IJAMS CNC TURNING PROCESS OPTIMIZATION USING BIO-INSPIRED ALGORITHM R. S. S. Prasanth1, K. Hans Raj1 1 Dayalbagh Educational Institute, Dayalbagh, Agra 282110, INDIA
[email protected],
[email protected] Abstract: Optimization of any machining process, involves estimation of optimal cutting parameters that affect the total production time that in turn affects production cost and quality of the work piece. In this paper, an effort is made to apply a recent swarm intelligence inspired evolutionary heuristic which is a class of bio-inspired algorithms, called artificial bee colony (ABC) algorithm to optimize CNC turning process. The algorithm is applied to minimize the total production time by estimating the optimal the process parameters such as cutting speed, feed rate and depth of cut, while satisfying the cutting force, cutting power, chip tool interface temperature, and surface roughness constraints. The results are compared with real coded genetic algorithm integrated with Laplace cross over and power mutation operator (RCGA-LXPM), and differential evolution (DE) algorithm. The results suggest that ABC algorithm outperforms the RCGA-LXPM, and DE in terms of quality of the solution. Keywords: Bio-inspired algorithms; CNC turning; Optimization; ABC algorithm
1. Introduction The implicit financial restraint involved in all product manufacturing process activities essentially demand intelligent optimization techniques that can determine optimal parameters. Particularly for CNC manufacturing, due to large capital investments it becomes all the more important to minimize the production time. But the exceedingly complex non linear processes that are associated with many variables, constraints and conditions make manufacturing process optimization even more difficult. Mathematically complex traditional approaches often fail to give optimal results and struck in local optima in solving such nonlinear manufacturing processes [1]. Therefore, researchers are leaning more towards, bio-inspired computing techniques [2] that are flexible and easy to implement than their counterparts. Among such bio-inspired algorithms, such as ant colony optimization (ACO), particle swarm optimization (PSO), bees algorithm (BA) that mimic the behavior of ants, birds, bees etc are also increasingly becoming popular because of their computational efficiency [3], which are based on the swarm intelligence.
Swarm intelligence is as defined by Bonabeau, as “ …any attempt to design algorithms or distributed problem solving devices inspired by the collective behavior of social insect colonies and other animal societies …” [4]. One among such recent swarm intelligence inspired evolutionary algorithms is artificial bee colony (ABC) algorithm [5] which is attracting many researchers [6, 7]. CNC turning economics essentially depends upon minimization of production cost per piece, minimizing production time and maximizing production rate, while meeting several dynamic machining constraints and environmental boundary conditions. At the same time several machining constraints must be satisfied, specified by machinists. The traditional selection of process variables which is based on handbooks fail to produce repeatable results and neglect the economics of machining. For CNC machine as they are meant for precision manufacturing the traditional parameter selection mechanism is inappropriate. Therefore, by integrating a robust optimization algorithm into a CNC based manufacturing system we can make it more agile that can address
Volume 15 Issue 2 ©2015 IJAMS
105
R. S. S. Prasanth, K. Hans Raj
dynamic and mutually conflicting interests cost and quality of manufacturing sector. In this paper we applied artificial bee colony algorithm (ABC) to solve a mathematical model of CNC turning operation so as to minimize the production time by selecting optimal cutting parameters such as cutting speed v m/min, feed rate f mm/rev, depth of cut doc while meeting the realistic constraints of force, power, temperature and surface roughness within the specified boundaries. This is paper is further organized as follows. In section two, literature of machining optimization using bio-inspired algorithms is reviewed. In section three the empirical model for optimization of CNC turning is presented. In section four, artificial bee colony algorithm (ABC) is described, followed by its results that are presented and compared with a real coded genetic algorithm and differential evolution (DE) algorithm, in section five. In section six, conclusions are drawn accordingly that are followed by acknowledgment and references.
2. Literature review Although, the initial works on parameters optimization of turning may be dated back to 1907 [8], but here recent literature is reviewed. Researchers experimentally determined optimal steels for alloy steels and developed a mathematical model for minimum surface roughness for plane turning and applied a real coded genetic algorithm (RCGA) for the optimization such process [9, 10]. Authors [11] predicted optimal cutting parameters using differential evolution algorithm for orthogonal cutting. The results showed improved performance of DE over (RCGA). And further they applied artificial bee colony (ABC) algorithm [12] and quantum inspired evolutionary algorithm (QIEA) for process optimization of plane turning and produced highly comparable results and reported that QIEA is significantly better than that of ABC, in terms of rate of convergence for all the sixteen operating conditions considered [13]. But this model is a limited one and it has only boundary conditions and it does not possess any constraints.
