Recent Trends in Artificial Intelligence and Machine Learning Methods Applied to Water Jet Machining

Rehan Khan; Michał Wieczorowski; Ariba Qureshi; Muhammad Ammar; Tauseef Ahmed; Umair Khan

doi:10.1007/978-3-031-56444-4_3

Recent Trends in Artificial Intelligence and Machine Learning Methods Applied to Water Jet Machining

Rehan Khan^*
, Michał Wieczorowski
, Ariba Qureshi
, Muhammad Ammar
, Tauseef Ahmed
, Umair Khan

^*Corresponding author for this work

Interdisciplinary Research Center for Refining and Advanced Chemicals

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Abrasive Water Jet Machining is a revolutionary unconventional cutting technology that has a wide range of applications in the machining of difficult-to-machine materials. Process parameters are critical in determining the efficiency and economics of a high-quality machining process. As a consequence of advancements in sensor technology, machining operations may now be automated, and the massive amounts of data generated can be used to model and monitor the processes using Artificial Intelligence (AI) and Machine Learning (ML) approaches. This paper presents an overview of the current research trends linking the application of AI and ML methods to AWJM processes for enhanced performance metrics, process monitoring and control, and improved variable optimization. Overcoming challenges related to data quality, model interpretability, and system integration will be essential for the successful implementation of AI and ML in the field of water jet machining. The potential future directions in the ever-expanding field of AI and machining processes, particularly AWJM, are also discussed.

Original language	English
Title of host publication	Advances in Manufacturing 4 - Volume 2 - Production Engineering
Subtitle of host publication	Digitalization, Sustainability and Industry Applications
Editors	Justyna Trojanowska, Agnieszka Kujawińska, Ivan Pavlenko, Jozef Husar
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	34-45
Number of pages	12
ISBN (Print)	9783031564468
DOIs	https://doi.org/10.1007/978-3-031-56444-4_3
State	Published - 2024

Publication series

Name	Lecture Notes in Mechanical Engineering
ISSN (Print)	2195-4356
ISSN (Electronic)	2195-4364

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

Abrasive Waterjet
artificial neural network
modelling
process parameters

ASJC Scopus subject areas

Automotive Engineering
Aerospace Engineering
Mechanical Engineering
Fluid Flow and Transfer Processes

Access to Document

10.1007/978-3-031-56444-4_3

Cite this

Khan, R., Wieczorowski, M., Qureshi, A., Ammar, M., Ahmed, T., & Khan, U. (2024). Recent Trends in Artificial Intelligence and Machine Learning Methods Applied to Water Jet Machining. In J. Trojanowska, A. Kujawińska, I. Pavlenko, & J. Husar (Eds.), Advances in Manufacturing 4 - Volume 2 - Production Engineering: Digitalization, Sustainability and Industry Applications (pp. 34-45). (Lecture Notes in Mechanical Engineering). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-56444-4_3

Khan, Rehan ; Wieczorowski, Michał ; Qureshi, Ariba et al. / Recent Trends in Artificial Intelligence and Machine Learning Methods Applied to Water Jet Machining. Advances in Manufacturing 4 - Volume 2 - Production Engineering: Digitalization, Sustainability and Industry Applications. editor / Justyna Trojanowska ; Agnieszka Kujawińska ; Ivan Pavlenko ; Jozef Husar. Springer Science and Business Media Deutschland GmbH, 2024. pp. 34-45 (Lecture Notes in Mechanical Engineering).

