Usage and Challenges of Machine Vision in Smart Factories
Nissinen, Eeva (2026)
2026
Teknisten tieteiden kandidaattiohjelma - Bachelor's Programme in Engineering Sciences
Tekniikan ja luonnontieteiden tiedekunta - Faculty of Engineering and Natural Sciences
This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
Hyväksymispäivämäärä
2026-04-20
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202604174014
https://urn.fi/URN:NBN:fi:tuni-202604174014
Tiivistelmä
Smart factories and Industry 4.0 concepts increasingly rely on machine vision systems to support automation, intelligent decision-making and flexible manufacturing processes. Machine vision plays a central role in applications such as quality control, predictive maintenance, process optimization and the operation of industrial robots. Despite its growing adoption, the implementation and effective use of machine vision in industrial environments remain associated with significant technical, organizational and security-related challenges.
The aim of this thesis is to provide a comprehensive overview of machine vision technologies in the context of smart factories and to analyze both their practical applications and the main challenges related to their implementation. The thesis begins by presenting the fundamental principles of machine vision, including core methods, hardware components and the needed software for image processing. It then examines the role of machine vision within Industry 4.0, placing emphasis on advanced technologies, such as artificial intelligence (AI), machine learning (ML) and cyber-physical systems (CPS). The research method was a literature review, primarily focused on peer-reviewed scientific articles, conference publications and books.
The key findings of this thesis show that machine vision contributes significantly to improved defect detection, process optimization and production efficiency. However, the research emphasizes that challenges related to variable operating conditions, data quality and quantity, real-time processing and system integration with existing manufacturing systems remain critical.
In conclusion, this thesis shows that while machine vision offers significant potential to improve efficiency, quality and flexibility in smart manufacturing, addressing the identified challenges is essential for achieving reliable and sustainable industrial solutions. The successful implementation of machine vision relies on carefully balancing the capabilities of the system with the production environments organizational and operational needs.
The aim of this thesis is to provide a comprehensive overview of machine vision technologies in the context of smart factories and to analyze both their practical applications and the main challenges related to their implementation. The thesis begins by presenting the fundamental principles of machine vision, including core methods, hardware components and the needed software for image processing. It then examines the role of machine vision within Industry 4.0, placing emphasis on advanced technologies, such as artificial intelligence (AI), machine learning (ML) and cyber-physical systems (CPS). The research method was a literature review, primarily focused on peer-reviewed scientific articles, conference publications and books.
The key findings of this thesis show that machine vision contributes significantly to improved defect detection, process optimization and production efficiency. However, the research emphasizes that challenges related to variable operating conditions, data quality and quantity, real-time processing and system integration with existing manufacturing systems remain critical.
In conclusion, this thesis shows that while machine vision offers significant potential to improve efficiency, quality and flexibility in smart manufacturing, addressing the identified challenges is essential for achieving reliable and sustainable industrial solutions. The successful implementation of machine vision relies on carefully balancing the capabilities of the system with the production environments organizational and operational needs.
Kokoelmat
- Kandidaatintutkielmat [10986]