Design and Implement Low-Cost Industry 4.0 System using Hybrid Six Sigma Methodology for CNC Manufacturing Process

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10253820" target="_blank" >RIV/61989100:27240/23:10253820 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10314511" target="_blank" >https://ieeexplore.ieee.org/document/10314511</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2023.3331818" target="_blank" >10.1109/ACCESS.2023.3331818</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Design and Implement Low-Cost Industry 4.0 System using Hybrid Six Sigma Methodology for CNC Manufacturing Process

Popis výsledku v původním jazyce

The mechanical components manufacturing industry is carried out through turning processes using CNC machines. Improving productivity and improving product quality in the CNC process is necessary. Automating operations at the CNC process with Industry 4.0 technology at low cost is a direction in the right direction with the development trend of Industry 4.0 technology. Making decisions to improve CNC processes at operations and implementing continuous improvement is an urgent activity. Industry 4.0 technology helps connect data between customer companies, business sectors, and supply chain partners in a more specific and simple way. Commonly connected data between companies helps decision-makers at each company have a common source of analytical data to make more effective production and business development decisions. Industry 4.0 technologies are the subject of much research, but it is important to consider how to integrate them into the manufacturing world in the most efficient and effective way possible. Manufacturers will benefit from this article&apos;s additional insights to solve their problems more effectively. This paper proposes a Hybrid Six Sigma methodology based on Fuzzy TOPSIS and PLS-SEM methods. First, the Fuzzy TOPSIS method helps decision-makers to select problem points for improvement in an unknown environment to implement continuous improvement. Second, the PLS-SEM method evaluates the impact factors of the results of continuous improvement at the mechanical component factory. The Industry 4.0 technology is proposed to be designed, tested, and installed in the CNC manufacturing process. As a result of this study, the product length dimensional error rate decreased from 54.90% per month to zero defects, saving $9593 in annual production costs. Research machines are semi-automatic and cannot increase or digitize their performance because of studies on the application of low-cost Industry 4.0 technology systems to increase the capacity of industrial processes. This study contributes by using Industry 4.0 technology&apos;s low-cost way to improve manufacturing processes using the Hybrid DMAIC method in Six Sigma methodology. This hybrid approach is adaptable and can be used with different business process improvement models. Authors

Název v anglickém jazyce

Design and Implement Low-Cost Industry 4.0 System using Hybrid Six Sigma Methodology for CNC Manufacturing Process

Popis výsledku anglicky

The mechanical components manufacturing industry is carried out through turning processes using CNC machines. Improving productivity and improving product quality in the CNC process is necessary. Automating operations at the CNC process with Industry 4.0 technology at low cost is a direction in the right direction with the development trend of Industry 4.0 technology. Making decisions to improve CNC processes at operations and implementing continuous improvement is an urgent activity. Industry 4.0 technology helps connect data between customer companies, business sectors, and supply chain partners in a more specific and simple way. Commonly connected data between companies helps decision-makers at each company have a common source of analytical data to make more effective production and business development decisions. Industry 4.0 technologies are the subject of much research, but it is important to consider how to integrate them into the manufacturing world in the most efficient and effective way possible. Manufacturers will benefit from this article&apos;s additional insights to solve their problems more effectively. This paper proposes a Hybrid Six Sigma methodology based on Fuzzy TOPSIS and PLS-SEM methods. First, the Fuzzy TOPSIS method helps decision-makers to select problem points for improvement in an unknown environment to implement continuous improvement. Second, the PLS-SEM method evaluates the impact factors of the results of continuous improvement at the mechanical component factory. The Industry 4.0 technology is proposed to be designed, tested, and installed in the CNC manufacturing process. As a result of this study, the product length dimensional error rate decreased from 54.90% per month to zero defects, saving $9593 in annual production costs. Research machines are semi-automatic and cannot increase or digitize their performance because of studies on the application of low-cost Industry 4.0 technology systems to increase the capacity of industrial processes. This study contributes by using Industry 4.0 technology&apos;s low-cost way to improve manufacturing processes using the Hybrid DMAIC method in Six Sigma methodology. This hybrid approach is adaptable and can be used with different business process improvement models. Authors

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

11

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

1

Strana od-do

1

Kód UT WoS článku

001118709500001

EID výsledku v databázi Scopus

2-s2.0-85177042889