Multi-Objective Optimization of Manufacturing Process Using Artificial Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43410%2F24%3A43926323" target="_blank" >RIV/62156489:43410/24:43926323 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.3390/systems12120569" target="_blank" >https://doi.org/10.3390/systems12120569</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/systems12120569" target="_blank" >10.3390/systems12120569</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-Objective Optimization of Manufacturing Process Using Artificial Neural Networks

Popis výsledku v původním jazyce

This paper focuses on the optimization of a critical operation in the furniture manufacturing process, identifying it as a key priority for improvement by applying Systems Theory. The primary objective of this study is to develop a mathematical model for optimizing the detected key process by employing artificial neural networks (ANNs) which mirror adaptive management principles. Three input and three output parameters significantly impacting the effectiveness of this key process have been systematically identified and experimentally measured. It was necessary to perform multi-objective optimization (MOO), which consisted in achieving the minimum values of cost and process time and the maximum value of the quality index through the optimal setting of the input parameters (cutting speed, feed rate, and volume of removed material). The application of ANNs in MOO in this research study is a novelty in this field. The results obtained through application of the ANN method reveal the optimal values of the examined parameters, which represent the best combination of input technical variables leading to the best results in output economic parameters. This multi-objective optimizing solution facilitates enhanced process efficiency. By integrating Systems Theory, Complexity Theory, and adaptive management, this research advances sustainable process improvements by minimizing resource use, reducing waste, and enhancing overall system efficiency.

Název v anglickém jazyce

Multi-Objective Optimization of Manufacturing Process Using Artificial Neural Networks

Popis výsledku anglicky

This paper focuses on the optimization of a critical operation in the furniture manufacturing process, identifying it as a key priority for improvement by applying Systems Theory. The primary objective of this study is to develop a mathematical model for optimizing the detected key process by employing artificial neural networks (ANNs) which mirror adaptive management principles. Three input and three output parameters significantly impacting the effectiveness of this key process have been systematically identified and experimentally measured. It was necessary to perform multi-objective optimization (MOO), which consisted in achieving the minimum values of cost and process time and the maximum value of the quality index through the optimal setting of the input parameters (cutting speed, feed rate, and volume of removed material). The application of ANNs in MOO in this research study is a novelty in this field. The results obtained through application of the ANN method reveal the optimal values of the examined parameters, which represent the best combination of input technical variables leading to the best results in output economic parameters. This multi-objective optimizing solution facilitates enhanced process efficiency. By integrating Systems Theory, Complexity Theory, and adaptive management, this research advances sustainable process improvements by minimizing resource use, reducing waste, and enhancing overall system efficiency.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Systems

ISSN

2079-8954

e-ISSN

2079-8954

Svazek periodika

12

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

25

Strana od-do

569

Kód UT WoS článku

001386939100001

EID výsledku v databázi Scopus

2-s2.0-85213472237