Ali Rıza Yıldız [14] applied a novel particle swarm optimization approach i.e., a new hybrid optimization approach (Particle Swarm and Receptor Editing-PSRE) is presented for product design and manufacturing and compared its performance with a hybrid genetic algorithm, scatter search algorithm, genetic algorithm, and integration of simulated annealing and HookeJeeves pattern search. Janakiraman V. & Saravanan R. [15], attempted to concurrently optimize the manufacturing cost of piston and cylinder components by optimizing the operating parameters of the machining processes. The concurrent optimization problem was to minimize total manufacturing cost and quality loss function. Genetic algorithm is followed for optimizing the parameters. And they successfully reduced the manufacturing cost without violating any constraints. Antonio Costa et al. [16] applied hybrid particle swarm optimization technique embedded with simulated annealing for the optimization of multi pass turning economies and compared its performance with other nontraditional optimization techniques such as PSO, SA, ACO etc. Shutong XIE, Yinbiao GUO [17] developed a genetic algorithm for intelligent selection of machining parameters in multi pass turning and compared its performance with PSO, SA, SS etc. Chauhan Pinkey et al. [18] developed a real coded genetic algorithm equipped with Laplace crossover operator and Power mutation operator (LXPM) and applied on CNC turning optimization problem. The performances of both the algorithms are compared with several other optimization algorithms like PSO, Binary GA, SA, NMS, and BSP. The discussion of results shows that both LXPM and DE performed better than Binary GA, NMS, SA, and BSP. However, if LXPM and DE are compared to each other than it can be observed that LXPM performed better than DE. This paper primarily focuses on the implementation and application of artificial bee colony (ABC) algorithm on such CNC turning optimization problem which is described in the following section
Volume 15 Issue 2 ©2015 IJAMS
106
CNC Turning Process Optimization Using Bio-inspired Algorithm
Table 1: Nomenclature and values
3. CNC turning optimization model The empirical model of CNC turning optimization is to minimize the production time. Production time is the sum of machining time, tool changing time, quick return time and workpiece handling time. The model adopted [19, 20] considers cutting parameters such as cutting speed, feed rate and depth of cut besides four constraints i.e., force, power, surface roughness, and temperature. Tu = tm + tcs (tm /T) + tr+th
... (1)
Where cutting time tm (per pass)
Symbo l D L Vmin Vmax fmin fmax R Rmax Pmax
tm =ПDL / 1000V f
... (2)
Taylor’s series tool life equation may be given as V f a1doc a2Ta3 = K
... (3)
Subject to: (i) Boundary conditions: Vmin
≤V≥
Vmax
... (4)
fmin
≤ f≥
fmax
... (5)
docmin ≤ doc ≥ docmax
... (6)
(ii) Constraints: The following four practical constraints i.e., force, power, chip-tool temperature and surface roughness are considered in this model.
