared to the previous best result. The proposed work can be beneficial to choose ideal process conditions to enhance the performance of turning operation.

Sand casting is a widely used process for making various products used in numerous industries such as medical, electrical, automotive etc. However, the poor mechanical properties and microstructural defects are the challenging concern to the foundry engineers for a last decade. In the present work, the microstructural and mechanical assets of Al 1230 alloy are investigated by slow and rapid cooling in a sand casting process. The effect of cooling strategy is analyzed using a scanning electron microscope (SEM) and ultimate tensile strength. Eventually, it is observed that the rapid cooling is a more suitable technique, which enhances the microstructural and mechanical properties of the cast component by inducing of silicon particles during the process. Hence, the proposed work is very useful to smart industries for achieving desired mechanical property while fabricating sand cast components.

Nickel based alloys, are difficult to machine with conventional machining approach at precision level. However, these alloys can be machined up to good accuracy and preciseness with non-conventional machining process such as electrical discharge machining (EDM) process. In the present study, EDM of Nimonic C263 work piece, a nickel based super alloy, has been carried out with three tool materials viz. copper, copper-tungsten and tungsten. Box-behnken design is used to plan the experiments. Consequences of different electrode material, voltage, duration of spark and discharge current are studied on machining performance of the process viz. material removal rate (MRR) and surface roughness. The influence of parameters on each response is analyzed by means of analysis of variance (ANOVA). It is observed that discharge current, electrode material and voltage have significant effect on the performance measures. Proposed study is useful for tool engineers working on aerospace and aviation industries manufacturing various aircraft components up to desired accuracy.

This article proposes an extensive experimental and microstructural analysis to study the consequences of deep cryogenic treatment (DCT) soaking duration on tungsten carbide end mill cutter on the machinability of P20 mold steel. A Box-Behnken design of response surface methodology (RSM) is utilized to collect data for the study. Cutting speed, feed rate, depth of cut and milling cutters subjected to various soaking durations are taken as important process variables which are a function of performance measures viz. tool wear rate (TWR), material removal rate (MRR) and surface roughness (Ra). It is observed that cutting speed, feed, depth of cut and cryogenic treatment exhibit a considerable effect on performance measures. In the present study, NSGA-II multi-objective optimization technique is used to obtain the optimal process parameters of end milling process to enhance the productivity of the process. For this purpose, the developed RSM model is coupled with NSGA-II in MATLAB.

Globe valves are primarily manufactured by a casting process are extensively used for controlling the fluid flow in pipelines in various mechanical devices, viz. boilers, condensers, overhead tanks, etc. However, the component faces a lot rejection due to various casting defects occurring during the manufacturing process majorly because of the improper gating system design. In view of this, the present work proposes a numerical simulation on globe valves prepared by the sand casting process by varying the number of gates in gating system design. The effectiveness of both the designs is evaluated in terms of flow front, air entrapment and porosity percentages obtained from both the designs. From the simulation results, it is observed that an increasing number of gates in gating system design can result in better flow front, lesser air entrapment, promotion of directional solidification and lesser porosity percentage in casted product. The proposed model can be useful for producing sound casted product and defect-free casting thereby reducing the manufacturing costs.

Electrical energy is used to machine metals in electric discharge machining (EDM) process. EDM is a thermoelectric process, in which machining occurs without cutting force. Hence, electrical energy is converted to thermal energy to remove material. Usually, straight polarity is used in EDM, i.e., workpiece is connected to positive terminal (anode) and tool is connected to negative terminal (cathode). But, reverse can be possibly have rarely studied. An in-house designed table-top EDM was built to perform the experiments in straight and reverse polarity. Copper material was used as tool and workpiece to avoid the effect of different material. The voltage and polarity have significant effect on removal of material from workpiece.

Now-a-days, alloys and super alloys viz. Titanium and Inconel have abundant applications in manufacturing complex parts in aircraft, automotive, and other industrial usages. Machining of these materials becomes extremely difficult with conventional machining process at precision level. These materials can be machined with ease; with desired accuracy with non-conventional machining process like electrical discharge machining (EDM) process. However, the high erosion of EDM tool during machining has emerged as the major obstacle for tool engineers to achieve productivity of the process. In view of this, the present work proposes an experimental investigation on cryogenically treated brass and copper electrodes which were used for machining of Ti-6Al-4V alloy workpiece. The effect of cryogenic treatment of tools on different performance measures like material removal rate (MRR), tool wear rate (TWR) and radial overcut is investigated. Experiments were conducted by full factorial design to find the outcome of important process parameters viz. current (I), pulse on-time (Ton), voltage(V) and pulse-off time (Toff) on responses viz. MRR, TWR and radial overcut. It has been observed that owing cryogenic treatment tool wear and radial overcut reduces considerably where as MRR remains unaffected.

