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Experimental Investigations on Machinability Aspects of Nickel-Titanium Based Alloys during Wire Electrical Discharge Machining / Himadri Majumder

By: Majumder, Himadri.
Contributor(s): Maity, Kalipada [Supervisor] | Department of Mechanical Engineering.
Material type: materialTypeLabelBookPublisher: 2019Description: xxv, 176p.Subject(s): Mechanical Engineering | Machine DesignOnline resources: Click here to access online Dissertation note: Thesis Ph.D/M.Tech (R) National Institute of Technology, Rourkela Summary: With the development of the modern manufacturing industry, the demands for hard and difficult-to-cut materials having properties like biocompatibility, corrosion resistance, toughness, light weight, environmental and health friendly nature are increasing day by day. Some of the common difficult-to-cut materials are shape memory alloys (SMA), titanium based alloys, nickel based alloys, etc. are found to be widely used in biomedical, automobile, aerospace and micro-engineering industries. Continuous development and innovation of modern engineering materials as well as the necessity for complex shaped components have made wire electrical discharge machining (WEDM) an imperative choice in production industry. The unique ability of SMA to memorize its previous form has drawn notable attention recently in a wide range of commercial applications. In recent years, development and growth of SMA nitinol applications in the manufacturing industry have also been fairly strong. For the manufacturing sector, machinability study of nitinol has become a significant feature. The choice of the titanium alloy grade 6 was made taking into account its widespread area of applications for airframe, propellant containment for rockets and space vehicles. Further, to face the global competitiveness in manufacturing industry, selection of the best process, machine and equipment, the best parameter setting for preferred outcomes is absolutely important. The contradictive nature of responses and the availability of numerous options make it extremely difficult to select the best prediction and selection procedure. At the beginning, the effect of different independent input parameters like pulse-on time (TON), discharge current (I), wire tension (WT), wire speed (WS) and flushing pressure (FP) on the key machinability aspects namely kerf width (KW), arithmetic mean roughness (Ra), material removal rate (MRR) and micro-hardness (μh) of SMA nitinol during WEDM was analyzed. It was observed that lower level of WEDM process parameters were more favorable to obtain the least KW and Ra values. On the contrary, higher value of MRR and μh can be attained at higher level of I, WT and FP. Further, the effect of uncoated and zinc coated brass wire electrodes were also studied considering four responses, viz. Ra, KW, machining time (MT) and μh. It was apparent that, zinc coated brass wire was more preferable for responses like Ra, KW and μh compared to uncoated brass wire counterparts. FESEM analysis discovered that micro and large cracks, wide craters, recast layer, surface crack density, etc. were more prominent on the machined surface of brass wire compare to zinc coated brass wire. In efforts to precisely predict and compare few significant WEDM machinability aspects of SMA nitinol, the soft computing technique general regression neural network (GRNN) model was developed. The prediction error of the GRNN model was noted as ±5% within the studied range of machining parameters. A multivariate approach, Fuzzy logic coupled with VIKOR (Vise Kriterijumska Optimizacija Kompromisno Resenje) was also introduced to optimize different correlated responses. Confirmation test has been done to validate the optimum process combination which demonstrates the improvement in WEDM responses. In the next stage, two smart prediction tools, GRNN and multiple regression analysis (MRA) model were developed to predict and compare WEDM machinability aspects of titanium grade 6 which proposes that the GRNN model is more consistent and satisfactory than that of the MRA model. A mathematical model was also developed using MRA to define the important parameters stirring the responses. A multivariate hybrid approach, combining multi-objective optimization on the basis of ratio analysis (MOORA) and principal component analysis (PCA) was proposed to optimize different correlated responses during WEDM process of titanium grade 6. Multivariate hybrid approach MOORA-PCA was recognized to be time saving, effective, accurate and comprehensive approach to solve multi-criteria tasks in WEDM operation. In the final stage, a miniature spur gear using WEDM operation was successfully made from SMA nitinol plate. A multi-criteria decision making (MCDM) approach, desirability function analysis (DFA), was utilized to find out the optimum input parameter setting for the same. The optimum process parameters for each desired responses were also attained using Taguchi‘s signal-to-noise ratio. A 3D thermal model was developed for the optimum parametric condition to observe the total heat flux, temperature and equivalent stress distribution in nitinol.
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Thesis (Ph.D/M.Tech R) Thesis (Ph.D/M.Tech R) Thesis Section Reference Not for loan T881