Fmax Θmax docmin docmax T tm tcs th tr Tu C0 Ct CT a1, a2, a3, K
Significance Diameter of the workpiece (mm) Length of the workpiece (mm) Minimum cutting speed (m/min) Maximum cutting speed (m/min) Minimum feed rate (mm/rev) Maximum feed rate (mm/rev) Surface roughness (µm) Maximum surface roughness of rough and finish cut (µm) Maximum power of the machine (KW) Maximum cutting force (N) Maximum temperature of tool workpiece interface (0C) Minimum depth of cut (mm) Maximum of depth of cut (mm) Tool life (min) Machining time (min) Tool change time (min/edge) Loading and unloading time (min/pass) Quick return time (min/pass) Total production time (min) Operating cost (RS/piece) Tool cost per cutting edge (RS/edge) Total production cost (RS/edge) Constants used in tool life equation
Value 152 203 30 200 0.254 0.762 50 5 900 550 2.0 5.0
0.5 1.5 0.13 3.5 17.5
0.29; 0.35; 0.25; 193.3
F = 844 V -0.010133 f 0.725 doc 0.75
≤ Fmax
... (7)
4. Artificial bee colony (ABC) algorithm
P = 0.0373 V 0.91 f 0.78 doc 0.75
≤ Pmax
... (8)
θ = 74.96 V 0.4 f 0.2 doc 0.105
≤ θ max
... (9)
R = 14.785 V -1.52 f 1.004 doc 0.25
≤ Rmax
... (10)
Artificial bee colony algorithm is essentially a stochastic search technique that mimics the foraging behavior of honey bees [21]. The ABC algorithm simulates the interactions of three artificial agents namely, employee, onlooker and scout bees. Each of these bees will perform a specific role in order to find the rich food source and to maximize the nectar amount unloaded at hive. The ABC algorithm works in three phases and its general philosophy is explained below. In the initialization phase, the basic control parameters (Number of Food sources, Number of Iterations,
Volume 15 Issue 2 ©2015 IJAMS
107
R. S. S. Prasanth, K. Hans Raj
Modification Rate and the Limit or Scout Production Period) are initialized and the artificial scouts are allowed to randomly generate the population of food sources and assign one employee bee to a randomly generated food source. In the employee bee phase, the employee bee searches for the better neighborhood food sources. Once the neighborhood food source is located, the employee bee applies the greedy selection strategy to find the best of its known two food sources and shares this information with the onlooker bees waiting at the hive, by dancing on the dancing area. The onlooker bee gathers the information from the dancing pattern, tempo and the duration of the employee bee and adopts the employee bee food source probabilistically. Once the onlooker bee chooses its food source, the onlooker bee agents also searches for the better neighborhood solution and applies the greedy strategy to find the best its two food sources. The food sources, whose solutions were not improved, even after a predefined number of trials, will be rendered to scouts for random search. All the three phases are repeated until a termination condition (number of cycles or the CPU time) is satisfied. The pseudo-code of artificial algorithm [22] is presented below:
bee
[∅ij - is a random number in the range [−1, 1]. k ∈ {1, 2 ,... SN} (SN: Number of solutions in a colony) is randomly chosen index. Although k is determined randomly, it has to be different from i. Rj is a randomly chosen real number in the range [0, 1] and j ∈ {1, 2, ... D} (D: Number of dimensions in a problem). MR, modification rate, is a control parameter.] 7. Apply greedy selection process for the employed bees between the vi and xi 8. Calculate the probability values Pi using (12) for the solutions xi 𝑃𝑖 = 𝐹𝑖𝑡𝑛𝑒𝑠𝑠𝑖 / 𝑆𝑁 𝑁=1 𝐹𝑖𝑡𝑛𝑒𝑠𝑠𝑁 … (12) 9. For each onlooker bee, produce a new solution vi by using (2) in the neighborhood of the solution selected depending on Pi and evaluate it. 10. Apply greedy selection process for the onlooker bees between the vi and xi 11. If Scout Production Period (SPP) is completed, determine the abandoned solutions by using “limit” parameter for the scout, if it exists, replace it with a new randomly produced solution using (13) xji = xjmin + rand (0,1) (xjmax - xjmin) …(13) 12. Memorize the best solution achieved so far 13. cycle = cycle +1 14. UNTIL ( Max Cycle Number or Max CPU time)
colony
1. Initialize the Colony Size (CS), Number of Food Sources/Solutions (SN), Number of dimensions to each solution (D), Modification Rate (MR), SPP (Scout Production Periodlimit) 2. Initialize the population of solutions xi,j where i= 1 .. SN and j= 1 .. D 3. Evaluate the population 4. cycle = 1 5. REPEAT 6. Produce a new solution vi for each employed bee by using (11) and evaluate it vij = xij +∅ij (xij – xkj), if Rj < MR, otherwise xij …….. (11)
Volume 15 Issue 2 ©2015 IJAMS
108
CNC Turning Process Optimization Using Bio-inspired Algorithm
Initialize food sources Evaluate food source quality
Employee bee phase: Produce neighborhood food sources and evaluate
Apply greedy selection process between initial solutions and neighborhood solutions
Onlooker bee phase: Produce neighborhood solutions for the solutions that are selected based on their probability
Apply greedy selection process between initial solutions and neighborhood solutions
Store best Solution
Find the abandoned food sources after the scout production period limit, and if there are any, produce new food sources
N Is termination criteria met?
