Faulty Process Detection Using Machine Learning Techniques

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review


These days, a shortage of resources leads to the adaptation of new digital manufacturing systems. These systems are playing a critical role in improving productivity and quality assurance in real-time process monitoring tasks and are being designed to monitor a production process and aim to save resources, energy, and time. This becomes even more vital in drilling processes to be as optimized and accurate as possible since it often comes last in the processes and a tiny error could lead to undesirable drilling part breakage. It could be even more costly in case the drilling part itself is in an inappropriate condition or damaged. Especially, if the part has a long production time and, in the end, the drilling or threading process goes wrong. Therefore, faulty processes must be forecasted sufficiently in advance to prevent damage and further costs. In this study, a set of machine learning algorithms have been used to develop an analysis of industrial manufacturing processes to detect faulty processes with the purpose of tool and machine protection as well as product quality assurance. The results of this study show that machine learning algorithms can detect faulty processes in the production process with high accuracy.

Original languageEnglish
Title of host publicationCongress on Smart Computing Technologies : Proceedings of CSCT 2022
EditorsJagdish Chand Bansal, Harish Sharma, Antorweep Chakravorty
Number of pages13
PublisherSpringer Singapore
Publication date11.07.2023
ISBN (Print)978-981-99-2467-7, 978-981-99-2470-7
ISBN (Electronic)978-981-99-2468-4
Publication statusPublished - 11.07.2023
EventCongress on Smart Computing Technologies, 2022 - Soft Computing Research Society / ONLINE , New Delhi, India
Duration: 11.12.202212.12.2022

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

    Research areas

  • Drill tool breakage, Faulty process detection, Machine learning, Tool condition monitoring
  • Engineering