Ijraset Journal For Research in Applied Science and Engineering Technology
Authors: Ashok Kumar C, Sarvesh Chinniah A, Pranesh G, Mohan Kumar RS, Dr. Sreeharan BN
DOI Link: https://doi.org/10.22214/ijraset.2022.46891
Certificate: View Certificate
In the press moulding process, the challenging task is to increase the productivity cycle of the product with zero rejection. Major problems involved in this process are the consumption of time and deduction of temperature for the loading and unloading process of the material in the mould plate. It is recommended to preheat the mould plate to a certain limit so that the temperature needed while processing is obtained in a shorter span of time. To overcome the time consumption problem, the mould plate should be mounted in a shorter interval of time to decrease the raw material loading time process. In this case, it helps to uplift the productiveness and leads us to a zero rejection rate.
I. INTRODUCTION
The productivity of a company depends upon the modern innovation and technology used by the manufacturing unit. When there is a miss appropriation of the techniques and strategies leads to the reaction of quality and quantity of the product in the process of productivity there are several factors influence to ensure the zero reduction of the product. When the technical innovations are properly analysed and the technical know-how is properly used to ensure the productivity of the product. In this study, seven QC tools are critically analysed and the findings helped us to ensure zero rejection with high quality and quantity of the product.
II. LITERATURE REVIEW
It is about quality improvement applied at yarn manufacturing company depending on six sigma methods. Specifically, the DMAIC (Define, Measure, Analyse, Improve and Control), Project management methodology and different tools are used to streamline processes and improve productivity. Defects percent of textile products in yarn manufacturing process are very important in industry perception. It Plays an essential rate for improvement of yield and financial conditions of the company. Actually, Defects percent has direct effect on profit of the product and reduces the quality cost during the Manufacturing of product. By inspection of damages of product at various point in production, where more defects are to happen. A hundred defects opportunities would create in final package of yarn. So it is decided to work and implement DMAIC methodology in winding departments where final Package of yarn is made[1]. It is about the challenges faced by a spinning mill, So they remain unsuccessful, So they need to find the root cause for their failure and rectify it and improve their productivity in better quality. So, they use DMAIC technique used for increase the productivity along with ‘why’ analysis. It is used to find the root cause of the problem the and Pareto chart is used to plot various factors that affects productivity. It focuses on power failure and absenteeism and hence discovered that pay structure and benefits caused absenteeism .so, productivity-based incentives were given to them. Then the power failure issue, there is a leakage of pipeline in generator .so, it is replaced, then the productivity too increased [2]. It is that every enterprise need to earn profit by selling goods and converting the inputs like manpower, raw materials, energy, cost of material into finished goods. In this the sector is DVD production. To increase the quantity and reduce cost of production in DVD manufacturing some input parameter and effects were used. Shortening the production cycle time improves the responsiveness to demands, Cycle time reduction is reducing work in process to increase output. The result is reduction of cycle time from 2.5 Seconds to 2 Seconds, this causes cost reduction [3]. Here we discuss, Bus body manufacturing, which has a major role in automotive manufacturing. So, they meet the requirements of delivery on time, cost and quality in their sector to survive. There are some issues like low labour utilization, material wastage and unorganized work flows. To overcome this, they used Value Stream Mapping which is used as productivity improvement tool which was supported by line balancing techniques. This results in increase in efficiency 13.1% and a reduction the cycle time by 7 days [4].
It is about introduction about the lead time reduction in inventory control, this paper proposes key factors, from the study scope, literature are distinguished into four categories from year (1991 to 2000) and second part from (2000 to 2004), a third part from (2005 to 2008) and final part from (2008 to 2012). Literatures in each were reviewed according key factors. This provides a overview of lead time reduction inventory study field, starting point for further work of researches [5]. It is that, there are two paint assembly lines in an automobile assembly plant, in first assembly line they tried to reduce cycle time of different workstation to complete operations in assembly Study done on each bottle neck operation before paint assembly line and found that workers should carry parts to assembly line from sub assembly station which carries lot of time, this time were reduced by introducing trolley kits before paint assembly line and reduced 163 seconds [6].