With the help of the flow chart of ABC algorithm presented in figure 1, the algorithm may be described as follows. In initialization phase, generate random food sources for all the cutting parameters i.e., in this case of turning randomly generate cutting speed, feed rate for different depth of cut values within the specified bounds and evaluate them for their nectar quantity i.e., evaluate the fitness function using (1). In employee bee phase, produce neighborhood sources (vi) of the initial food sources (xi) using equation (11) and evaluate them for their quality i.e., surface roughness. Apply greedy selection strategy between vi and xi. And now, in onlooker phase, select the food sources based on the probability computed using (12) and produce neighborhood food sources vi of selected food sources xi using (11) and evaluate them for their fitness. Again apply greedy selection process between vi and xi, and store the best solution. In scout bee phase, if the scout production period is over, find the abandoned food source (a food source that is unable to improve) if any exits and replace it with new randomly generated food source using (13). Now memorize the final best solutions obtained so far and continue the iterative process until the predetermined requirements are met. Constraint handling: Constraint handling is the key concern in optimizing the manufacturing optimization problems, as the choice of the technique tends to have a definite impact on quality of search technique. For CNC turning optimization, Deb’s feasibility rules [23] are adopted for constraints handling. In this method tournament selection is employed, that is two solutions are compared with each other, according to the following order of preference of feasible solution.
Y Store final best solution
If both solutions are feasible, the one with better objective function value wins. If one solution is feasible and the other unfeasible, the feasible one wins. If both solutions are infeasible, the one with lower constraint violation wins.
Figure1: Flow chart of (ABC) algorithm Volume 15 Issue 2 ©2015 IJAMS
109
R. S. S. Prasanth, K. Hans Raj
Table 2: ABC control parameters Parameter Colony size (CS) Number of food sources (SN) Modification rate (MR) Maximum cycle number (MCN) Scout production limit
Value 40 20 0.8 500 0.25 * MCN
6. Results Optimal cutting parameters such as feed rate, cutting speed for different depth of cut and the corresponding total production time, obtained using ABC is presented in Table 2. The results of ABC are also compared with real coded genetic algorithm (RCGA) equipped with LXPM and differential evolution (DE) algorithm [18] are also presented in Table 2, that indicate that the optimal cutting parameters predicted by ABC along with corresponding minimum production time achieved predicted for different settings of depth cut, is better than that of RCGA with LXPM and DE. Table 3: ABC algorithm results of CNC turning process optimization at different depth of cut values Depth of Cut/ Algorith m 2.0 2.5 3.0 3.5 4.0 4.5 5.0
Cutting speed (V) in m/min
Feed rate (f) mm/rev
Total production time TU in min
150.7987
0.7620
2.54344
129.0802 121.5745 122.6193 123.5359 124.3534 125.0974
0.7620 0.6858 0.5854 0.5104 0.4523 0.4059
2.68014 2.86215 3.06109 3.25924 3.45667 3.6535
Algorith TU at TU at TU at TU TU m d is d is d is at d at d 2.0 2.5 3.0 is is 3.5 4.0 BSP 2.84 2.93 3.11 3.3 3.59 [24] 4 NMS 2.87 2.97 3.15 3.4 3.69 [24] 4 GA [24] 2.85 3.12 3.13 3.4 3.51 6 SA [24] 2.85 2.93 3.15 3.3 3.59 4 PSO 2.78 2.87 3.04 3.2 3.55 [20] 9 DE [18] 2.77 2.87 3.06 3.3 3.57 1 LXPM 2.78 2.87 3.06 3.3 3.55 [18] ABC 2.54 2.68 2.86 3.0 3.25 6
TU at d is 4.5 3.84
TU at d is 5.0 4.1
3.88 4.23 3.96 4.14 3.85 4.12 3.82 4.08 3.83 4.09 3.82 4.08 3.45 3.65
In table 4, the comparison of ABC algorithm on CNC turning process optimization is compared with different traditional and non-traditional algorithms and it depicts the potential of ABC algorithm.
3.8 3.3 2.8
ABC
2.3
LXPM 2.00 2.50 3.00 3.50 4.00 4.50 5.00
Artificial bee colony algorithm is implemented in MATLAB 7.0, and tested on a Laptop machine equipped with Intel Centrino Duo processor, 512 MB RAM, and 150 GB HDD. The following control parameters are adopted for this study.
Table 4: Performance comparison of ABC algorithm with state of the art algorithm on CNC turning process optimization at different depth of cut values
Mean total production time
5. Artificial bee colony (ABC) algorithm parameters
DE
Depth of cut
Figure2: Comparison of ABC algorithm with DE and LXPM algorithms on CNC turning process optimization problem at different depth of cut values.
