EXECUTION OF SYSTEMATIC LEAN APPROACH FOR REDUCTION OF DEFECTS IN FOUNDRY DIVISION OF MANUFACTURING

International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) ISSN (P): 2249–6890; ISSN (E): 2249–8001 Vol. 10, Issue 3, Jun 2020, 811-822 © TJPRC Pvt. Ltd.

EXECUTION OF SYSTEMATIC LEAN APPROACH FOR REDUCTION OF DEFECTS IN FOUNDRY DIVISION OF MANUFACTURING INDUSTRY S. NALLUSAMY1, GYAN RANJAN2, JASHIM UDDIN3, KEISHAM DANESH KUMAR4 & KRISHNA GOVIND SWAMY5 1

Professor and Dean, Department of Mechanical Engineering, Dr. M.G.R. Educational and Research Institute, Chennai, Tamil Nadu, India 2,3 4,5

UG Scholar, Department of Mechanical Engineering, Dr. M.G.R. Educational and Research Institute, Chennai, Tamil Nadu, India

ABSTRACT In current global scenario, production demand and customer satisfaction has progressively amplified with the development of new technological process. The main objective of this research are to reduce waste by reducing rejection in manufacturing process and to maintain rejection percentage to meet company standards for specified departments.

The current rejection percentage of the industry was analysed based on department wise and also investigated the numbers of rejection for different products. After identifying the various causes for rejection in the current system,

like Pareto analysis, cause and effect diagram and Response Surface Plot (RSP) were applied to identify the defects and also to minimize the rejection level. Pareto analysis was carried out to identify the major defects and cause and effect diagram was used to identify various methods to reduce the defects. ANOVA statistical tool, which was used for RSP is used for finding out the percentage of defects. From the final results, it was noticed that the rejection rate was reduced to less than 4.95%.

Original Article

various methods are proposed to reduce the rejection level by identifying different types of defects. Different lean tools

KEYWORDS: Lean Tools, Rejection Rate, Foundry, Productivity & ANOVA

Received: Apr 17, 2020; Accepted: May 07, 2020; Published: May 28, 2020; Paper Id.: IJMPERDJUN202073

1. INTRODUCTION In today‘s competitive market, planning productivity is one of the main components of successful industrial organizations. Increase in productivity can reduce the cost of production and increase the output. Productivity depends on various factors like labour, machinery, raw materials, quality, rejection etc. In this research, the application of lean manufacturing and other concepts to the foundry industry which manufactures enclosed drive were addressed. Due to the various constraints in their production process, the company is not able to meet their customer demands at right time. Hence, this work is aimed to increase the productivity by implementing some of the lean concepts for minimizing the rejection level and also to reduce the overall lead time which can satisfy customer demands to certain extent. With over thirty years of experience, Visva pumps (Pvt) Ltd produces wide range of water pumps for the agricultural, commercial and domestic sectors. They are an award winning manufacturer with captive foundry, motor lamination, and winding wire units. When other companies focuses upon global market of demand for customers, Visva groups focuses on quality which can be achieved by reducing rejection rates by using various lean methodologies whereas rework of rejected products leads to loss of companies profit. The company may be a mass producing of products for series of process to come out with final products.

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Pumps parts manufacturing have a series of process which makes quality control important Being an important part in a foundry, controlling measures are to be sent to quality engineer as a report every day to avoid the rejection.

2. LITERATURE REVIEW An article based on sand casting defects with the use of lean techniques in a foundry, the author found 11 casting defects and the risk priority for each defects were found through FMEA sheet [1, 2]. The results after implementation show a significant effect in reducing the sigma level and cost of rejection per unit. The defects occurring in casting process was studied [3, 4]. The author has classified the defects into three types. These three categories are classified based on filling, shape, thermal. Each defect has been identified and remedies have been given for quantifying results to be produced inside the industries. A study on reducing casting defects in a foundry line was proposed [5, 6]. The study shows effective use of DMAIC methodology inside foundry line and found 3 casting defects. The defects were analyzed and improvement methods have been made. The results shows defects have been reduced and increased in productivity. A review on different sand casting defects in foundry industry was given [7, 8]. The article shows 38 different types casting defects and also given the causes for each defects. Hence, the article gives a detail root cause for each defect which will help the quality department. An overview on implementing lean manufacturing in a foundry industry was given [9, 10]. The article shows that lean manufacturing has proved itself in economic stability. DMAIC methodology is used in effective implementation of lean manufacturing. The article shows the finding of casting defects and improvement methods used to reduce the defects. A case study on analysis of casting defects in industrial scenario was given [11, 12]. The case study shows 9 different casting defects. The author has given a remedial measure to overcome the defects which will be useful for productivity improvement and improved quality level. A proposed study on failure mode and effect analysis was given [13, 14]. The study is done in a cylinder head of a manufacturing industry. The article shows how to identify the potential causes and calculate the risk priority number and to eliminate the problems. The result shows that FMEA is process enhancement tool in a manufacturing industry. A research on minimizing the defects in casting using FMEA and DOE for improving quality was proposed [15-17]. An overview on implementing lean manufacturing in a foundry industry is studied and it shows that lean manufacturing has proved itself in economic stability. A case study on analysis of casting defects in industrial scenario was discussed and it shows nine different casting defects. Appropriate remedial measures to overcome the defects were suggested to improve the overall productivity and quality level [18, 19]. A diagnostic study on analysis the casting defects in a foundry was carried out and found defects such as sand drop, blow hole, mismatch and oversize and also suitable remedial measures have been given for each defect. The final result shows that, about 65-77% reduction in rejection for each defect [20, 21]. Based on the above literature, a case study was taken for this research based on a foundry unit in a manufacturing industry.

