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i-manager's Journal on Mechanical Engineering (JME)

Current Issue Vol. 14 Issue 2

Mechanization and Import Substitution in Zimbabwean Farmers' Equipment: A Case Study of the Revitalization of an Abandoned Tractor Trailer
Drill String Vibrational Analysis and Parametric Optimization for a Portable Water Well Rig Development
An Efficient Deep Neural Network with Amplifying Sine Unit for Nonlinear Oscillatory Systems
The Occupational Directness of Nanorobots in Medical Surgeries
Recent Trends in Solar Thermal Cooling Technologies

Most Cited

Volume 3 Issue 3 May - July 2013

Research Paper

Influence of Process Parameters during Fabrication of Si Microchannels Using Microultrasonic Machining

Tarlochan Singh* , Apurbba Kumar Sharma, Akshay Dvivedi*

* P.G. Scholar, Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, India.

Associate Professor, Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, India.

* Assistant Professor, Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, India.

Singh, T., Sharma, A. K., & Dvivedi, A. (2013). Influence of Process Parameters during Fabrication of Si Microchannels Using Microultrasonic Machining. i-manager's Journal on Mechanical Engineering, 3(3), 1-7. https://doi.org/10.26634/jme.3.3.2365

Abstract

Micro ultrasonic machining is being explored of late as a mechanical micromachining method to fabricate micro channels on hard and brittle materials. A significant advantage of the process is that the process can be used to machine materials irrespective of their electrical conducting unlike many other popular micro fabrication processes. In the present paper, influences of process parameters were investigated while fabricating microchannels on silicon wafers. Response Surface Methodology (RSM) approach was used to analyze the results. Abrasive mesh size, static load, slurry concentration and power rating were considered as input process parameters while surface roughness and Material Removal Rate (MRR) were considered as process responses. The response curves illustrating the individual effect and also the combined effect of process parameters have been presented.

References

[1]. Tuckerman D.B. and Pease R.F.W., (1981). “High-performance heat sinking for VLSI”, IEEE Electron Journal, 2, 126–129.

[2]. Kandlikar S.G. and Grande W.J., (2002). “Evolution of Microchannel Flow Passages Thermohydraulic Performance and Fabrication Technology”, paper presented at International Mechanical Engineering Conference and Exposition, New Orleans.

[3].Kandlikar S.G., (2003). “Microchannels and Minichannels History, Terminology, Classification and Current Research needs”, paper presented at International Conference on Microchannels and Minichannels, Rochester, New York, USA.

[4].Gilmore R., (1989). “Ultrasonic machining and orbital abrasion techniques”, Society of Manufacturing Engineers Tech. Paper, NM89-419, 1–20.

[5]. Neppiras E. A. and Foskett R. D., (1957). “Operating conditions and performance of ultrasonic drills”, Philips Tech. Rev., 18(12) ,368–379.

[6].Moore D., (1985), “Ultrasonic impact grinding”, Proc. Non-traditional Machining Conference, Cincinnati.

[7].Gilmore R., (1990). “Ultrasonic machining of ceramics”, Society of Manufacturing Engineers Tech. Paper, MS90-346, 12.

[8].Rozenberg L. D., Kazantsev V. F. et al., (1964). “Ultrasonic cutting, Consultants Bureau”, New York.

[9]. Kohls J., (1984). “Ultrasonic manufacturing process: Ultrasonic machining (USM) and ultrasonic impact grinding (USIG)”, The Carbide and Tool Journal, 16(5), 12–15.

[10]. Moreland M. A., (1988). “Versatile performance of ultrasonic machining”, Ceramic bulletin, 67(6), 1045 –1047.

[11]. Thoe T.B., Aspinwall D. K. and Wise M. L. H., (1998). “Review on ultrasonic machining”, Int. J. Mach. Tools Manufacturing, Elsevier Science Lt., 38(4), 239–255.