Volume 15 Issue 2 ©2015 IJAMS
110
CNC Turning Process Optimization Using Bio-inspired Algorithm
artificial systems”. Oxford University Press; (1999)
7. Summary Artificial bee colony (ABC) algorithm is implemented in MATLAB 7.0 environment. It is further applied for CNC turning process optimization problem to predict optimal cutting parameters such as cutting speed, feed rate to achieve minimum total production time at different, depth of cuts. The performance of ABC algorithm on CNC turning optimization problem is compared with real coded genetic algorithm (RCGA) equipped with Laplace crossover and Power mutation (LXPM) and differential evolution (DE) algorithm. The results indicate that ABC estimated optimal process parameters for different depth of cut and achieved minimum production time for CNC turning operation with respect to RCGA with LXPM and DE algorithms. Therefore ABC is a promising heuristic that can be useful to intelligent manufacturing systems.
5.
Karaboga, D., “An idea based on honey bee swarm for numerical optimization. Technical report TR06”, Erciyes University, Engineering Faculty, Computer Engineering Department. (2005)
6.
Karaboga, D., & Basturk, B, “A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (abc) algorithm”. Journal of Global Optimization, 39(3), 459–471. (2007)
7.
Karaboga, D., & Basturk, B, “On the performance of artificial bee colony (abc) algorithm”. Applied Soft Computing, 8(1), 687– 697 (2008)
8.
Taylor F.W., “On the art of cutting metals”, Transactions of the American society of mechanical Engineers, 28, 31--35,(1907)
9.
Srikanth T., Dr Kamala V., “Experimental determination of optimal speeds for alloy steels in plane turning”, Proceedings of the 9th Biennial ASME (2008).
10.
Srikanth T., Dr Kamala V., “A Real Coded Genetic Algorithm for Optimization of Cutting Parameters in Turning”, IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.6, pp189193. (2008)
11.
Prasanth R.S.S., and Hans Raj K., “ Application of Differential evolution algorithm for optimizing orthogonal cutting”, Proceedings of International Conference on Systemics, Cybernetics and Informatics, ICSCI-2011, Hyderabad, pp. 122-126 (2011)
12.
Prasanth R.S.S. and Hans Raj K, “Process optimization of plane turning using artificial bee colony algorithm”, Proceedings of National Systems conference, NSC- 2011, IIT Bhubaneswar, pp 112 – 118 (2011)
8. Acknowledgement We gratefully acknowledge the inspiration and guidance provided by Most Revered Chairman of Advisory Committee on Education, Dayalbagh.
9. References 1.
Rao RV, Pawar PJ, “Grinding process parameter optimization using nontraditional optimization algorithms”. Proc Inst Mech Eng Part B-J Eng Manuf 224(B6):887–898 (2010)
2.
Deb S, Dixit US, “Intelligent machining: computational methods and optimization”. In: Davim JP (ed) Machining: fundamentals and recent advances. Springer, London (2008)
3.
Kennedy J, Eberhart R, “Particle swarm optimization”. In: Proceedings of the IEEE International Conference on Neural Networks (ICNN’95), Perth, Australia, (1995)
4.
Bonabeau E, Dorigo M, Théraulaz G., “Swarm intelligence: from natural to
Volume 15 Issue 2 ©2015 IJAMS
111
R. S. S. Prasanth, K. Hans Raj
13.
14.
15.
16.
Prasanth R.S.S., and Hans Raj K., “Estimation of optimal cutting parameters of plane turning using quantum inspired evolutionary algorithm”, International Journal of Modern Engineering Research, IJMER, Vol 2, Issue 1, Jan – Feb 2012, ISSN:2249-6645, pp 304 – 312 (2012) Ali Rıza Yıldız, “A novel particle swarm optimization approach for product design and manufacturing”, Int J Adv Manuf Technol (2009) 40:617–628 DOI 10.1007/s00170-008-1453-1 (2009) JanakiramanV. and Saravanan R. “Concurrent optimization of machining process parameters and tolerance allocation”, Int J Adv Manuf Technol, 51:357–369 (2010) DOI 10.1007/s00170010-2602-x Antonio Costa & Giovanni Celano & Sergio Fichera, “Optimization of multi-pass turning economies through a hybrid particle swarm optimization technique”, Int J Adv Manuf Technol 53:421–433 (2011), DOI 10.1007/s00170-010-2861-6
17.
