Discriminant Analysis for Defect Reduction in Plastic Printing Process of Beverage Closures

Article Sidebar

PDF (ภาษาไทย)
Published: Dec 30, 2023
Keywords:
Discriminant analysis Defect Plastic printing process Defect reduction

Main Article Content

Kotchawan Yoothai
Department of Industrial Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang
Kittiwat Sirikasemsuk
1Department of Industrial Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang
Kanogkan Leerojanaprapa
Department of Statistics, School of Science, King Mongkut’s Institute of Technology Ladkrabang

Abstract

This research aimed to create the discriminant analysis equation for a classification group of good and bad products, in order to reduce the defects of the plastic printing process by means of the discriminant analysis. According to the company's case study, there was a problem with the beverage closures getting damaged because of the trademark ink peeling off. It was crucial for the brand of the goods and for attracting consumers to buy the product. A total of 180 sample data sets were collected. There were the following three predicted variables: gas pressure (x1), compressed air pressure (x2), and conveyor speed (x3). The dependent variables were separated into two categories: good components and defects. In this research, the discriminant analysis equation could be determined as gif.latex?\widehat{Y}=15.175+0.089x_{1}+0.171x_{2}+0.004x_{3}, under a 94.4% accuracy rate. The suitable gas pressure, compressed air pressure, and conveyor speed were 82 mbar, 7 psi, and 1000 rpm, respectively. These results were replicated experimentally, which proved that every beverage closure was good.

Article Details

How to Cite
Yoothai, K., Sirikasemsuk, K., & Leerojanaprapa, K. (2023). Discriminant Analysis for Defect Reduction in Plastic Printing Process of Beverage Closures. Journal of Science Ladkrabang, 32(2), 17–37. Retrieved from https://li01.tci-thaijo.org/index.php/science_kmitl/article/view/259756
Issue
Vol. 32 No. 2 (2023): July - December 2023
Section
Research article
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

References

Seethamraju, R. and Marjanovic, O. 2009. Role of process knowledge in business process improvement methodology: a case study. Business Process Management Journal, 15(6), 920-936.

Ajayi, S.O., Oyedele, L.O., Bilal, M., Akinade, O.O., Alaka, H.A., Owolabi, H.A. and Kadiri, K.O. 2015. Waste effectiveness of the construction industry: Understanding the impediments and requisites for improvements. Resources Conservation and Recycling, 102, 101-112.

Leksic, I., Stefanic, N. and Veza, I. 2020. The impact of using different lean manufacturing tools on waste reduction. Advances in Production Engineering and Management, 15(1), 50-55.

Starch, D. 1910. A demonstration of the trial and error method of learning. Psychological Bulletin, 7(1), 20.

Lim, S., Buswell, R., Le, T., Wackrow, R., Austin, S.A., Gibb, A. and Thorpe, T. 2011. Development of a viable concrete printing process. 2011 Proceedings of the 28th ISARC, Seoul, Korea, 665-670.

McKay, R.J. and Campbell, N.A. 1982. Variable selection techniques in discriminant analysis: II. Allocation. British Journal of Mathematical and Statistical Psychology, 35(1), 30-41.

Habbema, J.D.F. and Hermans, J. 1977. Selection of variables in discriminant analysis by F-statistic and error rate. Technometrics, 19(4), 487-493.

Sharma, A. and Paliwal, K.K. 2015. Linear discriminant analysis for the small sample size problem: an overview. International Journal of Machine Learning and Cybernetics, 6, 443-454.

กัลยา วานิชย์บัญชา. 2564. การวิเคราะห์สถิติขั้นสูงด้วย SPSS for Windows. พิมพ์ครั้งที่ 15, ห้างหุ้นส่วนจำกัดสามลดา, กรุงเทพฯ. [Kunlaya Wanidbuncha. 2021. Data analysis SPSS for Windows. 15th ed, Samladda, Bangkok. (in Thai)]

Izenman, A.J. 2013. Modern Multivariate Statistical Techniques. Springer Texts in Statistics. Springer, New York, NY.