[12]. Jain V.K., (2012). “Some Studies on Development of Microchannels using Microultrasonic machining”, Ph.D. Thesis, Indian Institute of Technology Roorkee, Roorkee.

[13]. Shaw M. C., (1956). “Ultrasonic grinding”. Microtechnic, 10(6), 257–265.

[14]. Wojchiechowski M. P. et al., (1972). “Ultrasonic machining: Past, present and future”, SME Paper, MR72-188, 12.

[15]. Ghabriel S. R., Saleh S. M., Kohail A. and Moisan A., (1982). “Problems associated with electro discharge machined electrochemically machined and ultrasonically machined surfaces”, Wear, 83, 275–283.

[16]. Weller E. J., (1984). “Non-traditional machining processes”, Society of Manufacturing Engineers, 2, 15–71.

[17]. Khairy A. B. E., (1990) “Assessment of some dynamic parameters for the ultrasonic machining process”, Wear, 137, 187–198.

[18]. Box G.E.P, Hunter J.S., (1957). “Multifactor experimental design for exploring response surfaces”, The Annals of Mathematical Statistics, 28, 195- 241.

[19]. Box G.E.P, Behnken D.W., (1960). “Some new three level design for the study of quantitative variables”, Technometrics, 2, 455-475.

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Research Paper

Determination of the Distinct Mechanisms from a Given Kinematic Chain

Ali Hasan*

*Department of Mechanical Engineering, Jamia Millia Islamia, Delhi, India.

Hasan, A. (2013). Determination of the Distinct Mechanisms from a Given Kinematic Chain. i-manager's Journal on Mechanical Engineering, 3(3), 8-14. https://doi.org/10.26634/jme.3.3.2366

Abstract

This paper presents a new method to identify the distinct mechanisms (shortened as DM) from a given kinematic chain. The kinematic chains (shortened as KC) are represented in the form of the Weighted Physical Connectivity [WPCM] Matrix. Two structural invariants derived from the characteristic polynomials of the [WPCM] matrix of the KC are the sum of absolute characteristic polynomial coefficients (shortened as ∑ WPCM) and maximum absolute value of the characteristic polynomial coefficient (shortened as MWPCM). These invariants are used as the composite identification number of a KC and mechanisms. It is capable of detecting DM in all types of simple jointed planer KC with same or different kinematic pairs (shortened as KP). This study will help the designer to select the best KC and mechanisms to perform the specified task at the conceptual stage of design. The application of this study is in research and development industries. The proposed method has been explained with the help of examples and does not require any test for isomorphism separately.

References

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[5] E.R. Tuttle et al., (1989). ASME Journal of Mechanisms, Transmissions, and Automation in Design, 111pp498.

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[7].J.K. Chu, W.Q. Cao, (1992). Proceedings of ASME Conference 47, pp207-210.

[8].P.W. Jensen, (1992). Classical and Modern Mechanism for Engineers and Inventors, Marcel Dekker, Inc. New York.

[9].F.G. Kong, QLi, w.j. Zhang, (1999). Mechanism, and Machine Theory, 34, pp 271-283.

[10].Uicher J.J. and Raicu A., (1975). Mech. Mach. Theory, 10, pp375-383.

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[12].C.Y. Hsiung & G.Y. Mao, Linear Algebra, World Scientific Publishing Co.Pte.Ltd.

[13]. Madan S.R., Jain R.C., (2002). IE (I) Journal-MC, 82, pp164-169.

[14]. Mruthyunjaya T.S., (1984). Mechanism and Machine Theory, Vol.19 (6), pp497-505..

[15]. Ali Hasan et. al, (2006). International Journal of Applied Engineering Research, Vol.1, No-2, pp.251-264.

[16]. Ali Hasan et.al., (2006). Journal of Mechanical Engineering, Slovak Republic, Vol.57,pp. 1-9.

[17]. Ali Hasan et.al., (2012). i-manager's Journal on Mechanical Engineering, Vol. 2 , No. 1,pp 7-15.