Shutong XIE, Yinbiao GUO, “Intelligent Selection of Machining Parameters in Multi-pass Turnings Using a GA-based Approach”, Journal of Computational Information Systems 7:5 (2011) 1714-1721 Available at http://www.Jofcis.com
18.
Chauhan Pinkey, Kusum Deep, Millie Pant, “Optimization of CNC turning process using real coded genetic algorithm and differential evolution”, Transaction on Evolutionary algorithm and Continuous Optimization ISSN: 2229-8711 Online Publication, June 2011
19.
Deep K., Thakur M., “A new mutation operator for real coded genetic algorithms”, Appl. Math. Comput..193, 21--230 (2007)
20.
Deep K., Bansal J.C., “Performance Analysis of Turning Process via Particle Swarm Optimization”, Studies in
Computational Intelligence (SCI). 129, 453-460 (2008) 21.
Karaboga, D., “An idea based on honey bee swarm for numerical optimization,” Tech. Rep. TR06, Computer Engineering Department, Engineering Faculty, Erciyes University, (2005).
22.
Karaboga, D., & Akay, B., “A comparative study of artificial bee colony algorithm”. Applied Mathematics and Computation, 214, 108–132, (2009).
23.
K. Deb, “An efficient constraint Handling Method for Genetic Algorithms”, Comput. Methods Appl. Mech. Engrg, 186, 311-338(2000)
24.
Saravanan, R., Asokan, P., Sachithanandam, M., “Comparative Analysis of Conventional and NonConventional Optimisation Techniques for CNC Turning Process”, Int J Adv Manuf Technol (2001) 17:471–476
Biography R.S.S. Prasanth has received B.E. in Mechanical Engineering from Agra University, M. Tech. in Engineering Systems from Dayalbagh Educational Institute. He is currently perusing Ph. D. in Mechanical Engineering from Dayalbagh Educational Institute. He is a life member of Systems Society of India. His research interests are Swarm based optimization algorithms and Quantum inspired evolutionary algorithms for intelligent manufacturing systems. He has few papers published in conference proceedings and journals. Dr. Kandikonda Hans Raj was born on 31st July 63. He has received B.Sc. Engineering in Mechanical Engineering from Agra University, M. Tech. in Mechanical Engineering from
Volume 15 Issue 2 ©2015 IJAMS
112
CNC Turning Process Optimization Using Bio-inspired Algorithm
I.I.T. Roorkee and Ph.D. from Dayalbagh Educational Institute. Presently he is a Professor in Mechanical Engineering Department at Dayalbagh Educational Institute, Dayalbagh, Agra. He is actively involved in research and teaching since 1988. His research interest includes Intelligent and Agile Manufacturing, Metal Forming Process Modeling and Optimization, Structural Design and Optimization, Finite Element Analysis, Soft Computing Applications in Manufacturing and Quantum Evolutionary Optimization. He has taught several courses to graduate and undergraduate students. He has 126 publications to his credit in journals and conferences. He is a research consultant to ADRDE (DRDO), India. He has successfully completed eleven research projects granted by Government of India. He has been a visiting scientist to CEMEF Laboratory, France, University of Kiel, Germany, Mathematics and Computer Science Department, University of Maryland, MD, Rensselaer Polytechnic Institute, NY, MIT, Boston, U.S.A. He has been awarded
prestigious “Production Engineering Division Medal” for 1999 & 2000 and most coveted “Institution medal” for the year 2001 of Institution of Engineers (India) for his research papers. He has also been awarded “Academic Excellence Award” 2011 by Aerial Delivery Research and Development (ADRDE), DRDO, Agra for his long standing academic and research support to ADRDE. He is a life member of Indian Society for Mechanical Engineers, Indian Society for Technical Education, Indian Society for Continuing Engineering Education, Institution of Engineers (India), Systems Society of India and. He is also a Fellow of Institution of Engineers, India (FIE), Aeronautical Society of India(FAeSI) and International Society of Productivity Enhancement (FISPE). He is an Associate Editor of International Journal of Agile Manufacturing (IJAM) and Chief Editor of International Journal of Advanced Manufacturing Systems (IJAMS).
Volume 15 Issue 2 ©2015 IJAMS
113
