eprintid: 20061
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/02/00/61
datestamp: 2024-06-04 14:19:48
lastmod: 2024-06-04 14:19:48
status_changed: 2024-06-04 14:16:30
type: article
metadata_visibility: show
creators_name: Khan, R.
creators_name: Wieczorowski, M.
creators_name: Qureshi, A.
creators_name: Ammar, M.
creators_name: Ahmed, T.
creators_name: Khan, U.
title: Recent Trends in Artificial Intelligence and Machine Learning Methods Applied to Water Jet Machining
ispublished: pub
keywords: Jets; Machine learning; Machining centers; Process monitoring, Abrasive water jet machining; Abrasive waterjets; Artificial intelligence learning; Cutting technology; Machine learning methods; Machining Process; Modeling; Process parameters; Recent trends; Waterjet machining, Neural networks
note: cited By 0; Conference of 8th International Scientific-Technical Conference Manufacturing, MANUFACTURING 2024 ; Conference Date: 14 May 2024 Through 16 May 2024; Conference Code:310369
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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
date: 2024
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190382628&doi=10.1007%2f978-3-031-56444-4_3&partnerID=40&md5=4fddf2c17be34e7196d11cd49b11bcfe
id_number: 10.1007/978-3-031-56444-4₃
full_text_status: none
publication: Lecture Notes in Mechanical Engineering
pagerange: 34-45
refereed: TRUE
citation:   Khan, R. and Wieczorowski, M. and Qureshi, A. and Ammar, M. and Ahmed, T. and Khan, U.  (2024) Recent Trends in Artificial Intelligence and Machine Learning Methods Applied to Water Jet Machining.  Lecture Notes in Mechanical Engineering.  pp. 34-45.