Many organizations face their competitors and trying to reduce cost and improve quality and production. Lean manufacturing concepts are used in some industries to reduce waste and improve production. In a manufacturing industry, the material handling system plays a major role regarding production, the research carries a case study. Minimizing the defects are important to any industry. It decides their outputs and profits. employees should be appreciated for their hard work. productivity software and other technologies can solve the gaps in communication. It will be creating a workplace engaged, productive and loyal [7]. It is that , Indian economy faced lot of challenge to Indian industries regarding productivity, cost , quality and delivery . Productivity is a important factor, to survive among competitors, it is a work, done at construction equipment company in which they improve the productivity in which the modifications were done for the assemblies of the hoist and the data were collected, before modification it requires 29.466 hours and after modification it requires 27.646 hours. The overall productivity improvement is 6.17% [8]. It is that, new combined methodology to improve the productivity with the help of work study methods along with Lean Manufacturing Principles and Tools. Lean manufacturing tools were used to eliminating wastes (MUDA), improve performance and quality, this concept gives solutions & concepts to implement Work Study Methods covering technical, engineering and manufacturing. This gives a huge scope to implementation and deployment of very own concepts. By using lean tools with method study 100% positive results are assured [9].
It is that traditional productivity matrices were not helpful to identify the problems and solve them for improving productivity in this concept a systematic methodology Overall Equipment Effectiveness (OEE) and Overall Throughput Effectiveness (OTE) were introduced. These were connected with computer simulation to analyse equipment and manufacturing system productivity. These results make possible the factory level productivity or overall effectiveness by OTE, and OTE to the top when compared with others. Experimental results showed that it is very effective in identifying problems and in increasing the productivity [10]. The integration of production planning and logistics is currently spreading throughout many businesses. A proper definition of logistics is just one of many variables that affect the function and duties of production logistics in a company. A more comprehensive understanding of logistics has emerged as a result of the development of the supply chain concept. The main objective of production logistics can be stated as the development of the implementation and delivery dependability capabilities at the lowest possible logistical and production costs. Production systems that change input quantities into products through time and space fall under this theory. Production and logistics are strongly correlated since distribution networks are at the system's output and logistical supply networks are its input [11]. It states that minimizing the defects in a product is the main condition to reduce the production cost and improve the quality of the product. DMAIC methodology named define, measure, analyse, improve and control is used to minimize the defect rate. Finally, it helps in higher productivity and also reduces the time of reworks [12].
Factory performance remains unpredictable nowadays also productivity measurement and improvement are the main factors because one cannot improve when one cannot measure. So this productivity measure and improvement have been summarized under four divisions (Operations Research- (OR-) based methods, system analysis-based methods, continuous improvement methods and performance metrics-based methods) [13]. Injection moulding is used to increase the production rate and also it can produce the accurate shape of the product at any shape with low cost and faster rate of productivity objective work is to see through the influence of process parameters in injection moulding of PMC cam bush. Now the experiments are conducted according to Taguchi L27 Orthogonal Array and the response data are analysed and optimized by using GRA [14]. This paper discusses the effect of latex degradation and its effect on aquatic organisms it was concluded that when the latex polymer degraded an increase in the formation of microscopic latex particles was observed. When zinc was used to speed up the rate of the curing process it migrated from the latex polymer into the test solutions and a mixture containing oxidised latex oligomers with additive residues formed. The exclusion of light and material thickness had a significant impact on degradation rate than stamped moment and PH [15].
The zero-rejection rate is the one that all the production units are aiming for, so the process has been examined and the defects are identified by the Pareto chart, process flow, cause and effect diagram all were helpful to finding the solutions, and reducing the flaw products. Here sigma rule has been taken which plays an important role in the financial results.
All of these help to find the key factor and to control and improve the overall process towards zero rejection [16][17]. In inventory management practices of various industries and companies were taken for study and we have come to understand the ABC analysis was used for a better improvement of the production process. We came to know that many companies were already using the ABC analysis method and found it very useful either manually or by a resource planning system [18].
Here the objective of the research is to attain zero error production in the industry so that the six sigma DMAIC phases have been adopted. Almost 3.34 defectives per million parts are produced globally. The six sigma phase is implemented with the pareo chart, flow process and fishbone diagram that had been used in the finding of zero error. The critical factors were identified and the improvement towards zero error rejection was implemented. The results from the selected industries show that the rate of rejection came down to 1.2% from 5.3% [19]. Organizations today must consistently and gradually improve their processes, products, and services. Lean six sigma is the only comprehensive strategy that addresses all aspects of an organization's competitiveness and is the only means of achieving total operational excellence. CNC there are many different processes involved in cutting, including machine configuration, blade speed, machine parameter, clamping, cooling, etc. It is incredibly challenging to generate cutting without flaws. The defect may have one or several causes for occurring. Through methodical measures, these causes can be reduced. The process of using different tools and techniques. In this study, cutting faults are analysed, investigated, and corrective actions are identified for a particular industry. On the entire cutting process, a diagnostic analysis was conducted. Cutting goods showed that the five most common flaws in cutting rejections—taper cutting, oversize, undersize, rough surface, and burr—were present. It was discovered that these flaws were commonly appearing in various places. To understand the causes of faults, systematic analyses were done, and appropriate corrective actions were found and put into place [20].
