DOI : 10.17577/IJERTV6IS110230
- Open Access
- Total Downloads : 367
- Authors : Mayank Agrawal , Prof. Ranjit Singh , Rishabh Singhal
- Paper ID : IJERTV6IS110230
- Volume & Issue : Volume 06, Issue 11 (November 2017)
- DOI : http://dx.doi.org/10.17577/IJERTV6IS110230
- Published (First Online): 27-11-2017
- ISSN (Online) : 2278-0181
- Publisher Name : IJERT
- License:
This work is licensed under a Creative Commons Attribution 4.0 International License
Productivity Improvement and Capacity Enhancement of an Automobile Industry: A Case Study
Mayank Agrawal
Lecturer
Department of Mechanical Engineering, Dayalbagh Educational Institute,
Agra, India
Rishabh Singhal
I Semester, B. Tech.,
Prof. Ranjit Singh
Department of Mechanical Engineering, Dayalbagh Educational Institute,
Agra, India
Department of Electrical Engineering Dayalbagh Educational Institute, Agra, India
Abstract: This paper presents the result of a study undertaken with the overarching objective of investigating the impact of productivity improvement approaches in automobile industries. Optimization of robotic arm/manipulators path in focused areas helps in eliminating non value added activities and results increase in productivity. Substantial reduction in material handling and operator manual work, reduction in manpower by using MOST (Maynard operation sequence technique) is obtained in detail.
The tool Kaizen (continuous improvement) is applied as a way to progress towards lean manufacturing and as a formula to lead the activities of improvement. This work is mainly concentrated to find bottle neck areas in weld, paint and assembly shop of automobile industry under consideration. The main objective is to analyze delay time and line stoppages, also to minimize down time and develop several strategies to eliminate waste on shop floor. In this paper the TSP (Travelling salesman problem) and other lean manufacturing tools such as automation, Kaizen, MOST (Maynard operational sequence technique) has been used to increase the productivity and capacity of a shop to achieve desired market demand. Results obtained are quite satisfactory in terms of improvement in market share, productivity and reducing the waiting time of vehicles in market.
Keywords: Travelling salesman problem (TSP), Robotic arms/Manipulator, Productivity, MOST (Maynard operational sequence technique)
-
INTRODUCTION
Automobile industry engaged in manufacturing of four wheelers vehicle in India is facing many challenges like high level of competition and ever increasing competitive pressure [9]. Increase in demand resulted in the need of business improvement in all aspects of manufacturing, continuous improvement tool are used in all industries engaged in automobile manufacturing. Kaizen is used as a tool for elimination of non-value added activities which lead to the improvement in activities with great focus on cost reduction and improvement in quality and productivity [6]. In present day manufacturing there is a strong need of continuous improvement tools to enhance the productivity.
This study has an objective of investigating bottleneck areas by suggesting continuous improvement and further optimizing welding paths for the robot engaged in welding shop for spot welding operations in the four wheeler industries. Travelling salesman problem (TSP) is used for finding the optimal welding paths for the robotic arm/manipulators and suggested most suitable feasible paths without interferences.
Robotic arm/manipulators path optimization is a technique which finds its applications in several areas. One of the areas is the welding shop, where the robotic arm or a manipulator carryout the spot welding process. In spot welding processes, the manipulator/robotic arm [1] as shown in figure 1, is required to weld a predefined number of spots. The manipulator/robotic arm, is programmed in such a way that it welds at a spot and then moves to the next spot until all the spots are welded. However, while programming the manipulator, no logic was implemented to ensure that the manipulator covered all the spots using the shortest path possible and therefore in minimum time. This led to time wastage, as the manipulator/robotic arm did not follow the shortest feasible path but followed a different path to cover the required number of spots.
Fig.1: Robotic Arm or Manipulator (Make Fanuc)
As shown in figure 4(a) (on page no.6) the path followed by the manipulator/robotic arm to weld all the spots is repeated using past approach. The shortcomings of this approach were soon realized and a time efficient method needed to be developed for minimum wastage of time during the spot welding process. This necessitated the fact that the path traversed by the manipulator/robotic arm should be the one with minimum distance and therefore minimum time. Travelling Salesman Problem (TSP) principle is used to find a path connecting all the spots (or the dots) such that the path is the one with minimum distance [5].
