Question

CASE STUDY/ IN-COURSE PROJECT

DIRECTIONS:

• In process of selecting materials for design applications, it is required knowledge’s about material testing.
• Set up a study about one of the subjects / tests shown below indicating its effect on selecting materials for design applications:

1. Non destructive testing
2. Tensile Testing                              
3. Compression Testing               
4. Charpy Impact Testing
5. Izod Impact Testing
6. Hardness Testing
7. Torsional Testing      
8. Bending Testing
9. Phase diagram
10. Alloy steel

The case study report must contain the followings:

1. Title of the Project /Case Study
2. Objective of the Study Summary Abstracts
3. Introduction and Literature Review
4. Results and Discussion
5. Conclusions and recommendations
6. References

• The submission of the report 11^th week of the term.
• Late submission will not be accepted and will merit a zero credit in the Final Project.
• Presentation of the report will be on 11th week of the term.

Answer 1

CASE STUDY ON ALLOY STEEL

ABSTRACT

            An Alloy Steel is one of the materials widely used in all of the industrial applications. So it is important to understand the properties and their compositions with alloying elements. Since this paper focused on to understand the concept of Alloy Steel. Also this paper contains additive information about alloy steel such as Properties of Alloy Steel, Alloying Elements and their effects on Mechanical Properties of Alloy Steel, percentage of alloying elements in compositions etc. Also this paper consists of a glance at applications of Alloy Steel.

OBJECTIVES

1. To understand the concept of Alloy Steel

2. Examine the properties of Alloy Steel

3. Study of Alloying Elements.

4. Effect of Alloying Elements on Microstructure and Mechanical Properties.

INTRODUCTION

Steel is one of the most popular elements in construction as well as mechanical industry. The variety of Steel elements provides a great scope for the application in wide range. Fundamentally, steel is an alloy of iron with low amounts of carbon. There are thousands of different types of steels which are created to suit different kinds of applications. These broadly fall into 4 types – carbon steel, tool steel, stainless steel and alloy steel. Carbon steels form the majority of steels produced in the world today. Tool steels are used to make machine parts, dies, and tools. Stainless steels are used to make common household items. Alloy steels are made of iron, carbon and other elements such as Vanadium, Silicon, Nickel, Manganese, Copper, and Chromium.

But sometimes, pure steel may lacks in some of applications due to limited properties, Alloy steel takes place instead of pure steel. Alloy Steel is nothing but a homogeneous mixture of Pure Steel and Few of the Alloying Elements in proper proportion. When other elements comprising metals and non-metals are added to carbon steel, alloy steel is formed. These alloy steels display various environmental, chemical, and physical properties that can vary with the elements used to alloy. Here the proportion of alloying elements can provide different mechanical properties.

