IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY (IJCSMT )

E-ISSN 2545-5699
P-ISSN 2695-1924
VOL. 9 NO. 5 2023
DOI: https://doi.org/10.56201/ijcsmt.v9.no5.2023.pg150.159

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Application of Machine Learning Techniques to the Reconfiguration of Automated Manufacturing System

Anamelechi Franklyn. C., Amanze Bethran., Agbasonu V.C

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Abstract

The aim of this research is to apply machine learning techniques to the reconfiguration of automated manufacturing system, the objective is to provide a model for fast decision making in automation processes in the manufacturing industry, to use the dataset in product reconfiguration to predict a product, to design an intelligent model that could provide an easy and faster reconfiguration of products in a manufacturing industry. The motivation towards this work is caused by the high rate of delay in the production processes caused by the disturbance, taking proper corrective actions to complete the production orders on time and to minimize the impact of the disturbances. Humans can break down during product production leading to reduction and delay in product production, there is need for an intelligent model that does not require human effort, the model would be able to take decision, automate processes and facilitate production processes. The data which is on the production of semi-conductors in an industry will be analyzed with R and R-Studio platform sourced from UCI machine learning repository. The methodology adopted in this project was SEMMA which stands for Sample Explore Modify Model Access which focuses on the main modeling tasks in the project without venturing into the business understanding and deployment according to oreilly.com. The expected result after the experiment is to develop an intelligent model for the reconfiguration of product in a manufacturing company and also facilitate production and decision making in the company using the dataset on the production of semi-conductor as a use case.

keywords:

Machine Learning, Manufacturing System, Customer, Information System

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References:

Leng, J., Liu, Q., Ye, S., Jing, J., Wang, Y., Zhang, C., ...& Chen, X. (2020).
Digital twin-
driven rapid reconfiguration of the automated manufacturing
system via an
open architecture model.Robotics and Computer-Integrated
Manufacturing, 63,
101895.

Alsafi, Y., &Vyatkin, V. (2010).Ontology-based reconfiguration agent for intelligent
mechatronic systems in flexible manufacturing.Robotics and
Computer-Integrated
Manufacturing, 26(4), 381-391.

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