Nickel based super alloys have been gaining importance day by day and it is being implemented extensively in various sectors like nuclear, aerospace, chemical and defense industries. But these alloys have an increased chemical affinity and low thermal conductivity which makes it difficult to turn it in when it is dry. Hence, Nimonic family of Nickel based super alloy is gradually getting importance in terms of research. Hard turning needs coolants in order to make a hole in the alloy, but since these coolants damage the environment, alternate methods are necessary to perform the turning process without these fluids. Hence, cryogenic process is done to coat the alloys with different components at very low temperatures. Performing the cryogenic process makes the alloy harder and improves the overall structural integrity of the alloy. This paper studies various cryogenic processes done in Nickel based alloys and then focuses on the Nimonic based Nickel alloys and compares the performance and properties of coated alloys with non-coated alloys.

High erosion of EDM electrode is a major concern for tool engineers working in electrical discharge machining (EDM) process. The machining cost and time are significantly affected by high erosion of tool and thus declines productivity of the process. In view of this, present work proposes a thermo-numerical model based on finite element method (FEM) for accurate prediction of two important performance measures for cryogenically treated EDM process viz. MRR and TWR. Open circuit voltage, pulse-on-time, duty factor, flushing pressure and brass electrodes subjected to different cryogenic soaking duration viz. 0 hrs, 24-hrs, 36-hrs are considered as important process variables for responses MRR and TWR while machining with Inconel 718 super alloy. A Box-bhenken design of response surface mythology (RSM) is adopted to reduce the number of experiments and extract maximum analysis from the study. It is observed that for the electrode treated with longer soaking duration (i.e. 36-hrs) wearing resistance is significantly improved due to enhancement of thermal properties owing to cryogenic treatments. Finally, a thermo- numerical model has been proposed to validate results obtained from the experimental results. The proposed model can be used to as a substitute to experimental methods to enhance the productivity for the cryogenically treated EDM process.

In today’s world, one of the important tasks is to handle the energy available and to minimize the heat losses. In automotive sector, engine is the primary energy source and there are many losses associated with an engine out of which engine cooling and exhaust losses takes major share. In four-stroke SI/CI engines, four strokes of reciprocating cylinder involve two rotations of the crankshaft. These strokes are suction, compression, power and exhaust respectively. A large amount of fuel energy is wasted when fuel is not compressed completely and also at the exhaust of internal combustion engines. This paper focusses on minimizing losses of internal combustion engine by secondary expansion of exhaust gases through 180 ° of crank revolution. For this purpose, four stroke four revolution mechanism is proposed and this is achieved by modifying current slider crank mechanism with double connecting rod arrangement. This modified engine shows an increase in the air standard cycle efficiency by 4.10% where as fuel air cycle efficiency rise is observed to be 5.25%. There is also significant rise observed in the modified engine over conventional engine in terms of gross indicated thermal efficiency and brake thermal efficiency as well.

In order to machine hard and high-strength-to-weight ratio materials, electrical discharge machining (EDM) process is extensively used in aerospace, automobile and other industrial applications. However, high erosion of tool and improper selection of machining variables have emerged as a major obstruction to achieve productivity in this direction. High erosion of tool not only enhances the cost of machining but also increases the machining time by causing interruption during machining. Therefore, proper selection of machining variables and tool material life are the two vital aspects for the tool engineers working in EDM. In view of this, the present work proposes an extensive experimental investigation and optimization of machining variables of cryogenically treated brass tool materials on machining competences of Inconel 718 workpiece. The study primarily highlights the outcome of cryogenically treated soaking duration of tools along with other important process variables, viz. discharge current, open-circuit voltage, pulse-on time, duty factor and flushing pressure, on the performance measures such as electrode wear ratio (EWR), surface roughness and radial over-cut. The study revealed that soaking duration in deep cryo-treatment of the electrode is a significant variable to achieve improved machining characteristics. The performance measures are converted into equivalent single performance measure by calculating the relative closeness coefficient by the techniques for order preferences by similarity to ideal solution (TOPSIS) approach. Finally, a novel teaching–learning-based optimization (TLBO) algorithm has been proposed to find the optimal level of machining variables for the performance measures. The optimal levels of cutting variables obtained through the algorithm are validated through confirmation test, predicting an error of 2.171 percentages between the computational and experimental results. The predicted result suggests that the proposed model can be used to select the ideal process states to achieve productivity for the cryo-treated EDM.

AA6061 aluminum alloy is widely used for fabrication of light weight structures requiring a high strength to weight ratio in aerospace, naval, rail and automotive industries. As compared to the traditional fusion welding processes which are commonly used for joining structural aluminum alloys, friction stir welding (FSW) process is an emerging solid state joining process in which the material that is being welded does not melt and recast. In view of this, the present work proposes an experimental investigation, to analyze the effect of welding parameters such as welding speed, tool rotational speed and tool profile on welding strength. The experimental architecture is planned as per Taguchi's L9 orthogonal array so as to extract maximum information with minimum experimental runs. The study revealed that welding speed is an important parameter for the variation of tensile strength.