LITERATURE REVIEW

A.Venkata Vishnu .et.al. [7]outlines an experimental study to optimize the effects of selected cutting parameters i.e. Cutting Speed, Feed rate, Depth of cut and type of tool, for Surface Roughness of EN-36 steel alloy by employing Taguchi robust design methodology. Taguchi orthogonal array is designed with three levels of turning parameters and experiments are carried out using L9 (34 ) orthogonal array. Taguchi method stresses the importance of studying the response variation using the Analysis of Variance (ANOVA), resulting the minimization of quality characteristic variation due to uncontrollable parameter. The surface roughness is considered as the quality characteristic parameter in the concept of “the smaller the better”. The surface roughness values measured from experiment and their optimum value for surface roughness are calculated. Analysis of Variance suggests that the selected cutting parameters are significant and Feed rate has the most significant factor for the surface roughness. By using Taguchi Robust Design methodology the End milling of EN-31 steel alloy is carried out in order to optimize the milling process parameters and to minimize the surface roughness. The selected milling process parameters are Cutting Speed, Feed rate, Depth of cut and coolant flow. Taguchi orthogonal array is designed with three levels, four factors and nine experiments using L9 (34 ) orthogonal array. The nine experiments are performed and surface roughness is calculated. Results obtained by Taguchi Method, shows that the factors affecting the surface roughness are Significant and Cutting Speed is the most influence significant parameter. Multiple Regression equation is formulated for estimating the predicted values for surface roughness [8]. Taguchi approach, the Turning of EN-36 steel alloy is carried out in order to optimize the turning process parameters. The present paper deals with the optimization of selected process parameters, i.e. Speed, Feed rate, Depth of cut and type of tool. Taguchi orthogonal array is designed with three levels of machining parameters and different experiments are done using L9 (34) orthogonal array. Taguchi method stresses the importance of studying the response variation using the signal to noise (S/N) ratio, resulting the minimization of quality characteristic variation due to uncontrollable parameter. The material removal rate is considered as the quality characteristic in the concept of “the larger the better”. The material removal values measured from experiment and their optimum value for material removal rate are calculated. The S/N ratio of predicted value and verification test values are valid when compared with the optimum value. It is found that S/N ratio value of verification test is within the limits of predicted value and the objective of the work is full filled [9]. Shashikant.et.al. used to investigate the relationships and parametric interactions between the measurable and controllable variables on the material removal rate (MRR) in die sinking EDM of EN19 material. The material is extensively being used for the application in High speed components e.g. gears. For conducting the experiments, four process variables viz. pulse on time, pulse off time, discharge current and gap voltage were considered and electrolytic copper was used as the electrode material. Total 31 experiments were carried out for different combinations of process parameters. The experimental results were analyzed using Response Surface Model (RSM). The significant coefficients were obtained by performing analysis of variance (ANOVA). From the analysis, it was found that pulse off time, discharge current, gap voltage and the interaction terms were significant where as the pulse on time had almost negligible effect towards MRR. This methodology was found to be very effective and the model sufficiency was very satisfactory. Moreover, an attempt has been made to optimize the material removal rate in the studied region. The error between the predicted and experimental MRR value was found to be 1.45% [10]. Mahendra Korat et.al. outlines an experimental study to optimize the effects of cutting parameters on surface finish and MRR of EN24 work material by employing Taguchi techniques. The orthogonal array, signal to noise ratio and analysis of variance were employed to study the performance characteristics in turning operation. Five parameters were chosen as process variables: Speed, Feed, Depth of cut, Nose radius, Cutting environment (wet and dry). The experimentation plan is designed using Taguchi’s L18 Orthogonal Array (OA) and Minitab 16 statistical software is used. Optimal cutting parameters for, minimum surface roughness (SR) and maximum material removal rate were obtained. Thus, it is possible to increase machine utilization and decrease production cost in an automated manufacturing environment [11]. Abhang, L. B.et.al. objective is to select a right lubricant from amongst a number of lubricants during the machining of En-31 steel work piece with tungsten carbide inserts by using combined multiple attribute decision–making method. The procedure is based on a combined TOPSIS and AHP method. The selection of an optimal material for an engineering design from a list of available alternative materials on the basis of two or more attributes in multiple attribute decision making problem. The analytic hierarchy process, being a simple, but powerful decision making tool, is being applied to solve different manufacturing problems. TOPSIS method is based on the concept that the chosen alternative should have the shortest Euclidean distance from the ideal solution