Now a days companies are facing problems due to increased demand, excessive back tracking of material, imbalanced assembly line, huge in-process inventories, and under-utilization of human resource and long waiting times resulting delays in deliveries [2]. The main cause of which is imbalanced assembly line. Line balancing is essential to survive in industries hence it is essential to balance line and enhance productivity by using MOST as a tool for the success of company survival in this competitive business era.
The Maynard Operation Sequence Technique (MOST) is a high-level predetermined motion time system (PMTS). It is a work measurement technique that concentrates on the movement of objects. It is used to analyze work and to determine the normal time that it would take to perform a particular process /operation[10].
The basic version of MOST is referred to as Basic MOST.
-
The focus of Basic MOST is on work activity involve the movement of objects. The majority of industrial manual work does involve moving objects (e.g., parts, tools) from one location to another in the workplace.
-
Basic MOST uses motion aggregates (collections of basic motion elements) that are concerned with moving things. The motion aggregates are called activity sequence models in Basic MOST.
-
There are three activity sequence models in Basic MOST, each of which consists of a standard sequence of actions:
-
General move: This sequence model is used when an object is moved freely through space from one location to the next (e.g., picking something up from the floor and placing it on a table).
-
Controlled move: This sequence model is used when an object is moved while it remains in contact with a surface (e.g., sliding the object along the surface) or the object is attached to some other object during its movement (e.g., moving a lever on a machine).
-
Tool use: This sequence model applies to the use of a hand tool (e.g., a hammer or screwdriver).
-
-
-
LITERATURE REVIEW
D. Rajenthirakumar and P. R. Thyla in Quality and Productivity Improvement in Automotive Component Manufacturing Company Using Kaizen showed that the implementation of lean manufacturing strategy allows strengthening the phase sequence that leads to operational excellence, a continuous improvement and the elimination of non-value added activities. Thus, the impact of lean practices pays substantially with the operating performance of plants and use of lean tools allows the improvement of results. The tool kaizen is applied as a way to progress toward lean manufacturing and as a formula to lead the activities of improvement. It has been gradually adopted as a potential solution for many organizations, particularly within the automotive and aerospace industrial areas. This work addresses the implementation of the lean tool kaizen in an automotive component manufacturing company with a focus on tube sub-assembly line.
The main objective of the study is to develop numeros approaches to remove waste on the shop floor. This paper describes how the value stream mapping (VSM) and other suite of lean tools such as kaizen can be used to map the current state of a production line and design a desired future state. A noteworthy increase in quality and efficiency is confirmed and the manufacture flow was smoothened by removal of several non-value-added activities.
Preyanan Mahakantee A and Kontorn Chamniprasart in Control of Robot Motion for the Shortest Path from Point to Point Through from Machine Vision showed that the semi-automatic machines that employees use must work continuously. Body and eye fatigue impacts employees performance and causes damage to the final product. The automatic machine works until the task is completed. The amount of time used is re-program depend on how different between task change. If the path or motion of automatic machine is change, the re-program time is likely to be long. If there are changes to tasks, the only time lost is in re-programming the automatic machine. Therefore, this paper is using computer vision to solve the problem of the automatic machine motion when it must be move from point to point. The process will be recorded and the image analyzed to find the points of robot motion. After finding these points, the solution uses the Traveling Salesman Problem to control the robot motion to find the shortest path from point to point.
Saravanan Tanjong Tuan, A. N. M. Karim, H. M. Emrul Kays, A. K. M. N. Amin and M. H. Hasan in their case study Improvement of Workflow and Productivity through Application of Maynard Operation Sequence Technique (MOST), the problems and challenges of an auto company engaged in assembling car rear window assembly are attributable to non-optimal actions with unproductive capacity planning. The whole assembly line agonizes due to the absence of recognized standard time for actions carried out by operatives, the non-value-added activities involved and the inefficient methods such as unplanned aisle and manual screwing, and walking distance, material wastages and imbalance in the material flow. In this study Maynard
Operation Sequence Technique (MOST) is used. Thus, through the process flow and process redesign analysis, workflow and material handling are improved. Therefore, it has been possible to reduce the production cycle time to supply the higher level of demand with shorter takt time maintaining the current level of manpower.
