A Predictive Quality Inspection Framework for the Manufacturing Process in the Context of Industry 4.0

The purpose of this research is to develop an innovative software framework with AI capabilities to predict the quality of automobiles at the end of the production line. By utilizing machine learning techniques, this framework aims to prevent defective vehicles from reaching customers, thus enhancin...

Full description

Saved in:
Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 24; no. 17; p. 5644
Main Authors Rydzi, Stefan, Zahradnikova, Barbora, Sutova, Zuzana, Ravas, Matus, Hornacek, Dominik, Tanuska, Pavol
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 30.08.2024
Subjects
Accuracy
AI framework
Algorithms
Automation
automotive manufacturing
Data analysis
Data collection
Decision making
defect detection
Defects
Efficiency
Lean manufacturing
Machine learning
predictive maintenance
production efficiency
Quality control
Quality management
Quality standards
Research methodology
Six Sigma
data analytics
defect detection
quality control
predictive maintenance
AI framework
automotive manufacturing
machine learning
production efficiency
predictive quality inspection
Online AccessGet full text

Cover

More Information
Summary:The purpose of this research is to develop an innovative software framework with AI capabilities to predict the quality of automobiles at the end of the production line. By utilizing machine learning techniques, this framework aims to prevent defective vehicles from reaching customers, thus enhancing production efficiency, reducing costs, and shortening the manufacturing time of automobiles. The principal results demonstrate that the predictive quality inspection framework significantly improves defect detection and supports personalized road tests. The major conclusions indicate that integrating AI into quality control processes offers a sustainable, long-term solution for continuous improvement in automotive manufacturing, ultimately increasing overall production efficiency. The economic benefit of our solution is significant. Currently, a final test drive takes 10-30 min, depending on the car model. If 200,000-300,000 cars are produced annually and our data prediction of quality saves 10 percent of test drives with test drivers, this represents a minimum annual saving of 200,000 production minutes.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s24175644