Thesis Ph.D/M.Tech (R) National Institute of Technology, Rourkela

With the development of the modern manufacturing industry, the demands for hard and difficult-to-cut materials having properties like biocompatibility, corrosion resistance, toughness, light weight, environmental and health friendly nature are increasing day by day. Some of the common difficult-to-cut materials are shape memory alloys (SMA), titanium based alloys, nickel based alloys, etc. are found to be widely used in biomedical, automobile, aerospace and micro-engineering industries. Continuous development and innovation of modern engineering materials as well as the necessity for complex shaped components have made wire electrical discharge machining (WEDM) an imperative choice in production industry.

The unique ability of SMA to memorize its previous form has drawn notable attention recently in a wide range of commercial applications. In recent years, development and growth of SMA nitinol applications in the manufacturing industry have also been fairly strong. For the manufacturing sector, machinability study of nitinol has become a significant feature. The choice of the titanium alloy grade 6 was made taking into account its widespread area of applications for airframe, propellant containment for rockets and space vehicles. Further, to face the global competitiveness in manufacturing industry, selection of the best process, machine and equipment, the best parameter setting for preferred outcomes is absolutely important. The contradictive nature of responses and the availability of numerous options make it extremely difficult to select the best prediction and selection procedure.

At the beginning, the effect of different independent input parameters like pulse-on time (TON), discharge current (I), wire tension (WT), wire speed (WS) and flushing pressure (FP) on the key machinability aspects namely kerf width (KW), arithmetic mean roughness (Ra), material removal rate (MRR) and micro-hardness (μh) of SMA nitinol during WEDM was analyzed. It was observed that lower level of WEDM process parameters were more favorable to obtain the least KW and Ra values. On the contrary, higher value of MRR and μh can be attained at higher level of I, WT and FP.
Further, the effect of uncoated and zinc coated brass wire electrodes were also studied considering four responses, viz. Ra, KW, machining time (MT) and μh. It was apparent that, zinc coated brass wire was more preferable for responses like Ra, KW and μh compared to uncoated brass wire counterparts. FESEM analysis discovered that micro and large cracks, wide craters, recast layer, surface crack density, etc. were more prominent on the machined surface of brass wire compare to zinc coated brass wire.

In efforts to precisely predict and compare few significant WEDM machinability aspects of SMA nitinol, the soft computing technique general regression neural network (GRNN) model was developed. The prediction error of the GRNN model was noted as ±5% within the studied range of machining parameters. A multivariate approach, Fuzzy logic coupled with VIKOR (Vise Kriterijumska Optimizacija Kompromisno Resenje) was also introduced to optimize different correlated responses. Confirmation test has been done to validate the optimum process combination which demonstrates the improvement in WEDM responses.
In the next stage, two smart prediction tools, GRNN and multiple regression analysis (MRA) model were developed to predict and compare WEDM machinability aspects of titanium grade 6 which proposes that the GRNN model is more consistent and satisfactory than that of the MRA model. A mathematical model was also developed using MRA to define the important parameters stirring the responses.

A multivariate hybrid approach, combining multi-objective optimization on the basis of ratio analysis (MOORA) and principal component analysis (PCA) was proposed to optimize different correlated responses during WEDM process of titanium grade 6. Multivariate hybrid approach MOORA-PCA was recognized to be time saving, effective, accurate and comprehensive approach to solve multi-criteria tasks in WEDM operation.

In the final stage, a miniature spur gear using WEDM operation was successfully made from SMA nitinol plate. A multi-criteria decision making (MCDM) approach, desirability function analysis (DFA), was utilized to find out the optimum input parameter setting for the same. The optimum process parameters for each desired responses were also attained using Taguchi‘s signal-to-noise ratio. A 3D thermal model was developed for the optimum parametric condition to observe the total heat flux, temperature and equivalent stress distribution in nitinol.

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