III. METHODOLOGY
IV. EXPERIMENTATION
Table 1: The actual data of the loading and unloading of the material in the plate and their respective time and temperature during loading and unloading process.
|
SETTING TIME |
TEMPERATURE DURING UNLOADING |
TEMPERATURE DURING LOADING |
||||||||||
S.No. |
UNLOADING |
LOADING |
SECONDS |
T1 |
T2 |
T3 |
T4 |
T5 |
T6 |
T7 |
T8 |
T9 |
T10 |
1 |
09:25:33 |
09:35:04 |
571 |
122 |
117 |
129 |
126 |
119 |
50 |
47 |
46 |
47 |
44 |
2 |
09:43:04 |
09:42:52 |
530 |
122 |
132 |
126 |
126 |
121 |
48 |
57 |
52 |
43 |
40 |
3 |
09:41:40 |
09:49:50 |
490 |
133 |
132 |
130 |
134 |
125 |
54 |
51 |
46 |
50 |
45 |
4 |
09:48:43 |
09:57:48 |
545 |
128 |
129 |
130 |
128 |
125 |
44 |
54 |
52 |
45 |
39 |
5 |
09:55:55 |
10:05:04 |
549 |
135 |
133 |
131 |
130 |
136 |
52 |
49 |
48 |
53 |
43 |
6 |
10:04:20 |
10:27:25 |
1385 |
135 |
134 |
131 |
136 |
138 |
44 |
59 |
50 |
41 |
43 |
7 |
10:11:22 |
10:34:02 |
1360 |
137 |
134 |
133 |
130 |
138 |
55 |
61 |
46 |
53 |
50 |
8 |
10:33:01 |
10:43:41 |
640 |
136 |
133 |
130 |
138 |
138 |
47 |
66 |
56 |
45 |
44 |
9 |
10:39:40 |
10:50:30 |
650 |
123 |
122 |
118 |
126 |
138 |
54 |
52 |
52 |
52 |
42 |
10 |
10:49:13 |
10:58:10 |
537 |
133 |
135 |
133 |
132 |
138 |
52 |
62 |
56 |
49 |
46 |
11 |
11:57:01 |
11:08:20 |
679 |
136 |
132 |
136 |
137 |
138 |
52 |
51 |
51 |
52 |
53 |
12 |
11:05:02 |
11:15:07 |
605 |
118 |
116 |
108 |
119 |
138 |
53 |
54 |
56 |
49 |
44 |
13 |
11:14:25 |
11:27:30 |
785 |
121 |
123 |
126 |
124 |
138 |
51 |
49 |
53 |
52 |
48 |
14 |
11:25:09 |
11:53:20 |
611 |
121 |
123 |
120 |
117 |
138 |
43 |
46 |
44 |
45 |
38 |
15 |
11:32:02 |
11:43:30 |
688 |
122 |
128 |
126 |
123 |
138 |
54 |
51 |
52 |
52 |
48 |
16 |
11:41:25 |
11:50:01 |
516 |
125 |
123 |
108 |
118 |
138 |
54 |
56 |
57 |
45 |
40 |
17 |
11:49:10 |
11:58:20 |
550 |
126 |
125 |
125 |
125 |
138 |
57 |
54 |
55 |
56 |
52 |
18 |
11:56:02 |
12:07:05 |
663 |
121 |
121 |
118 |
113 |
138 |
51 |
52 |
58 |
48 |
45 |
19 |
12:04:22 |
12:16:02 |
700 |
120 |
120 |
118 |
119 |
138 |
56 |
60 |
58 |
50 |
48 |
20 |
12:12:20 |
12:24:02 |
702 |
118 |
124 |
114 |
123 |
138 |
52 |
56 |
53 |
50 |
46 |
21 |
12:22:06 |
12:33:08 |
662 |
125 |
120 |
123 |
122 |
138 |
56 |
51 |
48 |
51 |
44 |
22 |
12:20:28 |
12:39:32 |
564 |
132 |
132 |
130 |
129 |
138 |
51 |
54 |
51 |
49 |
47 |
23 |
12:39:05 |
12:50:26 |
681 |
133 |
128 |
128 |
128 |
138 |
56 |
51 |
49 |
50 |
48 |
24 |
02:08:23 |
02:16:58 |
515 |
120 |
119 |
105 |
107 |
138 |
53 |
51 |
54 |
49 |
46 |
25 |
02:08:05 |
02:09:52 |
542 |
125 |
109 |
115 |
116 |
138 |
61 |
58 |
54 |
57 |
50 |
26 |
02:56:22 |
03:06:30 |
608 |
126 |
119 |
120 |
125 |
138 |
56 |