and the farthest from the negative ideal solution. TOPSIS thus gives a solution that is not only closest to the hypothetical best, which is also the farthest from the hypothetically worst. Lubricant selection factors are identified and these are chip-tool interface temperature, cutting force, tool wear and surface roughness. Combined multi-attribute decision-making is aimed at integrating different measures into a single global lubricant index helps to select right lubricant and rank the given lubricant for a steel turning operation. The framework that is used in steel turning operation could serve as one of the tools for making a strategic decision. The effectiveness of our model is demonstrated through an actual experimental work [12]. Keerthiprasad.Ket.al. have been discussed widely used in aerospace and automotive industries. Machining of these materials requires better understanding of cutting processes regarding accuracy and efficiency. This study addresses the modelling of the machinability of EN353 and 20mncr5 materials. In this study, multiple regression analysis (MRA) is used to investigate the influence of some parameters on the thrust force and torque in the drilling processes of alloy steel materials. The model were identified by using cutting speed, feed rate, and depth as input data and the thrust force and torque as the output data. The statistical analysis accompanied with results showed that cutting feed (f) were the most significant parameters on the drilling process, while spindle speed seemed insignificant. Since the spindle speed was insignificant, it directed us to set it either at the highest spindle speed to obtain high material removal rate or at the lowest spindle speed to prolong the tool life depending on the need for the application. The mathematical model is based on a power regression modelling, dependent on the three above mentioned parameters [13]. Alloy Steel EN-24 (Medium Carbon Steel) used in manufacturing of Automotive & aircraft components, Axles & Axles components, Shafts, Heavy duty Gears, Spindles, Studs, Pins, collets, bolts, couplings, sprockets, pinions & pinion arbors. Turning is the most common process used in manufacturing sector to produce smooth finish on cylindrical surfaces. Surface roughness is the important performance characteristics to be considered in the turning process is affected by several factors such as cutting tool material, spindle speed, feed rate, depth of cut and material properties. In this research Response surface methodology (RSM) was applied to determine the optimum machining parameters leading to minimum surface roughness in turning process. Puneet Saini et.al has studied the effect of carbide inserts on EN-24 Alloy Steel surface by using three parameters (spindle speed, feed rate and depth of cut). This research was conducted by using 100 HS Stallion CNC Lathe machine. Seventeen sets of experiments were performed. In this work empirical models were developed for surface roughness by considering spindle speed, feed rate and depth of cut as main controlling factors using response surface methodology. The optimum value of the surface roughness (Ra) comes out to be 0.48 μm. It is also concluded that feed rate is the most significant factor affecting surface roughness followed by depth of cut. As Cutting speed is the less significant factor affecting surface roughness. Optimum results are finally verified with the help of confirmation experiments [14].\ Joseph Emmanuel et. al. objective is to develop a taguchi optimization method for low surface roughness in terms of process parameters when turning the EN-353 steel on conventional lathe machine. Considering the process parameters as Depth of cut, Feed, Spindle Speed, Rake Angle & Pressurized Coolant Jet, a series of turning experiments were performed to measure surface roughness data. Taguchi orthogonal arrays, signal-to-noise(S/N) ratio, and analysis of variance (ANOVA) are used to find the optimal levels and the effect of the process parameters on surface roughness. Confirmation experiment with the optimal levels of process parameters was carried out in order to demonstrate the effectiveness of the taguchi method. It can be concluded that Taguchi method is very suitable in solving the surface quality problem of turned work pieces [15]. T.Rajaprabu et.al. Investigation focuses on the influence of machining parameters on the surface finish obtained in turning of EN19 steel. The experiments are conducted based on Taguchi‘s experimental design technique in this work; the effect of machining parameters on the surface roughness is evaluated and optimum machining conditions for maximizing the metal removal rate and minimizing the surface roughness are determined using Taguchi technique. Signal to Noise ratio, Analysis of means and ANOVA are employed for determining optimum level combination and percentage contribution. Finally an attempt has been made to develop a model for the turning process. The developed model can be effectively used to predict the surface roughness on the machining [16]. Turning is one of the common machining methods in manufacturing industry. Hardness of the material is the most significant property in the field of design to satisfy the safety and reliability. AL. Arumugam et.al investigation is to analyse the changes in the hardness of material on the machined surface due to machining operation (turning) by considering the spindle speed, feed and depth of cut. EN353 forged steel was selected for the analysis to measure the hardness. The hardness was estimated using Rockwell hardness tester by varying the cutting parameters using Taguchi method [17].