R. Suganthini Rekha, P. Periyasamyb and S. Nallusamy in Manufacturing Enhancement through Reduction of Cycle Time using Different Lean Techniques showed that In recent manufacturing system the most important parameters in production line are work in process, TAKT time and line balancing. In this article lean tools and techniques were implemented to reduce the cycle time. The aim was to enhance the productivity of the water pump pipe by identifying the bottleneck stations and nonvalue added activities. From the initial time study the bottleneck processes were identified and then necessary expanding processes were also identified for the bottleneck process. Afterward the advance actions have been established and applied using different lean tools like value stream mapping, line balancing and 5S. The current state value stream mapping was developed to describe the existing status and to identify various problem areas. 5S was used to implement the steps to reduce the process cycle time and unnecessary movements of man and material. The improvement activities were implemented with required suggested and the future state value stream mapping was developed. From the results it was concluded that the total cycle time was reduced about290.41 seconds and the customer demand has
paths for spot welding operation for the robots are modified by giving new effective paths (most suitable feasible path).
TSP Principle: The Travelling Salesman Problem (TSP) has wide spread application backgrounds and this important theory optimizes value in combination with efficiency [3]. The idea of TSP is to find the shortest tour path between a given number of cities, and each city can be visited only one time to achieve maximum efficiency in terms of distance, time and cost of trip. The feasibility of this solution TSP is (n-1)! /2, when n is the number of cities. If the number of cities increases, the feasibility of the solution increases as well formulating this problem requires the introduction of a decision variable which is given a value of 1.
If the salesman goes from i to j otherwise = 0, this is expressed mathematically by
= 1 = 1,2,
=1
(Because it is assumed that the salesman visits every city once)
= 1 = 1,2,
=1
(Because every city must be visited)
Where the objective function is:
been increased about 760 units.
=
Productivity Enhancement by Implementing Lean Tools and Techniques in an Automotive Industry by B. Suresh Kumar and S. Syath Abuthakeer describes the productivity enhancement by setup time reduction in a fagor press
involved in the machining of evaporator plates. The well
=1 =1
=
=
And constraint is:
known Single Minute Exchange of Die technique (SMED) was applied in this study. SMED is one of the many lean manufacture methods for minimizing waste in a manufacturing process. It provides a quick and effective
And
=1
= 1
way of altering a manufacturing process from running the current product to running the next product. Using SMED technique, the result shows that tool change over time was reduced from 40 minutes to 12.
-
METHODOLOGY
This study at the specific objective of investigating how useful is the continuous improving tool and optimizing the robots engaged in spot welding operations by using TSP method, in the bottleneck areas of manufacturing assembly lines. The study was carried out with the cooperation and support of an Indian four wheeler car manufacturing company.
In Welding shop robots engaged in spot welding operation for notch back car inner doors are studied and matrix for the path stations are made by observations. Detailed matrixes are used as data and further optimization is done by TSP method. TSP method is applied and optimal
= 1
=1
+ = 1 ,
Step 1: In this problem, total 20 spots are to be welded on inner door of a notch back.
Step 2: The distances between each of the spots are entered in the distance matrix as shown in table1.The distance matrix is a square matrix with 20 rows and 20 columns. The diagonal of the square matrix is a zero line, which results from the fact that the distance of each spot from itself is zero.1st row shows the distances of spot 1 from every other spot. For instance, the 2nd cell in the 1st row shows the distance of spot 1 from spot 2 (3.5cm in the present situation).Similarly, the 2nd row shows the distances of spot 2 from every other spot. If seen in a different way, the 1st column also shows the distances of spot 1 form every other spot. Similarly, the 2nd column shows the distances between
spot 2 and every other spot. The data on the two sides of the zero-diagonal line is a mirror reflection of each other.
Step 3:-The software generates a list of all the possible paths and displays it in the solution summary area. It also shows the distance traversed by the manipulator/robotic arm corresponding to each path [8].
Step 4:-The most feasible /the best near optimal path (row 8th as shown in figure 3) for the manipulator /robotic arm is implemented for spot welding operation as shown in figure 4(b).