52 |
52 |
46 |
43 |
27 |
03:04:02 |
03:14:01 |
599 |
124 |
124 |
113 |
125 |
138 |
63 |
56 |
54 |
56 |
53 |
28 |
03:13:05 |
03:21:04 |
529 |
132 |
128 |
125 |
129 |
138 |
53 |
64 |
61 |
57 |
48 |
29 |
03:20:02 |
03:29:30 |
568 |
128 |
127 |
125 |
128 |
138 |
53 |
52 |
51 |
56 |
54 |
30 |
03:27:02 |
03:26:01 |
539 |
127 |
130 |
131 |
128 |
138 |
52 |
64 |
62 |
48 |
44 |
31 |
03:34:55 |
03:43:01 |
486 |
130 |
124 |
126 |
127 |
138 |
55 |
47 |
48 |
53 |
49 |
32 |
03:41:50 |
03:49:50 |
480 |
123 |
118 |
121 |
120 |
138 |
52 |
62 |
54 |
50 |
48 |
33 |
03:48:32 |
03:29:01 |
569 |
133 |
128 |
123 |
126 |
138 |
56 |
57 |
52 |
54 |
53 |
34 |
03:55:33 |
04:05:02 |
569 |
132 |
138 |
121 |
132 |
138 |
50 |
63 |
60 |
49 |
45 |
35 |
04:03:40 |
04:12:09 |
509 |
133 |
137 |
133 |
131 |
138 |
53 |
52 |
57 |
52 |
49 |
Table 2: Temperature reduction trend on mould plate over the period of idle time. (Data taken without rubber part ejected from the plate)
OUTPUT TEMPERATURE |
OUTPUT TEMP AVERAGE |
INPUT TEMPERATURE |
|
|||||||||
T1 |
T2 |
T3 |
T4 |
T5 |
T 6 |
T 7 |
T 8 |
T 9 |
T1 0 |
INPUT TEMPERATURE AVERAGE |
REDUCTION PERCENTAGE |
|
122 |
117 |
129 |
126 |
119 |
122 |
5 |
4 |
4 |
4 |
44 |
46 |
62 |
|
|
|
|
|
|
0 |
7 |
6 |
7 |
|
|
|
122 |
132 |
126 |
126 |
121 |
125 |
4 |
5 |
5 |
4 |
40 |
48 |
62 |
|
|
|
|
|
|
8 |
7 |
2 |
3 |
|
|
|
133 |
132 |
130 |
134 |
125 |
131 |
5 |
5 |
4 |
5 |
45 |
49 |
62 |
|
|
|
|
|
|
4 |
1 |
6 |
0 |
|
|
|
128 |
129 |
130 |
128 |
125 |
128 |
4 |
5 |
5 |
4 |
39 |
47 |
63 |
|
|
|
|
|
|
4 |
4 |
2 |
5 |
|
|
|
135 |
133 |
131 |
130 |
136 |
133 |
5 |
4 |
4 |
5 |
43 |
49 |
63 |
|
|
|
|
|
|
2 |
9 |
8 |
3 |
|
|
|
Table 3: Temperature reduction trend on mould plate over the period of idle time. (data taken with rubber part not eject from the plate)
SAMPL E |
DURATIO N |
T1 |
T2 |
T3 |
T4 |
T5 |
AVG |
STATUS |
REDUCTIO N |
AVERAG E |
1 |
0 |
133 |
127 |
139 |
129 |
141 |
133.8 |
100 |
0 |
18.2 |
200 |
116 |
117 |
123 |
120 |
125 |
120.2 |
89 |
11 |
||
300 |
113 |
112 |
116 |
115 |
123 |
115.8 |
86 |
14 |
||
400 |
110 |
108 |
110 |
108 |
117 |
110.6 |
82 |
18 |
||
500 |
105 |
105 |
1055 |
105 |
105 |
105 |
78 |
22 |
||
|
600 |
98 |
96 |
102 |
100 |
102 |
99.6 |
74 |
26 |
|
2 |
0 |
128 |
127 |
136 |
1134 |
134 |
131.8 |
100 |
0 |
18.8 |
200 |
111 |
112 |
117 |
118 |
119 |
115.4 |
87 |
13 |
||
300 |
108 |
107 |
112 |
117 |
116 |
112 |
84 |
16 |
||
400 |
103 |
105 |
107 |
112 |
110 |
107.4 |
81 |
19 |
||
500 |
98 |
103 |
104 |
106 |
108 |
103.8 |
78 |
22 |
||
600 |
966 |
97 |
103 |
105 |
106 |
101.4 |
76 |
24 |
||
3 |
0 |
135 |
130 |
127 |
133 |
132 |
131.4 |
100 |
0 |
20.6 |
200 |
110 |
108 |
114 |
