Improving Energy Performance in Flexographic Printing Process through Lean and AI Techniques: A Case Study

Zaher Abusaq, Sadaf Zahoor, Muhammad Salman Habib, Mudassar Rehman, Jawad Mahmood, Mohammad Kanan and Ray Tahir Mushtaq
Additional contact information
Zaher Abusaq: Jeddah College of Engineering, University of Business and Technology, Jeddah 21448, Saudi Arabia
Sadaf Zahoor: Department of Industrial and Manufacturing Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
Muhammad Salman Habib: Department of Industrial and Manufacturing Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
Mudassar Rehman: Department of Industry Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
Jawad Mahmood: Regulated Software Research Center (RSRC), Dundalk Institute of Technology, A91 K584 Dundalk, Ireland
Mohammad Kanan: Jeddah College of Engineering, University of Business and Technology, Jeddah 21448, Saudi Arabia
Ray Tahir Mushtaq: Department of Industry Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China

Energies, 2023, vol. 16, issue 4, 1-15

Abstract: Flexographic printing is a highly sought-after technique within the realm of packaging and labeling due to its versatility, cost-effectiveness, high speed, high-quality images, and environmentally friendly nature. A major challenge in flexographic printing is the need to optimize energy usage, which requires diligent attention to resolve. This research combines lean principles and machine learning to improve energy efficiency in selected flexographic printing machines; i.e., Miraflex and F&K. By implementing the 5Why root cause analysis and Kaizen, the study found that the idle time was reduced by 30% for the Miraflex machine and the F&K machine, resulting in energy savings of 34.198% and 38.635% per meter, respectively. Additionally, a multi-linear regression model was developed using machine learning and a range of input parameters, such as machine speed, production meter, substrate density, machine idle time, machine working time, and total machine run time, to predict energy consumption and optimize job scheduling. The results of the research exhibit that the model was efficient and accurate, leading to a reduction in energy consumption and costs while maintaining or even improving the quality of the printed output. This approach can also add to reducing the carbon footprint of the manufacturing process and help companies meet sustainability goals.

Keywords: flexographic printing process; energy optimization; lean; multi-linear regression model; machine learning; job scheduling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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