Your browser does not allow JavaScript!
JavaScript is necessary for the proper functioning of this website. Please enable JavaScript or use a modern browser.
Open Science Slovenia
Open Science
DiKUL
slv
|
eng
Search
Browse
New in RUL
About RUL
In numbers
Help
Sign in
A comparative study of machine learning regression models for production systems condition monitoring
ID
Jankovič, Denis
(
Author
),
ID
Šimic, Marko
(
Author
),
ID
Herakovič, Niko
(
Author
)
PDF - Presentation file,
Download
(1,88 MB)
MD5: 22D0CF008C1930AF637A9910A6CA8989
URL - Source URL, Visit
https://apem-journal.org/Archives/2024/Abstract-APEM19-1_078-092.html
Image galllery
Abstract
This research investigates the benefits of different Machine Learning (ML) approaches in production systems, with respect to the given use case of considering the forming process and different friction conditions on hydraulic press response in between the phases of the sheet metal bending cycle, i.e. bending, levelling and movement. A framework for enhancing production systems with ML facilitates the transition to smarter processes and enables fast, accurate predictions integrated into decision-making and adaptive control. Comparative ML analysis provides insights into predictive regression models for hydraulic press condition recognition, enhancing process improvement. Our results are supported by performance evaluation metrics of predictive accuracy RMSE, MAE, MSE and R2 for Linear Regression (LR), Decision Trees (DT), Support Vector Machine (SVM), Gaussian Process Regression (GPR) and Neural Network (NN) models. Given the remarkable predictive accuracy of the regression models with R2 values between 0.9483 and 0.9995, it is noteworthy that less complex models exhibit significantly shorter training times, up to 437 times shorter than more complex models. In addition, simpler models have up to 36 times better prediction rates, compared to more complex models. The fundamentals illustrate the trade-offs between model complexity, accuracy and computational training and prediction rate.
Language:
English
Keywords:
hydraulic presses
,
metal forming
,
machine learning
,
linear regression
,
decision trees
,
support vector machines
,
gaussian process regression
,
artificial neural networks
Work type:
Article
Typology:
1.01 - Original Scientific Article
Organization:
FS - Faculty of Mechanical Engineering
Publication status:
Published
Publication version:
Version of Record
Year:
2024
Number of pages:
Str. 78–92
Numbering:
Vol. 19, nr. 1
PID:
20.500.12556/RUL-159626
UDC:
621.7:004.85
ISSN on article:
1854-6250
DOI:
10.14743/apem2024.1.494
COBISS.SI-ID:
201874947
Publication date in RUL:
16.07.2024
Views:
108
Downloads:
21
Metadata:
Cite this work
Plain text
BibTeX
EndNote XML
EndNote/Refer
RIS
ABNT
ACM Ref
AMA
APA
Chicago 17th Author-Date
Harvard
IEEE
ISO 690
MLA
Vancouver
:
Copy citation
Share:
Record is a part of a journal
Title:
Advances in production engineering & management
Shortened title:
Adv produc engineer manag
Publisher:
Fakulteta za strojništvo, Inštitut za proizvodno strojništvo
ISSN:
1854-6250
COBISS.SI-ID:
229859072
Licences
License:
CC BY 4.0, Creative Commons Attribution 4.0 International
Link:
http://creativecommons.org/licenses/by/4.0/
Description:
This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Secondary language
Language:
Slovenian
Keywords:
hidravlične stiskalnice
,
preoblikovanje kovin
,
strojno učenje
,
linearna regresija
,
odločitvena drevesa
,
podporni vektorski stroji
,
regresija Gaussovega procesa
,
umetne nevronske mreže
Projects
Funder:
ARIS - Slovenian Research and Innovation Agency
Project number:
P2-0248
Name:
Inovativni izdelovalni sistemi in procesi
Funder:
ARIS - Slovenian Research and Innovation Agency
Project number:
J2-4470
Name:
Raziskave zanesljivosti in učinkovitosti računanja na robu v pametni tovarni z uporabo tehnologij 5G
Funder:
ARIS - Slovenian Research and Innovation Agency
Funding programme:
Young researchers
Project number:
53512
Funder:
EC - European Commission
Funding programme:
HE
Project number:
101058693
Name:
Sustainable Transition to the Agile and Green Enterprise
Acronym:
STAGE
Similar documents
Similar works from RUL:
Similar works from other Slovenian collections:
Back