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Research Paper

Suppliers Selection In Manufacturing Environment Using Range Of Value Method

Goutam Kumar Jha* , Prasenjit Chatterjee, Rupsa Chatterjee*****

- MCKV Institute of Engineering, Liluah, Howrah, India.

Project Engineering Division, Linde India Ltd, Kolkata, India.

**** Department of Production Engineering, Jadavpur University, Kolkata, India.

Jha, G. K., Chatterjee, P., Chatterjee, R., & Chakraborty, S. (2013). Suppliers Selection in Manufacturing Environment using Range of Value Method. i-manager's Journal on Mechanical Engineering, 3(3), 15-22. https://doi.org/10.26634/jme.3.3.2367

Abstract

Selection and evaluation of suppliers is a burning issue in the context of modern manufacturing environment to increase organizational competitiveness due to an extensive variety of customer demands. It has become more and more complicated to meet the challenges of international competitiveness, and as the decision makers need to assess a wide range of alternative suppliers based on a set of conflicting criteria. Because of these reasons, supplier selection has got considerable attention by the academicians and researchers. Although, a huge number of mathematical approaches is now available for supplier selection under discrete manufacturing environment, this paper explores the applicability of almost a new multi-criteria decision-making approach, i.e. range of value method (ROVM) for supplier selection. The proposed method is used to rank the alternative suppliers, for which several ordinal and cardinal requirements are considered simultaneously. Two illustrative examples are cited which prove that ROVM method can be very useful to solve real time supplier selection problems. In each example, a list of all the possible choices from the best to the worst suitable suppliers is obtained, which almost match with the rankings as derived by the past researchers.

References

[1]. Chen, Y.J., (2011). “Structured methodology for supplier selection and evaluation in a supply chain”. Information Sciences, 181, pp. 1651-1670.

[2]. Weber, C.A., Current, J.R. & Benton, W.C., (1991). “Vendor selection criteria and methods”, European Journal of Operational Research, 50, pp. 2-18.

[3]. Dickson, G., (1996). “Analysis of vendor selection system and decisions”, Journal of Purchasing, 2, pp. 28-41.

[4]. Roodhooft, F. & Konings, J., (1996). “Vendor selection and evaluation an activity based costing approach”, European Journal of Operational Research, 96, pp. 97-102.

[5]. Ghodsypour, S.H. & O'Brien, C., (1998). “A decision support system for supplier selection using an integrated analytical hierarchy process and linear programming”, International Journal of Production Economics, 56-57, pp. 199-212.

[6]. Akarte, M.M., Surendra, N.V., Ravi, B. & Rangaraj, N., (2001). “Web based casting supplier evaluation using analytical hierarchy process”, Journal of the Operational Research Society, 52, pp. 511-522.

[7]. Talluri, S. & Narasimhan, R., (2003). “Vendor evaluation with performance variability: A max-min approach”, European Journal of Operational Research, 146, pp. 543-552.

[8]. Liu, F-H.F. & Hai, H.L., (2005). “The voting analytic hierarchy process method for selecting supplier”, International Journal of Production Economics, 97, pp. 308-317.

[9]. Zhu, J., Liu, S. & Fang, Z., (2006). “Interval analytical approach to roll vendor selection for a large-scale steel-iron enterprise”, Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China, pp. 7201-7205.

[10]. Shyur, H-J. & Shih, H-S., (2006). “A hybrid MCDM model for strategic vendor selection”, Mathematical and Computer Modelling, 44, pp. 749-761.

[11]. Amid, A., Ghodsypour, S.H. & O'Brien, C., (2006). “Fuzzy multi-objective linear model for supplier selection in a supply chain”, International Journal of Production Economics, 104, pp. 394-407.

[12]. Kumar, M., Vrat, P. & Shankar, R., (2006). “A fuzzy programming approach for vendor selection problem in a supply chain”, International Journal of Production Economics, 101, pp. 273-285.