Fig. 2: The example path of TSP showed motion of Robotic arm/manipulator path for 10 point to point spot welds
Table 1: Distance diagonal matrix for spots located inner door
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
1
0
3.5
27
55
62
67
69
74
78
16
15
14
60
63
65
32
56
71
128
103
2
3.5
0
25.5
53
59
66
67
71
74
19
17
16
57.5
59.5
60.5
29
56.5
71
128
103
3
27
25.5
0
29
35
40
43
50
56
42.5
40
38
39
42
44
38
73
89
139
89
4
55
53
29
0
7
12
14
25
33
70
68
66
23
27
30
58
96
108
152
79
5
62
59
35
7
0
6
9
20
31
76
74
72
24
28
31
63
102
111
159
79
6
67
66
40
12
6
0
4
19
29
81
79
77
27
30.5
33
69
110
119
162
79
7
69
67
43
14
9
4
0
16
26
85
83
81
25
28
30
70
110
120
162
77
8
74
71
50
25
20
19
16
0
10
90
88
86
18
20
21
67
105
114
155
62
9
78
74
56
33
31
29
26
10
0
92
90
88
19
17
17
66
105
110
150
53
10
16
19
42.5
70
76
81
85
90
92
0
5
9
73
76
77
37
49
64
122
109
11
15
17
40
68
74
79
83
88
90
5
0
4
72
75
77
39
54
69
125
110
12
14
16
38
66
72
77
81
86
88
9
4
0
74
76
79
42
56
72
130
111
13
60
57.5
39
23
24
27
25
18
19
73
72
74
0
5
8
48
88
97
137
57
14
63
59.5
42
27
28
30.5
28
20
17
76
75
76
5
0
3
50
89
96
133
52
15
65
60.5
44
30
31
33
30
21
17
77
77
79
8
3
0
51
8
97
131
49
16
32
29
38
58
63
69
70
67
66
37
39
42
48
50
51
0
41
51
102
74
17
56
56.5
73
96
102
110
110
105
105
49
54
56
88
89
8
41
0
16
79
97
18
71
71
89
108
111
119
120
114
110
64
69
72
97
96
97
51
16
0
55
97
19
128
128
139
152
159
162
162
155
150
122
125
130
137
133
131
102
79
55
0
111
20
103
103
89
79
79
79
77
62
53
109
110
111
57
52
49
74
97
97
111
0
Fig. 3: Screen Shot showing most feasible/best path starting from spot 8 (or dot 8) and completing at spot 20 in green color (S.No.8 and length 492), no collision between robots operation.
-
(b)
Fig 4(a): Robotic arm/manipulator path profile for inner door spot welding (without optimization)
Fig 4(b): The most feasible optimal path (the path in which robots dont have chance of collision during operations)
In order to optimize man movement and to reduce operator fatigue, MOST is applied as following [7]
-
Recording of bottleneck area has been made.
-
The operation/process is broken down into smaller steps/units.
-
The motions are analyzed in each step/unit by using a standard MOST method sequence.
-
Indices to the parameters constituting to the method sequence for each task are assigned.
-
The indices to arrive at a time value for each step/unit are summed up.
Table 2: Bottle neck identification using MOST
LINE NAME:- U/B
PROCESS
member component
,dash fitment on JIG(2341)and JIG(2311)
MODEL:- A
DATE
LINE TACT TIME
14
2017
8
15
Seconds
CODE
L-4
Y
M
D
57.00
No.
OPERATION DETAILS
SEQUENCE MODEL
FREQ
TMU
PART NO
0.8 FAC
PART NAME
SEC
1
walk 5 steps, bend 50%, grasp, place with adjustment on Jig
1
270.00
A
10
B
3
G
1
A
10
B
0
P
3
A
0
216.00
7.78
2
with in reach , readjust (take 10% , does not occure every time)
1
40.00
A
0
B
0
G
0
A 1 B
0
P 3
A
0
32.00
1.15
3
with in reach, tilting the object, walk one step , place with adjustment
1
100.00
A
1
B
0
G 3
A
3
B
0
P
3
A
0
80.00
2.88
4
walk four step , pick a panel dash lower side with R.H.,put in L.H.
1
90.00
A
6
B
0
G
1
A
1
B
0
P
1
A
0
72.00
2.59
5
walk four step, Pick assembly, walk four step , placement with adjustment on JIG(2341)
1
160.00
A
6
B
0
G
1
A
6
B
0
P
3
A
0
128.00
td>
4.61
6
already getting panel dash lower side place it on JIG.
1
40.00
A
0
B
0
G
0
A
1
B
0
P
3
A
0
32.00
1.15
7
walk 2 step, inspect the fittment of component on JIG on GOT.
1
40.00
A
0
B
0
G
0
A
3
B
0
P
0
T
1
A
0
B
0
P
0
A
0
32.00
1.15
8
Walk back two step ,readjust (take 10%
,does not occure every time)
1
7.00
A
3
B
0
G
1
M
3
X
0
I
0
A
0
5.60
0.20
9
walk 2 step, grasp,switch on.
