Issue |
Mechanics & Industry
Volume 25, 2024
Advanced Approaches in Manufacturing Engineering and Technologies Design
|
|
---|---|---|
Article Number | 17 | |
Number of page(s) | 10 | |
DOI | https://doi.org/10.1051/meca/2024012 | |
Published online | 23 May 2024 |
Original Article
Method for holistic optimization of the manufacturing process numerically described as low-dimensional database
1
Dunarea de Jos University of Galati, Domneasca str. 111, 800201 Galati, Romania
2
Rulmenti S.A., Republicii str. 320, 731108, Barlad, Romania
* e-mail: gabriel.frumusanu@ugal.ro
Received:
18
July
2023
Accepted:
5
April
2024
The management of the production processes in an optimal manner involves the usage of knowledge about past jobs as reference for current decisions. During a manufacturing flow in every process step the process engineers could be in situations that request quick decisions based on comparison of different potential manufacturing paths. The Method for Holistic Optimization was developed in order to be used as support for decisions. The method was validated thru different studies. For the mentioned studies there were used artificial and real instances databases. The approach of the optimal management of the manufacturing processes was developed in the current study in order to estimate the consequences of a decision, are used known methods, such as: NN modeling, big data analysis, statistics, etc. In all these cases, the database size plays an essential role in terms of estimation quality. The main purpose of the study is to analyze and validate that the Method for Holistic Optimization is feasible to be used in case a decision-maker uses a reduced database. This can be a significant advantage compared with other methods. The study it is performed using an instance database which was artificially generated in case of a turning process. The obtained results are consistent and promising.
Key words: Decision making / method for holistic optimization / instances database / comparative evaluation / turning process
© C. Chivu et al., Published by EDP Sciences 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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