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Prediction of surface roughness and material removal rate in wire electrical discharge machining on aluminum based alloys/composites using Taguchi coupled Grey Relational Analysis and Artificial Neural Networks

Titus Thankachan, K. Soorya Prakash, R Malini, S. Ramu, Prabhu Sundararaj, Sivakumar Rajandran, Devaraj Rammasamy, Sathiskumar Jothi Orcid Logo

Applied Surface Science

Swansea University Author: Sathiskumar Jothi Orcid Logo

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DOI (Published version): 10.1016/j.apsusc.2018.06.117

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In this research, a novel aluminum alloy and metal matrix composite was designed and developed for self healing purpose. Tin at varying weight percentages (5, 10, 15 & 20 wt %) was alloyed into aluminum along with other alloying elements to form a new set of metal alloy and 5 wt% of SiC particle...

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Published in: Applied Surface Science
ISSN: 01694332
Published: 2018
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URI: https://cronfa.swan.ac.uk/Record/cronfa40726
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first_indexed 2018-06-18T13:33:29Z
last_indexed 2018-09-04T18:55:02Z
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spelling 2018-09-04T14:34:10.6912328 v2 40726 2018-06-18 Prediction of surface roughness and material removal rate in wire electrical discharge machining on aluminum based alloys/composites using Taguchi coupled Grey Relational Analysis and Artificial Neural Networks 6cd28300413d3e63178f0bf7e2130569 0000-0001-7328-1112 Sathiskumar Jothi Sathiskumar Jothi true false 2018-06-18 EEN In this research, a novel aluminum alloy and metal matrix composite was designed and developed for self healing purpose. Tin at varying weight percentages (5, 10, 15 & 20 wt %) was alloyed into aluminum along with other alloying elements to form a new set of metal alloy and 5 wt% of SiC particles was dispersed to the above said combinations to develop new sets of composite materials. Optical microscope of the developed set of samples reveals a modification in the grain structure with dispersion of tin element and with respect to increment of tin content the hardness value tends to decrease. Investigated the effect of wire electric discharge machining (WEDM) process parameters such as Pulse On time (PON), Pulse Off time (POFF), wire feed rate (WFR) along with the material elemental composition parameters Sn wt% and SiC wt% using Taguchi coupled Grey Relational Analysis. On behalf of the above said parameters a L32 orthogonal array based experimental design was finalized and based on the experimental studies single and multi criteria based optimization was conceded. Significance of each processing parameters over the output responses Material Removal Rate (MRR) and surface roughness (Ra) was examined through ANOVA method. Machine learning techniques was used and Neural Network models was developed to predict the MRR and Ra values and the experimental confirmations identified the effectiveness of the developed models. Journal Article Applied Surface Science 01694332 Surface Roughness; Machine Learning; Artificial Neural Network; Aluminum alloys; Metal matrix composites; wire electric discharge machining; Parametric optimization 31 12 2018 2018-12-31 10.1016/j.apsusc.2018.06.117 COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University 2018-09-04T14:34:10.6912328 2018-06-18T09:29:29.4554586 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Titus Thankachan 1 K. Soorya Prakash 2 R Malini 3 S. Ramu 4 Prabhu Sundararaj 5 Sivakumar Rajandran 6 Devaraj Rammasamy 7 Sathiskumar Jothi 0000-0001-7328-1112 8 0040726-18062018093118.pdf thankachan2018.pdf 2018-06-18T09:31:18.2670000 Output 2598014 application/pdf Accepted Manuscript true 2019-06-15T00:00:00.0000000 true eng
title Prediction of surface roughness and material removal rate in wire electrical discharge machining on aluminum based alloys/composites using Taguchi coupled Grey Relational Analysis and Artificial Neural Networks
spellingShingle Prediction of surface roughness and material removal rate in wire electrical discharge machining on aluminum based alloys/composites using Taguchi coupled Grey Relational Analysis and Artificial Neural Networks
Sathiskumar Jothi
title_short Prediction of surface roughness and material removal rate in wire electrical discharge machining on aluminum based alloys/composites using Taguchi coupled Grey Relational Analysis and Artificial Neural Networks
title_full Prediction of surface roughness and material removal rate in wire electrical discharge machining on aluminum based alloys/composites using Taguchi coupled Grey Relational Analysis and Artificial Neural Networks
title_fullStr Prediction of surface roughness and material removal rate in wire electrical discharge machining on aluminum based alloys/composites using Taguchi coupled Grey Relational Analysis and Artificial Neural Networks
title_full_unstemmed Prediction of surface roughness and material removal rate in wire electrical discharge machining on aluminum based alloys/composites using Taguchi coupled Grey Relational Analysis and Artificial Neural Networks
title_sort Prediction of surface roughness and material removal rate in wire electrical discharge machining on aluminum based alloys/composites using Taguchi coupled Grey Relational Analysis and Artificial Neural Networks
author_id_str_mv 6cd28300413d3e63178f0bf7e2130569
author_id_fullname_str_mv 6cd28300413d3e63178f0bf7e2130569_***_Sathiskumar Jothi
author Sathiskumar Jothi
author2 Titus Thankachan
K. Soorya Prakash
R Malini
S. Ramu
Prabhu Sundararaj
Sivakumar Rajandran
Devaraj Rammasamy
Sathiskumar Jothi
format Journal article
container_title Applied Surface Science
publishDate 2018
institution Swansea University
issn 01694332
doi_str_mv 10.1016/j.apsusc.2018.06.117
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
active_str 0
description In this research, a novel aluminum alloy and metal matrix composite was designed and developed for self healing purpose. Tin at varying weight percentages (5, 10, 15 & 20 wt %) was alloyed into aluminum along with other alloying elements to form a new set of metal alloy and 5 wt% of SiC particles was dispersed to the above said combinations to develop new sets of composite materials. Optical microscope of the developed set of samples reveals a modification in the grain structure with dispersion of tin element and with respect to increment of tin content the hardness value tends to decrease. Investigated the effect of wire electric discharge machining (WEDM) process parameters such as Pulse On time (PON), Pulse Off time (POFF), wire feed rate (WFR) along with the material elemental composition parameters Sn wt% and SiC wt% using Taguchi coupled Grey Relational Analysis. On behalf of the above said parameters a L32 orthogonal array based experimental design was finalized and based on the experimental studies single and multi criteria based optimization was conceded. Significance of each processing parameters over the output responses Material Removal Rate (MRR) and surface roughness (Ra) was examined through ANOVA method. Machine learning techniques was used and Neural Network models was developed to predict the MRR and Ra values and the experimental confirmations identified the effectiveness of the developed models.
published_date 2018-12-31T03:51:50Z
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score 11.035874

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