A Review on Data-Driven Quality Prediction in the Production Process with Machine Learning for Industry 4.0 Article Swipe
Abdul Quadir
,
Keshav Jha
,
Sabireen Haneef
,
Arun Kumar Sivaraman
,
Kong Fah Tee
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/pr10101966
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/pr10101966
The quality-control process in manufacturing must ensure the product is free of defects and performs according to the customer’s expectations. Maintaining the quality of a firm’s products at the highest level is very important for keeping an edge over the competition. To maintain and enhance the quality of their products, manufacturers invest a lot of resources in quality control and quality assurance. During the assembly line, parts will arrive at a constant interval for assembly. The quality criteria must first be met before the parts are sent to the assembly line where the parts and subparts are assembled to get the final product. Once the product has been assembled, it is again inspected and tested before it is delivered to the customer. Because manufacturers are mostly focused on visual quality inspection, there can be bottlenecks before and after assembly. The manufacturer may suffer a loss if the assembly line is slowed down by this bottleneck. To improve quality, state-of-the-art sensors are being used to replace visual inspections and machine learning is used to help determine which part will fail. Using machine learning techniques, a review of quality assessment in various production processes is presented, along with a summary of the four industrial revolutions that have occurred in manufacturing, highlighting the need to detect anomalies in assembly lines, the need to detect the features of the assembly line, the use of machine learning algorithms in manufacturing, the research challenges, the computing paradigms, and the use of state-of-the-art sensors in Industry 4.0.
Related Topics
Concepts
Bottleneck
Quality (philosophy)
Quality assurance
Production line
Product (mathematics)
Computer science
Process (computing)
Manufacturing engineering
Visual inspection
Production (economics)
Assembly line
Product line
Control (management)
Industrial engineering
Artificial intelligence
Engineering
Operations management
Mechanical engineering
Embedded system
Operating system
Philosophy
External quality assessment
Epistemology
Mathematics
Economics
Geometry
Macroeconomics
Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.3390/pr10101966
- https://www.mdpi.com/2227-9717/10/10/1966/pdf?version=1666336785
- OA Status
- gold
- Cited By
- 48
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4298145934
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4298145934Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/pr10101966Digital Object Identifier
- Title
-
A Review on Data-Driven Quality Prediction in the Production Process with Machine Learning for Industry 4.0Work title
- Type
-
reviewOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-09-29Full publication date if available
- Authors
-
Abdul Quadir, Keshav Jha, Sabireen Haneef, Arun Kumar Sivaraman, Kong Fah TeeList of authors in order
- Landing page
-
https://doi.org/10.3390/pr10101966Publisher landing page
- PDF URL
-
https://www.mdpi.com/2227-9717/10/10/1966/pdf?version=1666336785Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2227-9717/10/10/1966/pdf?version=1666336785Direct OA link when available
- Concepts
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Bottleneck, Quality (philosophy), Quality assurance, Production line, Product (mathematics), Computer science, Process (computing), Manufacturing engineering, Visual inspection, Production (economics), Assembly line, Product line, Control (management), Industrial engineering, Artificial intelligence, Engineering, Operations management, Mechanical engineering, Embedded system, Operating system, Philosophy, External quality assessment, Epistemology, Mathematics, Economics, Geometry, MacroeconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
48Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 18, 2024: 19, 2023: 11Per-year citation counts (last 5 years)
- References (count)
-
45Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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