eprintid: 17039
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/70/39
datestamp: 2023-12-19 03:23:30
lastmod: 2023-12-19 03:23:30
status_changed: 2023-12-19 03:07:21
type: article
metadata_visibility: show
creators_name: Ting, H.Y.
creators_name: Asmelash, M.
creators_name: Azhari, A.
creators_name: Alemu, T.
creators_name: Saptaji, K.
title: Prediction of surface roughness of titanium alloy in abrasive waterjet machining process
ispublished: pub
keywords: Backpropagation; Design of experiments; Forecasting; Machining; Machining centers; Multilayer neural networks; Regression analysis; Support vector machines; Titanium alloys, Abrasive waterjet machining; Abrasive waterjets; Comparison between methods; Low pressures; Machining Process; Regression analysis models; Support vector regressions; Support vectors machine; Titanium (alloys); Waterjet machining, Surface roughness
note: cited By 6
abstract: This work presents a comparison between methods for predicting surface roughness (Ra) of titanium alloy machined by low-pressure abrasive water jet machine developed in our laboratory. Artificial neural network (ANN), support vector machine (SVM) and regression analysis (RA) models were used to analyse the machining data. The work aims at the comparison and selection of the best prediction method based on the accuracy of the predicted surface roughness. An experiment was designed using full factorial experimental design. In the experiment, machining parameters of traverse speed (V), waterjet pressure (P) and standoff distance (h) were considered as model variables. The actual surface roughness values were collected based on the designed experiment. A feed forward back propagation neural network was used, structured as one input layer, one hidden layer with 5 hidden neurons and one output layer. Both ANN and SVM models were trained and analysis of variance was used. F-test was used to validate the RA model. The results showed that the proposed methods indicated an acceptable level of accuracy for predicting the surface roughness, however, ANN model had better accuracy than SVM and RA models as it produced lower relative errors between the predicted values and experimental results. © 2022, The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature.
date: 2022
publisher: Springer-Verlag Italia s.r.l.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123113832&doi=10.1007%2fs12008-021-00830-9&partnerID=40&md5=babc75e0b9cb844d142f73b6c9347836
id_number: 10.1007/s12008-021-00830-9
full_text_status: none
publication: International Journal on Interactive Design and Manufacturing
volume: 16
number: 1
pagerange: 281-289
refereed: TRUE
issn: 19552513
citation:   Ting, H.Y. and Asmelash, M. and Azhari, A. and Alemu, T. and Saptaji, K.  (2022) Prediction of surface roughness of titanium alloy in abrasive waterjet machining process.  International Journal on Interactive Design and Manufacturing, 16 (1).  pp. 281-289.  ISSN 19552513