AI-Driven Random Forest Model and the Six Sigma Approach for Enhancing Offset Printing Process and Product Quality
Source abstract
Abstract
The Six Sigma methodology for quality improvement enabled a high degree of process compliance and enhanced process capability. This research develops a new model for optimizing the offset printing process based on the Six Sigma approach, with the aim of reducing process variability and achieving stable, predictable production outcomes. Special focus was placed on defining Critical Product Characteristics (CPCs) and Critical to Quality (CTQs) points and analysing their impact on process output quality, defined by the sigma level. Based on the research, variability limits of production parameters were defined to ensure consistency and high product quality. The integration of Artificial Intelligence (AI) within the Six Sigma framework allowed for additional automation and model adaptation to changing production conditions. The use of the Random Forest model enabled efficient analysis of critical variability points, prediction of potential deviations, and real-time process adjustment. AI is utilized to improve precision and efficiency in quality management, which further enhances process stability and optimization in line with the dynamic demands of modern production. The proposed model represents an innovative approach that facilitates maintaining stable production results and provides a sustainable foundation for future process optimizations in the printing industry.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Manufacturing Process and Optimization · Additive Manufacturing and 3D Printing Technologies · Industrial Vision Systems and Defect Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Graphic Arts, University of Zagreb, Getaldićeva 2, 10 000 Zagreb, Croatia
Department for Multimedia, University North, Jurja Križanića 31b, 42 000 Varaždin, Croatia
References (73)
Show source-provided references
1. Joglekar, A.M. (2003). Statistical Methods for Six Sigma in R&D and Manufacturing, John Wiley and Sons. [1st ed.].10.1002/0471721212 ↗
2. Goetsch, D.L., and Davis, S.B. (2006). Quality Management: Introduction to Total Quality Management for Production, Processing and Services, Pearson Prentice Hall. [5th ed.].
3. Allen, T.T. (2019). Introduction to Engineering Statistic and Lean Sigma, Springer. [3rd ed.].10.1007/978-1-4471-7420-2 ↗
4. Process Improvement Using Intelligent Six Sigma
5. Abdolrasol, M.G.M., Hussain, S.M.S., Ustun, T.S., Sarker, M.R., Hannan, M.A., Mohamed, R., Ali, J.A., Mekhilef, S., and Milad, A. (2021). Artificial Neural Networks Based Optimization Techniques: A Review. Electronics, 10.10.3390/electronics10212689 ↗
6. Progress and Trends in Industry 4.0 and Lean Six Sigma Integration10.1007/978-3-031-23615-0_9 ↗
7. The Impact of Artificial Intelligence and Blockchain on Six Sigma: A Systematic Literature Review of the Evidence and Implications10.1109/TEM.2023.3324542 ↗
8. Lean Six Sigma and Industry 4.0 implementation framework for operational excellence: A case study10.1080/14783363.2024.2351979 ↗
9. Deep learning for manufacturing sustainability: Models, applications in Industry 4.0 and implications
10. Present, Past and Future of Lean Six Sigma Applications: From Evolution to the Era of Artificial Intelligence10.2174/1872212118666230511111808 ↗
11. How Industry 4.0, artificial intelligence and augmented reality can boost Digital Lean Six Sigma10.1080/14783363.2024.2375299 ↗
12. Bertocci, F., Grandoni, A., Fidanza, M., and Berni, R. (2021). A Guideline for Implementing a Robust Optimization of a Complex Multi-Stage Manufacturing Process. Appl. Sci., 11.10.3390/app11041418 ↗
13. The Application of Machine Learning to Consolidate Critical Success Factors of Lean Six Sigma10.1109/ACCESS.2021.3103931 ↗