[13]. Chen, C-T., Lin, C-T., & Huang S-F., (2006). “A fuzzy approach for supplier evaluation and selection in supply chain management”, International Journal of Production Economics, 102, pp. 289-301.

[14]. Wang, J-J., & Li, H-F., (2007). “Developing a decision model for supplier selection”, Proceedings of the International Conference on Wireless Communications, Networking and Mobile Computing, Honolulu, Hawaii, USA, pp. 4662-4665.

[15]. Almeida, A.T., (2007). “Multicriteria decision model for outsourcing contracts selection based on utility function and ELECTRE method”, Computers and Operations Research, 34, pp. 3569-3574.

[16]. Ng, W.L., (2008). “An efficient and simple model for multiple criteria supplier selection problem”, European Journal of Operational Research, 186, pp. 1059-1067.

[17]. Wu, T. and Blackhurst, J., (2009). “Supplier evaluation and selection: An augmented DEA approach”, International Journal of Production Research, 47, pp. 4593-4608.

[18]. Athawale, V.M., Mukherjee, P. & Chakraborty, S., (2009). “Supplier selection using multi-criteria decision-making (MCDM) methods”, The IUP Journal of Operations Management, VIII, pp. 41-60.

[19]. Athawale, V.M., Chatterjee P., & Chakraborty S. (2010). “Supplier Selection using PROMETHEE-II method”. International Journal of Manufacturing Technology and Industrial Engineering, 1, pp. 95-102.

[20]. Agarwal, P., Sahai, M., Mishra, V., Bag, M., & Singh, V., (2011). “A review of multi-criteria decision making techniques for supplier evaluation and selection”, International Journal of Industrial Engineering Computations, 2, pp. 801-810.

[21]. Chatterjee, P., Mukherjee, P. & Chakraborty S., (2011). “Supplier selection using Compromise Ranking and Outranking methods”, Journal of Industrial Engineering International, 7, pp. 61-73.

[22]. Chatterjee, P., & Chatterjee, R., (2012). “Supplier evaluation in manufacturing environment using compromise ranking method with grey interval numbers”, International Journal of Industrial Engineering Computations, 3, pp. 393-402.

[23]. Hajkowicz, S. & Higgins, A., (2008). “A comparison of multiple criteria analysis techniques for water resource management”, European Journal of Operational Research, 184, pp. 255-265.

[24]. Liu, F., Ding, F.Y. & Lall, V., (2000). “Using data envelopment analysis to compare vendors for vendor selection and performance improvement”, Supply Chain Management: An International Journal, 5, pp. 143-150.

[25]. Rao, R.V., (2007). “Decision making in the manufacturing environment using graph theory and fuzzy multi attribute decision making methods”, Springer-Verlag, London.

[26]. Pi, W-N. & Low, C., (2006). “Supplier evaluation and selection via Taguchi loss functions and an AHP”, International Journal of Advanced Manufacturing Technology, 27, pp. 625-630.

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Research Paper

Characterization of Structure-Thermo-Mechanical Properties of Ultra High Temperature Ceramic Composites (UHTCCS) By Nondestructive Testing (NDT)

Manab Mallik* , K.K.Roy, Rahul Mitra*, Animesh Talapatra****

Assistant Professor, Department of Metallurgical and Materials engineering, NIT, Durgapur, West Bengal, India.

Professor (Retired), Department of Metallurgical and Materials engineering, IIT, Kharagpur, West Bengal, India.

Professor, Department of Metallurgical and Materials engineering, IIT, Kharagpur, West Bengal, India.

**** Assistant Professor, Department of Automobile Engineering, MCKV Institute of Engineering, Howrah, West Bengal, India.