CONCLUSIONS

A thorough study of literature suggests that the machining of Alloy Steel is very difficult, compared to other alloy materials. Very few works have been done in the Optimization of process parameters in Machining of steel alloy with different controlled parameters. Review of various latest optimizing techniques such as Taguchi’s approach, shows significant effect of process parameters i.e. depth of cut, feed rate, cutting speed etc. on performance characteristics like surface roughness, tool flank wear, MRR. We also found that for surface roughness the most significant parameters are speed and feed for most of the Alloy Steels and for MRR the most significant parameters are DOC, feed and speed. Form the Literature, the best Suited Lubricant for Most of the Alloy Steels for which optimum results drawn are SAE10, 20 and 40 along with Boric acid. Carbide cutting tools is the best suited tool for Alloy steels compared to HSS.

REFERENCES

[1] Smith, William F.; Hashemi, Javad (2001), Foundations of Material Science and Engineering (4th edition), McGrawHill, p. 394, ISBN 0-07-295358-6.

[2] Degarmo, E. Paul; Black, J T.; Kohser, Ronald A. (2007), Materials and Processes in Manufacturing (10th ed.), Wiley, ISBN 978-0-470-05512-0.

[3] Degarmo, E. Paul; Black, J T.; Kohser, Ronald A. (2003), Materials and Processes in Manufacturing (9th ed.), Wiley, ISBN 0-471-65653-4.

[4] Oberg, E.; et al. (1996), Machinery's Handbook (25th ed.), Industrial Press Inc.

[5] Groover, M. P., 2007, p. 105-106, Fundamentals of Modern Manufacturing: Materials, Processes and Systems, 3rd ed, John Wiley & Sons, Inc., Hoboken, NJ, ISBN 978-0-471-74485-6.

[6] "Stainless steel properties for structural automotive applications" (PDF). Euro Inox. June 2000. Retrieved 2007-08-14

[7] A. Venkata Vishnu, K B G Tilak, Manik Reddy, “Optimization of Process Parameters for Surface Roughness in CNC Turning of EN-36 Material Using Taguchi Robust Design Methodology”, International Journal of Core Engineering & Management (IJCEM), ICCEMT-2015, ISSN: 2348-9510, Special issue, December-2015. pp: 89-104.

[8] A. Venkata Vishnu, G. Guruvaiah Naidu , Ch.Pranav Srivatsav, “Optimization and Regression Analysis for Surface Roughness in Milling of EN-31 Steel Alloy Material”, International Journal of Core Engineering & Management (IJCEM), ICCEMT-2015, ISSN: 2348-9510, Special issue, December-2015. pp: 139-150.

[9] A. Venkata Vishnu, G. Guruvaiah Naidu, K B G Tilak, J.Ramakrishna, “Application of Taguchi Method in the Optimization of Turning Parameters for Material Removal Rate of En-36 Material”, International Journal of Advance Engineering and Research Development E-ISSN (O): 2348-4470 P-ISSN (P): 2348-6406, Volume 2, Issue 8, August2015. pp: 54-62.

[10] Shashikant, Apurba Kumar Roy, Kaushik Kumar “Optimization of machine process parameters on material removal rate in EDM for EN19 material using RSM” IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) e-ISSN: 2278-1684, p-ISSN: 2320-334X PP 24-28.

[11] Mahendra Korat, Neeraj Agarwal, “Optimization of Different Machining Parameters of En24 Alloy Steel In CNC Turning by Use of Taguchi Method” International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622, Vol. 2, Issue 5, September- October 2012, pp.160-164.

[12] Abhang, L. B., Hameedullah, M, “Selection Of Lubricant Using Combined Multiple Attribute Decision-Making Method”, Advances In Production Engineering & Management 7 (2012) 1, 39-50 Issn 1854-6250.

[13] Keerthiprasad.K, Prof Narendra Babu, Dr Chandrashekara, “Regression Analysis and Analysis Of Variance for EN353 and20MnCr5 Alloyed Steels for Drilling Cutting Forces”, Int. Journal of Engineering Research and Applications www.ijera.com ISSN: 2248-9622, Vol. 4, Issue 6( Version 1), June 2014, pp.136-146.

[14] Puneet Saini, Shanti Parkash, Devender Choudhary, “Experimental Investigation of Machining Parameters For Surface Roughness In High Speed CNC Turning of EN-24 Alloy Steel Using Response Surface Methodology”, Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 4, Issue 5( Version 7), May 2014, pp.153-160.

[15] Joseph Emmanuela* and Rahul Davis, “An Experimental Study and Analysis of Surface Roughness in Wet Turning Operation of EN 353 Steel”, International Journal of Current Engineering and Technology ISSN 2277 – 4106, 2013 INPRESSCO.

[16] T.Rajaprabu, Dr.K.Chandrasekaran , P.Dheenathayalan, V.Thirumalairaj& R.Sivakumar, “Optimium Condition For Turning En19 Steel Using Design Of Experiments, International Journal of Applied Engineering Research ISSN 0973- 4562 Volume 10, Number 15 (2015).

[17] A.L. Arumugam, R. Ragothsingh, “Optimization of Turning Process Parameters for Hardness in Forged Steel”, International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 12, December – 2013, ISSN: 2278- 0181.