Knoke, J.D. 1982. Discriminant analysis with discrete and continuous variables. Biometrics, 38, 191-200.

Brown, J.D. 2001. Point-biserial correlation coefficients. Statistics, 5(3), 12-16.

Visa, S., Ramsay, B., Ralescu, A. L. and Van Der Knaap, E. 2011. Confusion matrix-based feature selection. Maics, 710(1), 120-127.

Strobel, M., Jones, V., Lyons, C.S., Ulsh, M., Kushner, M.J., Dorai, R. and Branch, M.C. 2003. A comparison of corona-treated and flame-treated polypropylene films. Plasmas and Polymers, 8, 61-95.

Farris, S., Pozzoli, S., Biagioni, P., Duó, L., Mancinelli, S. and Piergiovanni, L. 2010. The fundamentals of flame treatment for the surface activation of polyolefin polymers–A review. Polymer, 51(16), 3591-3605.

Lee, T.M., Han, H.S., Kim, B., Kwak, S.W., Noh, J.H. and Kim, I. 2013. Roll offset printing process based on interface separation for fine and smoot patterning. Thin solid films, 548, 566-571.

Abliz, D., Duan, Y., Steuernagel, L., Xie, L., Li, D. and Ziegmann, G. 2013. Curing methods for advanced polymer composites-a review. Polymers and Polymer Composites, 21(6), 341-348.

Shukla, V., Bajpai, M., Singh, D.K., Singh, M. and Shukla, R. 2004. Review of basic chemistry of UV‐curing technology. Pigment & Resin Technology, 33(5), 272-279.

Sutherland, I., Brewis, D.M., Health, R.J. and Sheng, E. 1991. Modification of polypropylene surfaces by flame treatment. Surface and interface analysis, 17(7), 507-510.

Medeiros, E., Brandes, R., Al-Qureshi, H.A. and Recouvreux, D.D. 2017. Effect of Flame Treatment in the Coating Adhesion on Polypropylene Substrate. 24th International Congress of Mechanical Engineering, 200-206.

Mazzola, L., Bemporad, E. and Carassiti, F. 2011. Flame treatment on plastic: A new surface free energy statistical prediction model and characterization of treated surfaces. Applied Surface Science, 257(6), 2148-2158.

Antonogeorgos, G., Panagiotakos, D.B., Priftis, K.N. and Tzonou, A. 2009. Logistic regression and linear discriminant analyses in evaluating factors associated with asthma prevalence among 10-to 12-years-old children: divergence and similarity of the two statistical methods. International journal of pediatrics, 18(8), 500-506.

Worth, A.P. and Cronin, M.T. 2003. The use of discriminant analysis, logistic regression and classification tree analysis in the development of classification models for human health effects. Journal of Molecular Structure: THEOCHEM, 622(2), 97-111.

Press, S.J. and Wilson, S. 1978. Choosing between logistic regression and discriminant analysis. Journal of the American Statistical Association, 73(364), 699-705.

Krzanowski, W.J. 1980. Mixtures of continuous and categorical variables in discriminant analysis. Biometrics, 36, 493-499.

Dattalo, P. 1994. A comparison of discriminant analysis and logistic regression. Journal of Social Service Research, 19(3-4), 121-144.

Efron, B. 1975. The efficiency of logistic regression compared to normal discriminant analysis. Journal of the American Statistical Association, 70(352), 892-898.

Abliz, D., Duan, Y., Steuernagel, L., Xie, L., Li, D. and Ziegmann, G. 2013. Curing methods for advanced polymer composites-a review. Polymers and Polymer Composites, 21(6), 341-348.

Polit, D.F. 2017. Clinical significance in nursing research: A discussion and descriptive analysis. International Journal of Nursing Studies, 73, 17-23.