@article {10.3844/jmrsp.2023.42.47,
article_type = {journal},
title = {Smart Manufacturing in Mining. Adopting Machine Learning to Improve a Copper Milling Process},
author = {Mateo, Federico Walas and Redchuk, Andrés and Tornillo, Julian Eloy},
volume = {7},
year = {2023},
month = {Apr},
pages = {42-47},
doi = {10.3844/jmrsp.2023.42.47},
url = {https://thescipub.com/abstract/jmrsp.2023.42.47},
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 = {Journal of Mechatronics and Robotics},
publisher = {Science Publications}
}