14. Vargas, M., Mosquera, R., Fuertes, G., Alfaro, M., and Perez Vergara, I.G. (2024). Process Optimization in a Condiment SME through Improved Lean Six Sigma with a Surface Tension Neural Network. Processes, 12.10.3390/pr12092001 ↗
15. Chakrabarti, S., Sakib, A.A., Singh, R., Banerji, P.K., Poddar, S., Bhattacharya, A., Gangopadhyaya, M., and Chakraborty, S. (2022, January 24–26). Advanced Analytics Tools for Process Improvement: A Case Study in a Brewery. Proceedings of the International Conference on Interdisciplinary Research in Technology and Management, IRTM 2022, Kolkata, India.10.1201/9781003358589 ↗
16. Evolution of modified LSS 4.0 model for sustainable Indian textile industry: A narrative review
17. Hegazi, E.H., Yang, L., and Huang, J. (2021). A Convolutional Neural Network Algorithm for Soil Moisture Prediction from Sentinel-1 SAR Images. Remote Sens., 13.10.3390/rs13244964 ↗
18. On the use of artificial intelligence in predicting the compressive strength of various cardboard packaging10.1002/pts.2783 ↗
19. Revolutionizing Corrugated Board Production and Optimization with Artificial Intelligence10.15376/biores.19.2.2003-2006 ↗
20. Six-Sigma Quality Management of Additive Manufacturing10.1109/JPROC.2020.3034519 ↗
21. Rojek, I., Dostatni, E., Kopowski, J., Macko, M., and Mikołajewski, D. (2022). AI-Based Support System for Monitoring the Quality of a Product within Industry 4.0 Paradigm. Sensors, 22.10.3390/s22218107 ↗
22. Machine learning-driven 3D printing: A review
23. A Review of AI for optimization of 3D Printing of Sustainable Polymers and Composites10.1016/j.jcomc.2024.100513 ↗
24. Ulkir, O., Bayraklılar, M.S., and Kuncan, M. (2024). Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm. Appl. Sci., 14.10.3390/app14052046 ↗
25. Živković, M., Žujović, M., and Milošević, J. (2023). Architectural 3D-Printed Structures Created Using Artificial Intelligence: A Review of Techniques and Applications. Appl. Sci., 13.10.20944/preprints202307.1826.v1 ↗
26. Zgodavova, K., Bober, P., Majstorovic, V., Monkova, K., Santos, G., and Juhaszova, D. (2020). Innovative Methods for Small Mixed Batches Production System Improvement: The Case of a Bakery Machine Manufacturer. Sustainability, 12.10.3390/su12156266 ↗
27. Henry, W., Mensaco, M., and Takada, H. (1989). New Product Development and Testing, Lexington Books, Macmillan Inc.. [1st ed.].
28. Juran, J.M., and Godfrey, A.B. (1998). Juran’s Quality Handbook, McGraw-Hill. [5th ed.].
29. Riedl, R., Neumann, D., and Teubner, J. (1989). Tehnologie des Offsetdruck, VEB Fachbuchverlang. [1st ed.].
30. Walenski, W. (1991). Der Offsetdruck: Eine Einführung in Theorie und Praxis, DuMont. [1st ed.].
31. Walenski, W. (1991). Polygraph-Handbuch: Offserdruck, Polygraph Verlag. [1st ed.].
32. Helbig, T., and Bosse, R. (1993). Druckqualität: Grundlagen der Qualitätsbewertungim Offsetdruck, Polygraph Verlag. [1st ed.].
33. Walenski, W. (1995). Der Rollenoffsetdruck, Fachschriften Verlag. [1st ed.].
34. Teschner, H. (1997). Offsetdruck Technik: Technologien und Wekstoffe in der Druckindustrie, Fachschriften Verlag. [1st ed.].
35. MacPhee, J. (1998). Fundamentals of Lithographic Printing Vol, 1: Mechanics of Printing, PIA GATF Press. [1st ed.].
36. Kipphan, H. (2001). Handbook of Print Media: Technologies and Production Methods, Springer. [1st ed.].10.1007/978-3-540-29900-4 ↗
37. DeJidas, L.P., and Destree, T.M. (2005). Sheetfed Offset Press Operating, PIA GATF Press. [3rd ed.].
38. Breede, H.M. (2006). Handbook of Graphic Arts Equations, PIA/GATF Press. [2nd ed.].
39. Bann, D. (2007). The All New Print Production Handbook, Watson-Guptil Publications. [1st ed.].
40. Johansson, K., Lundberg, P., and Ryberg, R. (2011). A Guide to Graphic Print Production, John Wiley & Sons. [3rd ed.].