Mallik, M., Roy, K. K., Mitra, R., & Talapatra, A. (2013). Characterization of Structure-Thermo-Mechanical Properties of Ultra High Temperature Ceramic Composites (UHTCCS) By Nondestructive Testing (NDT). i-manager's Journal on Mechanical Engineering, 3(3), 23-30. https://doi.org/10.26634/jme.3.3.2368

Abstract

The objective of the work is to find out microstructure and properties of two zirconium diboride based ceramic composites which were obtained from ?ne commercial zirconium diboride powders and the addition of zirconium carbide, Silicon carbide, Silicon nitride as secondary phases and correlation between microstructure-thermo mechanical properties of two Ultra High Temperature Ceramic Composites (UHTCC) based upon the different secondary phases composition. After that characterization, the influence of composition in microstructure on elevated temperature behavior of zirconium diboride based ultra high temperature ceramic composites are analysed. Micro - structural analyses by Optical and SEM showed oxidation-induced surface modifications of ZrB /SiC materials with oxide 2 layers composed of silica, boron and zirconia (at higher temperature). These composites were prepared from zirconium diboride, zirconium carbide, Silicon carbide and Silicon nitride powders by ball milling and hot pressing. These two UHTCCs were fabricated by hot pressing into 25 mm diameter and 4 mm thickness discs. The measurement of the mechanical properties of two ceramic composite were done by pulse-echo ultrasonic testing. Indentation water quenched method was used for measuring thermal stresses in UHTCCs during characterizing thermo mechanical properties and thermal shock behavior.

References

[1]. Clougherty, E.V., Pober, R.L., and Kaufman, L., (1968). “Synthesis of oxidation resistant metal diboride Composites”. Trans. Met. Soc. AIME, 242, 1077–1082.

[2]. Fenter, J.R., (1971). “Refractory diborides as engineering materials”. SAMPE Quarterly, 2, 1–15.

[3]. Tripp, W.C., Davis, H.H., and Graham, H.C., (1973). “Effect of a SiC addition on the oxidation of ZrB2 Cer”. Bull., 52, 612–616.

[4]. Anon, (1981). “Engineering property data on selected ceramics”, Vol. III, Single oxides. MCIC-HB-07, Battelle, Columbus, OH.

[5]. Jin, Z.-H. and Mai, Y.-W. J. Am. Ceram. Soc.,(1995). 78, 1873.

[6]. Zheng, Guo- jun, Zhen-Yan D, Naoki K, Jian-Feng Y, and Tatsuki O, (2000). “Reactive Hot Pressing of ZrB2-SiC composites”, Journal of American Ceramic Society, 83 [27] 233032.

[7]. Zheng, Shuqi, Guanghui M, Zengda Z, Huashun Y, Jiande H, and Weiti W, (2002). “Oxidation behavior of ZrB2 powders below 1073 K in air”, Metallofizika I Noveishie Tekhnologii, 381-387, March.

[8]. Anon., (1981). “Engineering property data on selected ceramics”, Vol. III, Single oxides. MCIC-HB-07, Battelle, Columbus, OH.

[9]. G.E. Lockyer, E.A. Proudfoot, (1967). “Nondestructive determinations of mechanical properties of refractory materials”, Am. Ceram. Soc. Bull, 46 [5] 521–526.

[10]. Parker, W.J. et al. (1961). “Flash Method of Determining Thermal Diffusivity, Heat Capacity, and Thermal Conductivity.” Journal of Applied Physics, Vol. 12, No. 9, pp. 1679-1685.

[11]. D.P.H. Hasseleman (1969). J. Amer.Ceram.Soc., 52, 600.

[12]. D.P.H. Hasseleman(1970). J. Amer.Ceram.Soc. 53,490.

[13]. Andersson T., Rowcliffe D. J. (1996). J. Am.Ceram. Soc. 79, 1509 (1996).

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Research Paper

Multi-Response Optimization of Turning Parts Using Integrated Taguchi-Grey Relational Analysis

Neeraj Sharma* , Sanjeev Garg, Pardeep Sharma*

* Assistant Professor, Department of Mechanical Engineering, R.P. Inderaprastha Institute of Technology, Karnal, India.

