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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Zaher Abusaq & Sadaf Zahoor & Muhammad Salman Habib & Mudassar Rehman & Jawad Mahmood & Mohammad Kanan & Ray Tahir Mushtaq, 2023. "Improving Energy Performance in Flexographic Printing Process through Lean and AI Techniques: A Case Study," Energies, MDPI, vol. 16(4), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1972-:d:1070660
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    References listed on IDEAS

    as
    1. Sun-Youn Shin & Han-Gyun Woo, 2022. "Energy Consumption Forecasting in Korea Using Machine Learning Algorithms," Energies, MDPI, vol. 15(13), pages 1-20, July.
    2. Mohammad Kanan & Muhammad Salman Habib & Anam Shahbaz & Amjad Hussain & Tufail Habib & Hamid Raza & Zaher Abusaq & Ramiz Assaf, 2022. "A Grey-Fuzzy Programming Approach towards Socio-Economic Optimization of Second-Generation Biodiesel Supply Chains," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    3. Nuha Al Habis & Muna Khushaim & Saja M. Nabat Al-Ajrash, 2023. "Energy Harvesting and Storage Devices through Intelligent Flexographic Technology: A Review Article," Energies, MDPI, vol. 16(2), pages 1-16, January.
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    Cited by:

    1. Mohammad Kanan & Ansa Rida Dilshad & Sadaf Zahoor & Amjad Hussain & Muhammad Salman Habib & Amjad Mehmood & Zaher Abusaq & Allam Hamdan & Jihad Asad, 2023. "An Empirical Study of the Implementation of an Integrated Ergo-Green-Lean Framework: A Case Study," Sustainability, MDPI, vol. 15(13), pages 1-24, June.

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