1
50.00
A
3
B
0
G
1
M
1
X
0
I
0
A
0
40.00
1.44
10
walk 5 steps, bend 50%, get bracket suspension upper and panel comp. front apron L.S. from right hand and place it on L.H.
2
180.00
A
6
B
3
G
1
A
1
B
0
P
1
A
0
144.00
5.18
11
get extension appron from L.H.
1
50.00
A
1
B
3
G
1
A
0
B
0
P
0
A
0
40.00
1.44
12
walk 4 step , place all these three component with adjust.on JIG(2311)
3
150.00
A
0
B
0
G
0
A
6
B
0
P
3
A
0
120.00
4.32
13
with in reach, inspect the fittment of component on JIG on GOT.
1
20.00
A
0
B
0
G
0
A
1
B
0
P
0
T
1
A
0
B
0
P
0
A
0
16.00
0.58
14
walk 1 step, grasp,switch on.
1
50.00
A
3
B
0
G
1
M
1
X
0
I
0
A
0
40.00
1.44
Individual Component Times OF SHEET
TMU
SUM
1247.00
SUM
997.60
Sec
SUM
35.91
Table 3: MOST Analysis after improvement
LINE NAME:- U/B
PROCESS
member component
,dash fitment on JIG(2341)and JIG(2311)
MODEL:-A
D
ATE
LINE TACT TIME
14
2017
8
15
Seconds
CODE
L-4
Y
M
D
57.00
No.
OPERATION DETAILS
SEQUENCE MODEL
FREQ
TMU
PART NO
0.8 FAC
PART NAME
SEC
1
walk 5 steps, bend 50%, grasp, pull the member front side(L.H.)
1
150.00
A
10
B
3
G
1
M
1
X
0
I
0
A
0
120.00
4.32
2
walk 5 steps,
1
130.00
A
0
B
0
G
0
A
10
B
0
P
3
A
0
104.00
3.74
3
with in reach, tilting the object, walk one step , place with adjustment
1
80.00
A
1
B
0
G
1
A
3
B
0
P
3
A
0
64.00
2.30
4
walk four step , pick a panel dash lower side with R.H.,put in L.H.
1
90.00
A
6
B
0
G
1
A
1
B
0
P
1
A
0
72.00
2.59
5
walk four step, Pick assembly, walk four step , placement with adjustment on JIG(2341)
1
160.00
A
6
B
0
G
1
A
6
B
0
P
3
A
0
128.00
4.61
6
already getting panel dash lower side place it on JIG.
1
40.00
A
0
B
0
G
0
A
1
B
0
P
3
A
0
32.00
1.15
7
walk 2 step, inspect the fittment of component on JIG on GOT.
1
40.00
A
0
B
0
G
0
A
3
B
0
P
0
T
1
A
0
B
0
P
0
A
0
32.00
1.15
8
Walk back two step , readjust (take 10% , does not occure every time)
1
7.00
A
3
B
0
G
1
M
3
X
0
I
0
A
0
5.60
0.20
9
walk 2 step, grasp,switch on.
1
50.00
A
3
B
0
G
1
M
1
X
0
I
0
A
0
40.00
1.44
10
walk 5 steps, bend 50%, get bracket suspension upper and panel comp. front apron L.S. from right hand and place it on L.H.
2
180.00
A
6
B
3
G
1
A
1
B
0
P
1
A
0
144.00
5.18
11
get extension appron from L.H.
1
50.00
A
1
B
3
G
1
A
0
B
0
P
0
A
0
40.00
1.44
12
walk 4 step , place all these three component with adjust.on JIG(2311)
3
150.00
A
0
B
0
G
0
A
6
B
0
P
3
A
0
120.00
4.32
13
with in reach, inspect the fittment of component on JIG on GOT.
1
20.00
A
0
B
0
G
0
A
1
B
0
P
0
T
1
A
0
B
0
P
0
A
0
16.00
0.58
14
walk 1 step, grasp,switch on.
1
50.00
A
3
B
0
G
1
M
1
X
0
I
0
A
0
40.00
1.44
Individual Component Times OF SHEET
TMU
SUM
1197.00
SUM
957.60
Sec
SUM
34.47
-
-
-
RESULTS AND DISCUSSIONS
The welding paths of robots were studied in detailed and a matrix for the paths are developed. The paths for
optimal and suitable (most feasible path) are selected for implementation in manufacturing of inner door spot welding operation. The developed path details are shown in Figure 3.