Head, Department of Mechanical Engineering, JMIT. Radaur (Yamunanagar), India.

* Assistant Professor, Department of Mechanical Engineering, P.I.E.T., Samalakha, India.

Sharma, N., Garg, S., & Sharma, P. (2013). Multi-Response Optimization of Turning Parts Using Integrated Taguchi-Grey Relational Analysis. i-manager's Journal on Mechanical Engineering, 3(3), 31-36. https://doi.org/10.26634/jme.3.3.2369

Abstract

Multi-Response optimization of response variables having opposite nature is cumbersome to obtain without use of optimization techniques. In this research work, Grey relational analysis and Taguchi Design approach are used for multi response optimization of metal removal rate and surface roughness during turning of . Taguchi orthogonal array L is used to design the experiment. Signal to Noise (S/N) ratio of both responses i.e. metal removal rate 9 and surface roughness are used to calculate grey relational grade. AISI 1040 MS bars Analysis of Variance (ANOVA) is used to identify the significance of process parameters on grey relational grade and shows that feed rate and spindle speed are most significant process parameters for both the responses. The confirmation experiments show that Taguchi-Grey relation analysis can be successfully utilized to obtain the multi-response optimization.

References

[1]. D. Agarwal, S. Basavarajappa, and G. Chandramohan. (2004). “Dry Sliding Wear Studies On Hybrid MMC's – A Taguchi Technique” International Symposium of Research Students on Materials Science and Engineering, ISRS 2004, pp 1-8.

[2]. A. Aggarwal and H. Singh. (2004). “Optimization of machining techniques – A retrospective and literature review” Sadhana, Vol. 30, No. 6, pp. 699–711.

[3]. H. Singh and P. Kumar. (2006). “Optimizing feed force for turned parts through the Taguchi Technique” Sadhana, Vol. 31, No. 6, pp. 671–681.

[4]. D. Selvaraj Philip and P. Chandramohan, P. (2010). “Optimization of Surface Roughness of AISI 304 Austenitic Stainless Steel in Dry Turning Operation Using Taguchi Design Method”, Journal of Engineering Science and Technology, Vol. 5, No. 3, pp. 293 –301.

[5]. L. Savadamuthu, S. Muthu, P. Vivekanandan. (2011). “Quality Improvement in Turning Process using Taguchi's Loss Function”, International Journal of Soft Computing and Engineering, Vol. 1, No. 4, pp 2231-2307.

[6]. N. Sharma, R. Sharma. (2012). “Optimization of Process Parameters of Turning Parts: A Taguchi Approach,” Journal of Engineering, Computers & Applied Sciences, Vol. 1, No. 1, pp.32-36.

[7]. M. V. Kumar, S.A. Babu, R. Venkatasamy, M. Raajenthiren. (2010). “Optimization of the WEDM Parameters on Machining Incoloy 800 Super alloy with Multiple Quality Characteristics”, International Journal of Engineering Science and Technology, Vol. 2, pp. 1538-1547.

[8]. H. Singh, R. Khanna. (2011). “Parametric optimization of cryogenic-treated D-3 for cutting rate in wire electrical discharge machining”, Journal of Engineering & Technology, Vol. 1, No. 2, pp. 59-64.

[9]. D.C. Montgomery. (1996). “Introduction to Statistical Quality Control”, 3rd Edition, John Wiley & Sons: New York, 1996.

[10]. J. Antony. (2011). “Simultaneous Optimisation of multiple quality characteristics in manufacturing processes using Taguchi's quality loss function”, Int J of Advanced Mfg Technol, Vol. 17, 134 p.

[11]. K.M. Patel, P. M. Pandey and P. V. Rao. (2010). “Optimisation of process parameters for multi-performance characteristics in EDM of Al2O3 ceramic composite”, Int J Adv Manuf Technol, Vol. 47, 1137–1147.

