Research Article Open Access

Smart Manufacturing in Mining. Adopting Machine Learning to Improve a Copper Milling Process

Federico Walas Mateo1, Andrés Redchuk2 and Julian Eloy Tornillo1
  • 1 Department of Industrial Engineering, Universidad Nacional de Lomas de Zamora, Ingeniería Industrial, Facultad de Ingeniería, Lomas de Zamora, Buenos Aires, Argentina
  • 2 ETSII, Universidad Rey Juan Carlos, Móstoles, Madrid, Spain

Abstract

Nowadays industries like mining are focused in the need of improving processes towards net zero emissions and accomplishing with united nations' sustainable development goals. This article presents a case at a copper mine where an artificial intelligence solution is adopted to optimize industrial processes. The paper illustrates the way a software solution using a low code platform framework can democratize the use of advanced analytical tools in the industrial sector to improve production processes. The low code approach is complemented by lean startup methodology to adapt the solution to the industrial domain and establish a co-creation environment among software engineers and industrial processes experts. This study pretends to highlight the use of industrial data and the way traditional industries are migrating towards the industry 5.0 paradigm, empowering people at the plant and achieving more environmentally friendly processes by the use of digital solutions.

Journal of Mechatronics and Robotics
Volume 7 No. 1, 2023, 42-47

DOI: https://doi.org/10.3844/jmrsp.2023.42.47

Submitted On: 28 February 2023 Published On: 15 April 2023

How to Cite: Mateo, F. W., Redchuk, A. & Tornillo, J. E. (2023). Smart Manufacturing in Mining. Adopting Machine Learning to Improve a Copper Milling Process. Journal of Mechatronics and Robotics, 7(1), 42-47. https://doi.org/10.3844/jmrsp.2023.42.47

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Keywords

  • Lean Startup Methodology
  • Smart Production
  • Low Code Solution
  • UN SDG
  • Industry 5.0