Table 4: Operation time saved after robotic arm/manipulators path optimization and application of MOST
After optimization of path
Robot ID
Timing before optimization
Timing after optimization
Time saved
M/B#3
110 Sec
89 Sec
21 Sec
After applying MOST Technique
Member Component and dash
fitment on Jig
35.91 Sec
34.47 Sec
1.44 Sec
The Fig. 4 (b) shows the most feasible optimized improved path [9] of the manipulator/robotic arm (inner door spot welding of notch back car) and it has been observed that significant reduction in tack time as shown in table 4 without any collision between the robots during operations.
By applying of MOST technique man movement in shop floor and operator fatigue has been reduce drastically.
A significant decrease in cycle time and tack time resulted in enhanced productivity. TSP software technique for optimization is applied and made manufacturing
operations more effective by adopting new feasible paths to reduce operation time and enhancing shop productivity by 8%, i.e. 14 vehicles per day in production target.
-
CONCLUSION
The study has highlighted the need of optimizing the welding path of robots. This will further make manufacturing economical and have competitive edge over the various players of manufacturing in the automobile industries.
-
To achieve enhanced productivity, companies need to be able to highlight the bottleneck areas, the key business issues and then apply appropriate tools and optimization techniques.
-
Wasting of resources which are valuable and scarce, missing business opportunity in implementing a range of continuous improvement initiatives which are failing to deliver the desired results.
-
Optimization of robotic arm/manipulators path in focused areas helped in eliminating non value added activities and resulted increase in productivity.
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The fluctuating demand of industries can be meet very
and control department. Experienced in
Mayank Agrawal born on 10th April 1987 received degree of B. Tech. in Mechanical Engineering from Dayalbagh Educational Institute, Agra, India. He received most prestigious award of the institute Founders Medal for best all-rounder among the first degree student of the institute graduating in the year 2010. He has three years of industrial experience in leading automobile industry of India as a assistant manager of production planning
preciously by enhancing productivity.
ACKNOWLEDGEMENT
We gratefully acknowledge Most Revered Prof. P.S.Satsangi, Chairman, Advisory Committee on Education, Dayalbagh Educational Institute, Agra for his constant support and inspiration.
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Shivalingaiah B K., (1995) Labour Productivity through Method study, A project report,Industrial Engineering & Operations research, IIT Bombay, India.
Production: – Scrutinizing and analyzing of key delay zones to enrich forthcoming production.
Planning:-To achieve utmost result with minimum resources concentrating on key result regions to gain optimized resultants.
Control:-Expertization of demanding and controlling of inventory/material /money and man power judiciously. Controlling and balancing the system by vertical and horizontal management.
Now he is dedicating him to endow his time as a Lecturer since Marcp3 in the department of Mechanical Engineering, Technical College, DEI, Agra. His research interest includes workshop technology, Manufacturing Process and Industrial Management.
Prof. Ranjit Singh is an Emeritus professor in Mechanical Engineering Department with more than 43 years of experience in teaching and research. He teaches Manufacturing process, Metal Cutting and tool Design, Advanced manufacturing system, Systems and Design Engineering, Operations Management, Operation planning and control etc. His research interests include intelligent manufacturing,
foundry technology, ergonomics, bio-medical engineering and soft computing applications in manufacturing. He is an eminent researcher and has authored more than 100 research papers. He has completed several R&D projects from Department of science and Technology, New Delhi, India and other funding agencies. In addition, he has co-edited the proceedings of the National Systems Conference 1994. He also co-edited two national seminars, SECTAS 2000 and SASECS-2002. He has chaired several technical sessions at various conferences and workshops in India and Abroad. He has visited number of countries and is doing collaborative research with industries and institutions of abroad. Professor Ranjit Singh has also won several awards/ certificates of merit/appreciation/ honours which include University Gold Medal for First Position in ME in production Engineering from IIT Roorkee, Most coveted P. Banerjee Medal for the best technical paper in Indian Foundry Journal, 2000 and prestigious Ramanna Fellowship for the year 2006 by the Dept. of Science and Technology, Government of India. Prof. K Arumugam National award for innovative work in engineering & technology was awarded in recognition of outstanding contribution in the area of foundry engineering by ISTE in 2012. He is a life member of the Institution of Engineers (India) and the Systems Society of India. Presently he is working on a UGC sponsored project under Emeritus fellowship.