3. PROBLEM DEFINITION AND METHODOLOGY Productivity is reduced due to occurrence of defects in foundry. These defects occurs in various process of manufacturing.

The rejection percentage is not within the company standards in some places. The higher the components produced, higher the rejection percentage, the companies’ acceptable rejection percentage is 5% which is the rejection limit. Hence in this study, more rejection rate was indentified and includes simple approach in the analysis of rejection rate in the process in the foundry in order to reduce rejection. The methodology flow chart fortuning to the research work is shown in Figure 1 which represents the framework to reduce the defects of castings.

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4. DATA COLLECTION AND ANALYSIS The study represents the minimization of rejection rate in casting division of selected manufacturing industry. The industry is manufacturing the cast iron components where they get rejection in various forms. Hence, it is essential to carry out Suppliers, Inputs, Process, Outputs and Customers (SIPOC) analysis to find out the various processes. HBF 46 RH housing unit was taken for this experiment and the developed SIPOC is shown in Figure 2.

The data was gathered for eight months in production line of foundry division from April 2019 to November 2019. There were different defects found that includes sand drop, sand fusion, core crush, blow holes, cold shut, slag, shrinkage, box lift, short poured and knock off fettling. The data for defects were collected and given in Table 1 for HBF 46 RH housing unit. Pareto analyses were carried out to know about the percentage of defective. From the results, it was observed that 80% of casting defects were found. Figure 3 shows the major defect of blow holes occurred in the manufacturing section. Overall, rejection data were collected and calculated and shown in Table 2. The levels of six sigma were deliberated based on the report of rejection and the results are given in Table 3.

5. CAUSE AND EFFECT DIAGRAM The fishbone or cause and effect diagram for the defect of blow holes in HBF 46 RH housing unit is drawn, as shown in Figure 4. The various major causes are identified based on material, design, methods and machines and their sub causes are also identified such as improper ventilation, gating system, ramming and pouring speed.

5.1 Suggestions From the Figure 4, it was observed that the major cause of design plays an important role in reduction of defects, whereas the necessary design modifications has to be carried out by the department of pattern development in foundry. Based on the pattern department, gating methods should be improved by increasing flow of liquid metal and modifying pattern design by introduction of strip of about 0.5mm which facilitates the flow of gases in pattern and helps in ventilation of gases.

Lab investigation was done to develop control charts on sand properties to verify the level issues and the representations are shown in Figure 5 and Figure 6 for moisture and compatibility respectively.

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Figure 1: Framework to Reduce the Defects of Castings.

Figure 2: SIPOC for Casting Process.

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Month (2019) April May June July August September October November

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Table 1: Rejection Data of Component Number of Defects Fettling / Total Molding Melting Others Defectives 390 6 396 195 2 197 218 81 299 231 74 305 340 70 1 411 219 71 1 291 374 110 490

Total Unit Produced 4896 1222 3564 3318 3476 3294 6180

Figure 3: Pareto Chart.

