AI-Driven Random Forest Model and the Six Sigma Approach for Enhancing Offset Printing Process and Product Quality
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 sta...
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| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/5/2266 |
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| author | Diana Bratić Petar Miljković Denis Jurečić Tvrtko Grabarić |
| author_facet | Diana Bratić Petar Miljković Denis Jurečić Tvrtko Grabarić |
| author_sort | Diana Bratić |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5fc917cad2954d45aa22aaa6cb074fe1 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-5fc917cad2954d45aa22aaa6cb074fe12025-08-20T02:57:40ZengMDPI AGApplied Sciences2076-34172025-02-01155226610.3390/app15052266AI-Driven Random Forest Model and the Six Sigma Approach for Enhancing Offset Printing Process and Product QualityDiana Bratić0Petar Miljković1Denis Jurečić2Tvrtko Grabarić3Faculty of Graphic Arts, University of Zagreb, Getaldićeva 2, 10 000 Zagreb, CroatiaDepartment for Multimedia, University North, Jurja Križanića 31b, 42 000 Varaždin, CroatiaFaculty of Graphic Arts, University of Zagreb, Getaldićeva 2, 10 000 Zagreb, CroatiaFaculty of Graphic Arts, University of Zagreb, Getaldićeva 2, 10 000 Zagreb, CroatiaThe 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.https://www.mdpi.com/2076-3417/15/5/2266offset printingoptimizationqualitySix Sigmaprocess variabilityArtificial Intelligence |
| spellingShingle | Diana Bratić Petar Miljković Denis Jurečić Tvrtko Grabarić AI-Driven Random Forest Model and the Six Sigma Approach for Enhancing Offset Printing Process and Product Quality Applied Sciences offset printing optimization quality Six Sigma process variability Artificial Intelligence |
| title | AI-Driven Random Forest Model and the Six Sigma Approach for Enhancing Offset Printing Process and Product Quality |
| title_full | AI-Driven Random Forest Model and the Six Sigma Approach for Enhancing Offset Printing Process and Product Quality |
| title_fullStr | AI-Driven Random Forest Model and the Six Sigma Approach for Enhancing Offset Printing Process and Product Quality |
| title_full_unstemmed | AI-Driven Random Forest Model and the Six Sigma Approach for Enhancing Offset Printing Process and Product Quality |
| title_short | AI-Driven Random Forest Model and the Six Sigma Approach for Enhancing Offset Printing Process and Product Quality |
| title_sort | ai driven random forest model and the six sigma approach for enhancing offset printing process and product quality |
| topic | offset printing optimization quality Six Sigma process variability Artificial Intelligence |
| url | https://www.mdpi.com/2076-3417/15/5/2266 |
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