An Application of Six Sigma for Optimality of Medium Density Fiberboard Production

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Sangtawan Jitsamruay, Ronnason Chinram, Kittisak Poolyarat, Thammarat Panityakul

Abstract

During the production process of MDF, there is a high level of internal bond (IB) variation. This results in the waste of out-of-standard IB values that account for 0.38 % with damage value over 1 million baht/year. The company required products with fewer volatile compounds from formaldehyde adhesives, focusing on reducing the amount of adhesive but still being strong according to IB-specification which will reduce the cost of production by about 20 − 30 million baht/year. The results of wood sampling and IB testing were divided into 6 areas, namely IB1-IB6. It was found that most of the data were symmetrical except for the IB5 data as the area where the most variation occurs. The distributions of the IB1 and IB6 data showed relatively low variability compared to data from other areas. IB1 - IB6 values were normal distribution, expect for IB5. Process capacity in IB2 was relatively high compared to IB from other areas. From the Correlation Matrix and Correlation Map, it was found that the variables that influenced the IB were Press Factor, % Dosing Glue, Heat Circuit1, Primary Circuit Intel and % Mc After Gluing. To conduct the experiment and find the best variable conditions by 25-2 - Factorial Design (Resolution: III). It was found that Glue = 7.4, Heat1 = 234.4, and Press = 6.5 would give IB = 0.88 which was closest to target (0.7). Glue = 7.1, Heat1 = 233.2, and Press = 6.48 would give IB = 1.15 which was the highest value. Results of production conditions at optimum or maximum that can be generalized from Rayleigh Method Dimensional Analysis was found that at the levels of 7.85, 254.28 and 257.70 of Glue, Heat1 and PrimCirIn, the target response (IB) was 0.7. and at the levels of 8.07, 233.35 and 281.60 of Glue, Heat1 and PrimCirIn resulted in a response value (IB) of 1.27.

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References

  1. M. Ahmed, N. Ahmad, An Application of Pareto Analysis and Cause-and-Effect Diagram (CED) for Minimizing Rejection of Raw Materials in Lamp Production Process, Manage. Sci. Eng. 5 (2011), 87-95.
  2. D. Desai, B.N. Prajapati, Competitive Advantage Through Six Sigma at Plastic Injection Molded Parts Manufacturing Unit, Int. J. Lean Six Sigma, 8 (2017), 411–435. https://doi.org/10.1108/ijlss-06-2016-0022.
  3. L.M. Gaikwad, V.K. Sunnapwar, S.N. Teli, A.B. Parab, Application of DMAIC and SPC to Improve Operational Performance of Manufacturing Industry: A Case Study, J. Inst. Eng. India Ser. C. 100 (2017), 229–238. https://doi.org/10.1007/s40032-017-0395-5.
  4. J. Jaynes, X. Ding, H. Xu, W.K. Wong, C.M. Ho, Application of Fractional Factorial Designs to Study Drug Combinations, Stat. Med. 32 (2012), 307–318. https://doi.org/10.1002/sim.5526.
  5. S.N. Khekalei, A.S. Chatpalliwar, N. Thaku, Minimization of Cord Wastages in Belt Industry Using DMAIC. Int. J. Eng. Sci. Technol. 2 (2010), 3687–3694.
  6. W. Minsan, N. Saengngammuang, P. Taninpon, S. Thumronglaohapun, Comparing Methods of Optimization in Solver of Excel 2019 and Whale Optimization Algorithm, UTK J. 15 (2021), 106–120.
  7. D.C. Montgomery, Introduction to Statistical Quality Control, (6th ed.), Wiley, New York, (2008).
  8. R.H. Myers, D.C. Montgomery, C.M. Anderson-Cook, Response Surface Methodology: Process and Product Optimization Using Designed Experiments. (3rd ed.), Wiley, New York, (2009).
  9. M.H. Sajjad, K. Naeem, M. Zubair, Q.M. Usman Jan, S.B. Khattak, M. Omair, R. Nawaz, Waste Reduction of Polypropylene Bag Manufacturing Process Using Six Sigma DMAIC Approach: A Case Study, Cogent Eng. 8 (2021), 1896419. https://doi.org/10.1080/23311916.2021.1896419.
  10. M. Shanmugaraja, M. Nataraj, N. Gunasekaran, Defect control analysis for improving quality and productivity: an innovative Six Sigma case study, Int. J. Qual. Innov. 1 (2011), 259–282. https://doi.org/10.1504/ijqi.2011.040428.
  11. K. Srinivasan, S. Muthu, S.R. Devadasan, C. Sugumaran, Six Sigma Through DMAIC Phases: A Literature Review, Int. J. Product. Qual. Manage. 17 (2016), 236–257. https://doi.org/10.1504/ijpqm.2016.074462.