Table 2: Overall Rejection Data Duration (2019)

Defects

Units

Opportunities

TOP

DPU

DPO

DPMO

April

30212.29

366484

25

9162100

0.0842

0.00329

3297.52

May

32341.20

4723

23

9423629

0.0789

0.00343

3431.92

June

29895.47

471476

21

9900996

0.06341

0.00302

3019.43

July

37604.44

12605208

0.06264

0.00298

August

35697.95

477893

23

10990389

0.07471

0.00324

3247.83

September

37310.39

448912

21

9427152

0.08311

0.00395

3957.75

October Total

3942.85 242391.27

476884

24

11445216 72954690

0.08263

0.003443 0.023352

3442.74 23380.44

600248

DPO DPMO Six Sigma Level

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21

Table 3: Calculation of Six Sigma =242391.27/72954690 1 = 0.003322 2 = DPO x 106 3 = 0.03322x 106= 3322 4 = 4.22 5 6

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690000 308000 66800 6120 320 3.4

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S. Nallusamy, Gyan Ranjan, Jashim Uddin, Keisham Danesh Kumar & Krishna Govind Swamy

Figure 4: Cause and Effect Diagram.

Figure 5: Control Chart for Moisture.

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Figure 6: Control Chart for Compatibility.

5.2 Progression Constraint Effects on the Defects Percentage The progression constraint effects are presented based on significant model terms only. Figure 7 shows the interaction of Clay (A) and Mold hardness (C). It is apparent from the figure that defects percentage tends to decrease with the increase in the clay content and decrease in mold hardness. Figure 8 shows the interaction of moisture content (B) and Mold hardness (C). It is apparent from the figure that defects percentage tends to decrease with the increase in the moisture content and decrease in mold hardness.

Figure 7: RSP for Parameter Outcome of A and C on Defects Percentage of Casting.

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Figure 8: RSP for Parameter Outcome of B and C on Defects Percentage of Casting.

5.3 Confirmation of Developed Model To validate the developed response surface equations, derived from multiple regression analysis, three confirmation experiments are conducted with casting conditions chosen randomly within the ranges for which the equations are derived.

From the observed results, it could be noticed that out of total of three tests, 7 components were produced for each trial and only 1 component were defective for trial 1 and 2 and 2 components defective for trial 3. Response Surface Model (RSM) yielded optimized control factors resulting in superior product quality and stability. A higher product yield is possible because prior to the application of the RSM, the casting defect of the molding process were 9.18% of the LH Hosing castings produced, and, after the application of RSM, the casting defects of the molding process declined to 8.73%. In this model, the appeal (value1) was obtained for the range of optimal parameters, defined in a very close level of predictive value and it was recommended for validation, as given in Table 4 and the optimized results shown in Table 5.

Clay (%) 12.21 10.51 12.25

Table 4: Validation Results % of Defects Mold Moisture (%) Hardness Predicted Exp. 3.26 91.68 14.6 14.28 3.01 89.30 14.1 14.28 3.10 89.59 15.5 28.57

Table 5: Optimum Settings of Various Limitations Limitation Symbol Best Possible Set Clay % 12.21 ~ 12 Moisture % 3.26 ~ 3.3 Mold Hardness No.s 91.68 90

6. RESULTS AND DISCUSSIONS In this study, six sigma DMAIC methodologies are used to minimize the overall rejection rate of the casting with green sand mould. The observed results after the appropriate execution of lean tools are given in Table 5:

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

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After analyzing the experimental results of RSM using ANOVA, it is found that the linear effects of clay content (A), moisture content (B), and mold hardness (C); along with the quadratic effects and the interaction effect of clay content and mold hardness (AxC) moisture content and mold hardness (BxC) have momentous effect on the casting process.



The results of validation experiments show that maximum of 0.32% error is found between the predicted and the experimental results.



Best possible settings were predicted and verified experimentally and also compared with the examined data to validate the fitness of the model and shown in Table 5.



From figure 9 it was observed that, the value of sigma level is 4.23 in existing and enhanced to 4.40 after execution of lean tools.

Figure 9: Comparison of Existing and Proposed Rejection Rate.

7. CONCLUSIONS Major industries play an important role of manufacturing the good quality of products to attain customer satisfaction. The aim of the minimization of rejection rate and lead time was carried out in this case study. From the above analysis, it was observed that the major defects occur on blow holes, core crush and sand drop. The defects such as blow holes, core crush and fettling has been reduced by changing of design pattern for providing proper ventilation, checking of core for damage by the workers through visual inspection and through in house processing respectively. This research article presents the execution of lean approaches like Six Sigma and DMAIC methodology to minimize the rate of rejection in casting unit of manufacturing industry.

After the appropriate lean approach, the following observations were made. 

HBF 46 RH housing unit casting rejection rate was reduced from 9.18% to 8.72%.



By implementing six sigma methodology, the regular performance of green sand casting is authenticated with its efficiency.



Suggestion of training the employees were adopted for entire organization level.



It was proved that, the execution of appropriate lean techniques in the selected industry by reducing the overall

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rejection rate of casting. 

The major role of the defect on HBF 46 RH housing unit has been reduced to 4.95% which is company acceptable standard

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