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abstract = "Abrasive Water Jet Machining is a revolutionary unconventional cutting technology that has a wide range of applications in the machining of difficult-to-machine materials. Process parameters are critical in determining the efficiency and economics of a high-quality machining process. As a consequence of advancements in sensor technology, machining operations may now be automated, and the massive amounts of data generated can be used to model and monitor the processes using Artificial Intelligence (AI) and Machine Learning (ML) approaches. This paper presents an overview of the current research trends linking the application of AI and ML methods to AWJM processes for enhanced performance metrics, process monitoring and control, and improved variable optimization. Overcoming challenges related to data quality, model interpretability, and system integration will be essential for the successful implementation of AI and ML in the field of water jet machining. The potential future directions in the ever-expanding field of AI and machining processes, particularly AWJM, are also discussed.",

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author = "Rehan Khan and Micha{\l} Wieczorowski and Ariba Qureshi and Muhammad Ammar and Tauseef Ahmed and Umair Khan",

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Khan, R, Wieczorowski, M, Qureshi, A, Ammar, M, Ahmed, T & Khan, U 2024, Recent Trends in Artificial Intelligence and Machine Learning Methods Applied to Water Jet Machining. in J Trojanowska, A Kujawińska, I Pavlenko & J Husar (eds), Advances in Manufacturing 4 - Volume 2 - Production Engineering: Digitalization, Sustainability and Industry Applications. Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH, pp. 34-45. https://doi.org/10.1007/978-3-031-56444-4_3

Recent Trends in Artificial Intelligence and Machine Learning Methods Applied to Water Jet Machining. / Khan, Rehan; Wieczorowski, Michał; Qureshi, Ariba et al.
Advances in Manufacturing 4 - Volume 2 - Production Engineering: Digitalization, Sustainability and Industry Applications. ed. / Justyna Trojanowska; Agnieszka Kujawińska; Ivan Pavlenko; Jozef Husar. Springer Science and Business Media Deutschland GmbH, 2024. p. 34-45 (Lecture Notes in Mechanical Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Khan, Rehan

AU - Wieczorowski, Michał

AU - Qureshi, Ariba

AU - Ammar, Muhammad

AU - Ahmed, Tauseef

AU - Khan, Umair

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - Abrasive Water Jet Machining is a revolutionary unconventional cutting technology that has a wide range of applications in the machining of difficult-to-machine materials. Process parameters are critical in determining the efficiency and economics of a high-quality machining process. As a consequence of advancements in sensor technology, machining operations may now be automated, and the massive amounts of data generated can be used to model and monitor the processes using Artificial Intelligence (AI) and Machine Learning (ML) approaches. This paper presents an overview of the current research trends linking the application of AI and ML methods to AWJM processes for enhanced performance metrics, process monitoring and control, and improved variable optimization. Overcoming challenges related to data quality, model interpretability, and system integration will be essential for the successful implementation of AI and ML in the field of water jet machining. The potential future directions in the ever-expanding field of AI and machining processes, particularly AWJM, are also discussed.

AB - Abrasive Water Jet Machining is a revolutionary unconventional cutting technology that has a wide range of applications in the machining of difficult-to-machine materials. Process parameters are critical in determining the efficiency and economics of a high-quality machining process. As a consequence of advancements in sensor technology, machining operations may now be automated, and the massive amounts of data generated can be used to model and monitor the processes using Artificial Intelligence (AI) and Machine Learning (ML) approaches. This paper presents an overview of the current research trends linking the application of AI and ML methods to AWJM processes for enhanced performance metrics, process monitoring and control, and improved variable optimization. Overcoming challenges related to data quality, model interpretability, and system integration will be essential for the successful implementation of AI and ML in the field of water jet machining. The potential future directions in the ever-expanding field of AI and machining processes, particularly AWJM, are also discussed.

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BT - Advances in Manufacturing 4 - Volume 2 - Production Engineering

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PB - Springer Science and Business Media Deutschland GmbH

ER -

Khan R, Wieczorowski M, Qureshi A, Ammar M, Ahmed T, Khan U. Recent Trends in Artificial Intelligence and Machine Learning Methods Applied to Water Jet Machining. In Trojanowska J, Kujawińska A, Pavlenko I, Husar J, editors, Advances in Manufacturing 4 - Volume 2 - Production Engineering: Digitalization, Sustainability and Industry Applications. Springer Science and Business Media Deutschland GmbH. 2024. p. 34-45. (Lecture Notes in Mechanical Engineering). doi: 10.1007/978-3-031-56444-4_3

Recent Trends in Artificial Intelligence and Machine Learning Methods Applied to Water Jet Machining

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this