[12]. J. Deng, (1982). “Control problems of grey systems”, System and Control Letters, Vol. 5, 288.

i-manager's Journal on Mechanical Engineering (JME)

Current Issue Vol. 14 Issue 2

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Most Cited

Volume 3 Issue 3 May - July 2013

Influence of Process Parameters during Fabrication of Si Microchannels Using Microultrasonic Machining

Tarlochan Singh* , Apurbba Kumar Sharma**, Akshay Dvivedi***

Singh, T., Sharma, A. K., & Dvivedi, A. (2013). Influence of Process Parameters during Fabrication of Si Microchannels Using Microultrasonic Machining. i-manager's Journal on Mechanical Engineering, 3(3), 1-7. https://doi.org/10.26634/jme.3.3.2365

Abstract

References

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Determination of the Distinct Mechanisms from a Given Kinematic Chain

Ali Hasan*

*Department of Mechanical Engineering, Jamia Millia Islamia, Delhi, India.

Hasan, A. (2013). Determination of the Distinct Mechanisms from a Given Kinematic Chain. i-manager's Journal on Mechanical Engineering, 3(3), 8-14. https://doi.org/10.26634/jme.3.3.2366

Abstract

References

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Suppliers Selection In Manufacturing Environment Using Range Of Value Method

Goutam Kumar Jha* , Prasenjit Chatterjee**, Rupsa Chatterjee*******

*-** MCKV Institute of Engineering, Liluah, Howrah, India. *** Project Engineering Division, Linde India Ltd, Kolkata, India. **** Department of Production Engineering, Jadavpur University, Kolkata, India.

Jha, G. K., Chatterjee, P., Chatterjee, R., & Chakraborty, S. (2013). Suppliers Selection in Manufacturing Environment using Range of Value Method. i-manager's Journal on Mechanical Engineering, 3(3), 15-22. https://doi.org/10.26634/jme.3.3.2367

Abstract

References

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Characterization of Structure-Thermo-Mechanical Properties of Ultra High Temperature Ceramic Composites (UHTCCS) By Nondestructive Testing (NDT)

Manab Mallik* , K.K.Roy**, Rahul Mitra***, Animesh Talapatra****

Abstract

References

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Multi-Response Optimization of Turning Parts Using Integrated Taguchi-Grey Relational Analysis

Neeraj Sharma* , Sanjeev Garg**, Pardeep Sharma***

* Assistant Professor, Department of Mechanical Engineering, R.P. Inderaprastha Institute of Technology, Karnal, India. ** Head, Department of Mechanical Engineering, JMIT. Radaur (Yamunanagar), India. *** Assistant Professor, Department of Mechanical Engineering, P.I.E.T., Samalakha, India.

Sharma, N., Garg, S., & Sharma, P. (2013). Multi-Response Optimization of Turning Parts Using Integrated Taguchi-Grey Relational Analysis. i-manager's Journal on Mechanical Engineering, 3(3), 31-36. https://doi.org/10.26634/jme.3.3.2369

Abstract

References

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Tarlochan Singh* , Apurbba Kumar Sharma, Akshay Dvivedi*

Goutam Kumar Jha* , Prasenjit Chatterjee, Rupsa Chatterjee*****

- MCKV Institute of Engineering, Liluah, Howrah, India.

Project Engineering Division, Linde India Ltd, Kolkata, India.

**** Department of Production Engineering, Jadavpur University, Kolkata, India.

Manab Mallik* , K.K.Roy, Rahul Mitra*, Animesh Talapatra****

Neeraj Sharma* , Sanjeev Garg, Pardeep Sharma*

* Assistant Professor, Department of Mechanical Engineering, R.P. Inderaprastha Institute of Technology, Karnal, India.

Head, Department of Mechanical Engineering, JMIT. Radaur (Yamunanagar), India.

* Assistant Professor, Department of Mechanical Engineering, P.I.E.T., Samalakha, India.