State-of-the-art review of applications of image processing techniques for tool condition monitoring on conventional machining processes

Danil Yu Pimenov*, Leonardo R.R. da Silva, Ali Ercetin, Oğuzhan Der, Tadeusz Mikolajczyk, Khaled Giasin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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In conventional machining, one of the main tasks is to ensure that the required dimensional accuracy and the desired surface quality of a part or product meet the customer needs. The successful accomplishment of these parameters in milling, turning, milling, drilling, grinding and other conventional machining operations directly depends on the current level of tool wear and cutting edge conditions. One of the proven non-contact methods of tool condition monitoring (TCM) is measuring systems based on image processing technologies that allow assessing the current state of the machined surface and the quantitative indicators of tool wear. This review article discusses image processing for tool monitoring in the conventional machining domain. For the first time, a comprehensive review of the application of image processing techniques for tool condition monitoring in conventional machining processes is provided for both direct and indirect measurement methods. Here we consider both applications of image processing in conventional machining processes, for the analysis of the tool cutting edge and for the control of surface images after machining. It also discusses the predominance, limitations and perspectives on the application of imaging systems as a tool for controlling machining processes. The perspectives and trends in the development of image processing in Industry 4.0, namely artificial intelligence, smart manufacturing, the internet of things and big data, were also elaborated and analysed.

Original languageEnglish
JournalInternational Journal of Advanced Manufacturing Technology
Early online date30 Nov 2023
Publication statusEarly online - 30 Nov 2023


  • Cutting edge wear
  • Image processing
  • Machining
  • Sensor systems
  • Surface roughness
  • Tool condition monitoring (TCM)

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