This paper proposes an experimental investigation and optimization of the various machining parameters for the electrical discharge machining (EDM) processes on Inconel 718 super alloy using a multi objective particle swarm optimization (MOPSO) algorithm. A Box-Behnkin design of response surface methodology has been used to collect data for the study. The machining performances of the process are evaluated in terms of material removal rate (MRR) and surface quality which are functions of process variables such as open circuit voltage, discharge current, pulse-on-time, duty factor, flushing pressure and tool material. Mathematical model is developed relating responses with process variables. Finally, a MOPSO algorithm has been proposed for the multi objective optimization of the responses.

Present research proposes an optimization methodology for the selection of best process parameters in multi-response situation. Experiments have been conducted on a die-sinking electric discharge machine under different conditions of process parameters. A response surface methodology (RSM) is adopted to establish effect of various process parameters such as discharge current (Ip), pulse on time (Ton), duty factor (t) and flushing pressure (Fp) on four important responses like material removal rate (MRR), tool wear rate (TWR), surface roughness (Ra) and circularity (r1/r2) of machined component. Since the natures of responses are contradicting in nature, it is difficult to find a single combination of machining parameters that provides the best performance satisfying all responses simultaneously. In order to achieve best machining condition, an equivalent single response capable of representing all individual responses is needed. The work includes data envelopment analysis (DEA) to obtain relative efficiency for each experimental run treating as decision making unit (DMU). Each DMU is evaluated using LINGO software to obtain relative efficiency. The relative efficiency is ranked in ascending order and average ranked value (ARV) is calculated to find the optimal solution. Finally, the optimal setting capable of improving all the responses simultaneously is found to be Ip=7 amp, Ton= 200 |a.s, t = 90%, and Fp = 0.4 kg/cm 2 . With this best combination of factorial level, the experimental values of responses are obtained as MRR=13.9600 mnrVmin, TWR=0.0201 mnrVmin, Ra=4.9300 umandcircularity=0.8401.

Electrochemical machining (ECM) is one of the non-conventional machining processes which is mostly used to machine difficult-to-machine materials such as super alloys, Ti-alloys, stainless steel, alloy steel etc. The major requirement of the process is that work piece should be electrically conducting in nature. A large number of parameters influence material removal rate (MRR) and surface roughness (SR) of parts produced by ECM. Usually, tool makers use thumb rules and machine manuals to set optimal parameters for the process. In this work, response surface methodology is adopted to study the effect of four important parameters such as current, voltage, flow rate of electrolyte and inter-electrode gap on MRR and SR. Statistically validated regression equation are developed relating response like MRR and SR with input parameters. Finally, a non-dominated sorted genetic algorithm is used to find out the optimal process parameters that simultaneously maximize MRR and minimize SR. The set of Pareto solutions provide flexibility to the tool makers to choose the best setting depending on applications.

The study presents experimental investigation to assess influence of cryogenic treatment of electrodes on machinability in powder-mixed electrical discharge machining (PMEDM). Response surface methodology is used to extract maximum process related information with less number of experimental runs. The machining characteristics of the process are evaluated in terms of material removal rate (MRR), electrode wear ratio (EWR) and surface roughness which are function of machining parameters viz. open circuit voltage, discharge current, pulse-on-time, duty factor, concentration of fine graphite powder and cryogenically treated work tool pair. The study reveals that discharge current, pulse-on-time, concentration of powder and work-tool pair exhibit significant effect on all the performance measures. It is also observed that treatment to both the electrodes results in more beneficial in comparison to treatment of either the tool or work piece. Finally, a novel multi-objective particle swarm optimisation algorithm (MOPSO) has been proposed in order to achieve optimal Pareto front.

In this work, a thermal model based on finite element method has been proposed to predict the material removal and tool wear in electrical discharge machining process by simulating a single spark discharge. The model is validated by conducting experiments on a die-sinking EDM machine. It is found that numerical model provides reasonably accurate estimation of responses. Parametric analysis is conducted on the proposed model to study the influence of significant process parameters on the responses. Regression analysis is carried out to develop empirical models relating responses with process parameters. Finally, a non-dominated sorting genetic algorithm has been employed to obtain Pareto optimal solution for multi-objective optimisation. The numerical method provides an inexpensive and time saving alternative to study the performance of machining before actual cutting operation.

In the present work, a thermal-structural model is presented to analyze the process parameters and their effect on three important responses such as material removal rate, tool wear rate and residual stresses on work piece in electrical discharge machining (EDM) process. The numerical model is validated conducting experiments on a die-sinking EDM machine. The numerical method provides an inexpensive and time saving alternative to study the performance of machining before actual cutting operation. A Box-Behnkin design of response surface methodology is adopted to collect data for analysis. Regression analysis is conducted to develop equations relating responses with process parameters. Finally, non-dominated sorting genetic algorithm is used to obtain pareto optimal solution for multi objective optimization.