119 |
117 |
113.6 |
86 |
14 |
||
300 |
106 |
107 |
106 |
111 |
109 |
107.8 |
82 |
18 |
||
400 |
103 |
104 |
102 |
108 |
105 |
104.4 |
79 |
21 |
||
500 |
101 |
100 |
94 |
105 |
100 |
100 |
76 |
24 |
||
600 |
100 |
99 |
91 |
103 |
98 |
98.2 |
74 |
26 |
||
4 |
0 |
128 |
129 |
135 |
133 |
135 |
132 |
100 |
0 |
19.6 |
200 |
118 |
118 |
124 |
117 |
128 |
121 |
91 |
9 |
||
300 |
108 |
107 |
112 |
109 |
113 |
109.8 |
83 |
17 |
||
400 |
102 |
104 |
105 |
103 |
106 |
104 |
78 |
22 |
||
500 |
100 |
101 |
101 |
102 |
105 |
101.8 |
77 |
23 |
||
600 |
97 |
95 |
98 |
100 |
96 |
97.2 |
7 |
27 |
||
5 |
0 |
128 |
129 |
127 |
124 |
128 |
127.2 |
100 |
0 |
21 |
200 |
112 |
117 |
116 |
115 |
119 |
115.8 |
91 |
9 |
||
300 |
110 |
110 |
102 |
105 |
111 |
107.6 |
84 |
16 |
||
400 |
98 |
102 |
93 |
94 |
102 |
97.8 |
76 |
24 |
||
500 |
95 |
93 |
91 |
92 |
96 |
93.4 |
73 |
27 |
||
600 |
92 |
91 |
90 |
89 |
91 |
90.6 |
71 |
29 |
V. IMPLEMENTATION OF HEATER
The plate type heater is chosen, because this kind of heater is appropriate for the machine, A fibre plate is fastened to the bottom of the heating plate to protect the machinery parts. For the project, a heater capable of producing 150 degrees Celsius is used. This heater has a total of three electrical circuits and components.
VI. RESULTS AND DISCUSSIONS
Productivity automatically rises as the entire cycle time decreases. Good product quality can be produced with the implementation of the following steps. Reducing the amount of rejected products naturally gives the growth for better products and increases productivity.
According to the preceding statistics, it can be seen that removing the rubber section right before loading lowers the temperature of the mould plate by 60%. The loading temperature is impacted by this lowering temperature, which also has an impact on productivity cycle time. Unloading temperatures are decreased by an average of 60%, from 120°C to 135°C, to 45°C to 50 °C. Pre-heater will therefore be used to control the constant temperature that is applied to the mould plate. Pre-heater temperature is adjusted to remain between 120°C and 130°C, assisting in reducing quenching and long idle times. Utilizing a pre-heater shortens the cycle time for productivity while increasing productivity and preventing defects in products.
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Copyright © 2022 Ashok Kumar C, Sarvesh Chinniah A, Pranesh G, Mohan Kumar RS, Dr. Sreeharan BN. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET46891
Publish Date : 2022-09-26
ISSN : 2321-9653
Publisher Name : IJRASET
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