41. Zjakić, I. (2007). Upravljanje Kvalitetom Ofsetnog Tiska, Hrvatska sveučilišna naklada. [1st ed.].
42. Ink flow control by multiple models in an offset lithographic printing process10.1016/j.cie.2008.01.019 ↗
43. Leach, R.H., and Pierce, R.J. (2008). The Printing Ink Manual, Springer. [5th ed.].
44. Forced wetting and dewetting of liquids on solid surfaces and their role sin offset printing10.1016/j.colsurfa.2007.08.037 ↗
45. Hird, K.F., and Finley, C.E. (2009). Offset Lithographic Technology, Tinley Park: Goodheart-Willcox Co.. [4th ed.].
46. Evaluation of Ink Optimization Technology in Offset Color Printing10.2352/J.ImagingSci.Technol.2010.54.6.060504 ↗
47. An approach to minimize carbon footprint for an environmental friendly printing by optimizing an offset machine in a printing facility10.1016/j.sbspro.2012.03.316 ↗
48. Methods of Measurement the Quality Metrics in a Printing System
49. Control of the offset printing image quality indices10.1088/1742-6596/858/1/012038 ↗
50. Researching the interaction of different printed materials types with liquids
51. The influence of surface tension on wetting in dampening solution and sustainability of printing: A review10.24867/JGED-2019-1-005 ↗
52. Angelsky, O.V. (2021, January 13–16). Stabilization of imprint’s optical parameters in offset printing. Proceedings of the 15th International Conference on Correlation Optics, Chernivtsi, Ukraine.
53. Quality assessment of offset thermosensitive printing plates10.24867/JGED-2022-2-029 ↗
54. Abdunazarov, M., and Babahanova, H. (2024). Offset Printing Process Parameters for Lightweight Paper, Our Knowledge Publishing. [1st ed.].
55. Multivariate Model for Characterising Physical Dot Gain, Chroma and Lightness in Offset Lithography
56. Kumar, D. (2006). Six Sigma Best Practices: A Guide to Business Process Excellence for Diverse Industries, J. Ross Publishing. [1st ed.].
57. Moore, D.S., McCabe, G.P., and Craig, B.A. (2017). Introduction to the Practice of Statistics, Macmillan Learning. [9th ed.].
58. Increasing newspaper accuracy using Six Sigma methodology
59. An overview of theory and practice on process capability indices for quality assurance10.1016/j.ijpe.2008.11.008 ↗
60. Juran, J.M., and Gryna, F.M. (1993). Quality Planning and Analysis, McGraw-Hill. [1st ed.].
61. Oakland, J.S. (2008). Statistical Process Control, Elsevier. [6th ed.].
62. Process capability indices10.1080/00224065.1986.11978984 ↗
63. Kotz, S., and Lovelace, C.R. (1998). Process Capability Indices in Theory and Practice, Hodder Education Publishers. [1st ed.].
64. Newbold, P., Carlson, W.L., and Thorne, B. (2020). Statistics for Business and Economics, Pearson Prentice Hall. [9th ed.].
65. Kleijnen, J.P.C. (2008). Design and Analysis of Simulation Experiments, Springer. [1st ed.].
66. Hair, J.F., Black, W.C., Babin, B.J., and Anderson, R.E. (2010). Multivariate Data Analysis: A Global Perspective, Pearson. [7th ed.].
67. Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization10.1016/j.gsf.2020.03.007 ↗
68. Machine learning techniques for sustainable industrial process control
69. Machine learning-based prediction and optimization of green hydrogen production technologies from water industries for a circular economy10.1016/j.desal.2023.116992 ↗
70. Process knowledge-based random forest regression for model predictive control on a nonlinear production process with multiple working conditions10.1016/j.aei.2022.101561 ↗
71. Bayesian optimization based random forest and extreme gradient boosting for the pavement density prediction in GPR detection10.1016/j.conbuildmat.2023.131564 ↗
72. Ertel, W., and Black, N.T. (2017). Introduction to Artificial Intelligence (Undergraduate Topics in Computer Science), Springer. [2nd ed.].10.1007/978-3-319-58487-4 ↗
73. Sheppard, C. (2019). Tree-Based Machine Learning Algorithms: Decision Trees, Random Forest and Boosting, CreateSpace Independent Publishing Platform. [2nd ed.].