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Dual-stage artificial neural network (ANN) model for sequential LBMM-μEDM-based micro-drilling

A sequential process combining laser beam micromachining (LBMM) and micro electro-discharge machining (μEDM) for the micro-drilling purpose was developed to incorporate both methods’ benefits. In this sequential process, a guiding hole is produced through LBMM first, followed by μEDM applied to th...

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Main Authors: Noor, Wazed Ibne, Saleh, Tanveer, Rashid, Mir Akmam Noor, Ibrahim, Azhar Mohd, Ali, Mohamed Sultan Mohamed, ,
Format: Article
Language:English
English
Published: Springer Nature 2021
Subjects:
T Technology (General)
Online Access:http://irep.iium.edu.my/91994/7/91994_Dual-stage%20artificial%20neural%20network%20%28ANN%29%20model.pdf
http://irep.iium.edu.my/91994/13/91994_Dual-stage%20artificial%20neural%20network%20%28ANN%29%20model_SCOPUS.pdf
http://irep.iium.edu.my/91994/
https://link.springer.com/article/10.1007/s00170-021-07910-w
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spelling my.iium.irep.919942021-09-06T07:28:17Z http://irep.iium.edu.my/91994/ Dual-stage artificial neural network (ANN) model for sequential LBMM-μEDM-based micro-drilling Noor, Wazed Ibne Saleh, Tanveer Rashid, Mir Akmam Noor Ibrahim, Azhar Mohd Ali, Mohamed Sultan Mohamed , T Technology (General) A sequential process combining laser beam micromachining (LBMM) and micro electro-discharge machining (μEDM) for the micro-drilling purpose was developed to incorporate both methods’ benefits. In this sequential process, a guiding hole is produced through LBMM first, followed by μEDM applied to that same hole for more fine machining. This process facilitates a more stable, efficient machining regime with faster processing (compared to pure μEDM) and a much better hole quality (compared to LBMMed holes). Studies suggest that strong correlations exist between the various input and output parameters of the sequential process. However, a mathematical model that maps and simultaneously predicts all these output parameters from the input parameters is yet to be developed. Our experimental study observed that the μEDMfinishing operation’s various output parameters are influenced by the morphological condition of the LBMMed holes. Hence, an artificial neural network (ANN)- based dual-stage modeling method was developed to predict the sequential process’s outputs. The first stage of the dual-stage model was utilized to predict various LBMM process outputs from different laser input parameters. Furthermore, in the second stage, LBMM-predicted outputs (such as pilot hole entry area, exit area, recast layer, and heat-affected zone) were used for the final prediction of the sequential process outputs (i.e., machining time by μEDM, machining stability during μEDM in terms of short circuit/arcing count, and toolwear during μEDM). The model was evaluated based on the average RMSE (rootmean square errors) values for the individual output parameters’ complete set data, i.e., μEDMtime, short circuit/arcing count, and tool wear. The values of average RMSE for the parameters as mentioned earlier were found to be 0.1272 (87.28% accuracy), 0.1085 (89.15% accuracy), and 0.097 (90.3% accuracy), respectively. Springer Nature 2021-08-27 Article PeerReviewed application/pdf en http://irep.iium.edu.my/91994/7/91994_Dual-stage%20artificial%20neural%20network%20%28ANN%29%20model.pdf application/pdf en http://irep.iium.edu.my/91994/13/91994_Dual-stage%20artificial%20neural%20network%20%28ANN%29%20model_SCOPUS.pdf Noor, Wazed Ibne and Saleh, Tanveer and Rashid, Mir Akmam Noor and Ibrahim, Azhar Mohd and Ali, Mohamed Sultan Mohamed and UNSPECIFIED (2021) Dual-stage artificial neural network (ANN) model for sequential LBMM-μEDM-based micro-drilling. The International Journal of Advanced Manufacturing Technology. pp. 1-24. ISSN 0268-3768 E-ISSN 1433-3015 https://link.springer.com/article/10.1007/s00170-021-07910-w 10.1007/s00170-021-07910-w
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic T Technology (General)
spellingShingle T Technology (General)
Noor, Wazed Ibne
Saleh, Tanveer
Rashid, Mir Akmam Noor
Ibrahim, Azhar Mohd
Ali, Mohamed Sultan Mohamed
,
Dual-stage artificial neural network (ANN) model for sequential LBMM-μEDM-based micro-drilling
description A sequential process combining laser beam micromachining (LBMM) and micro electro-discharge machining (μEDM) for the micro-drilling purpose was developed to incorporate both methods’ benefits. In this sequential process, a guiding hole is produced through LBMM first, followed by μEDM applied to that same hole for more fine machining. This process facilitates a more stable, efficient machining regime with faster processing (compared to pure μEDM) and a much better hole quality (compared to LBMMed holes). Studies suggest that strong correlations exist between the various input and output parameters of the sequential process. However, a mathematical model that maps and simultaneously predicts all these output parameters from the input parameters is yet to be developed. Our experimental study observed that the μEDMfinishing operation’s various output parameters are influenced by the morphological condition of the LBMMed holes. Hence, an artificial neural network (ANN)- based dual-stage modeling method was developed to predict the sequential process’s outputs. The first stage of the dual-stage model was utilized to predict various LBMM process outputs from different laser input parameters. Furthermore, in the second stage, LBMM-predicted outputs (such as pilot hole entry area, exit area, recast layer, and heat-affected zone) were used for the final prediction of the sequential process outputs (i.e., machining time by μEDM, machining stability during μEDM in terms of short circuit/arcing count, and toolwear during μEDM). The model was evaluated based on the average RMSE (rootmean square errors) values for the individual output parameters’ complete set data, i.e., μEDMtime, short circuit/arcing count, and tool wear. The values of average RMSE for the parameters as mentioned earlier were found to be 0.1272 (87.28% accuracy), 0.1085 (89.15% accuracy), and 0.097 (90.3% accuracy), respectively.
format Article
author Noor, Wazed Ibne
Saleh, Tanveer
Rashid, Mir Akmam Noor
Ibrahim, Azhar Mohd
Ali, Mohamed Sultan Mohamed
,
author_facet Noor, Wazed Ibne
Saleh, Tanveer
Rashid, Mir Akmam Noor
Ibrahim, Azhar Mohd
Ali, Mohamed Sultan Mohamed
,
author_sort Noor, Wazed Ibne
title Dual-stage artificial neural network (ANN) model for sequential LBMM-μEDM-based micro-drilling
title_short Dual-stage artificial neural network (ANN) model for sequential LBMM-μEDM-based micro-drilling
title_full Dual-stage artificial neural network (ANN) model for sequential LBMM-μEDM-based micro-drilling
title_fullStr Dual-stage artificial neural network (ANN) model for sequential LBMM-μEDM-based micro-drilling
title_full_unstemmed Dual-stage artificial neural network (ANN) model for sequential LBMM-μEDM-based micro-drilling
title_sort dual-stage artificial neural network (ann) model for sequential lbmm-μedm-based micro-drilling
publisher Springer Nature
publishDate 2021
url http://irep.iium.edu.my/91994/7/91994_Dual-stage%20artificial%20neural%20network%20%28ANN%29%20model.pdf
http://irep.iium.edu.my/91994/13/91994_Dual-stage%20artificial%20neural%20network%20%28ANN%29%20model_SCOPUS.pdf
http://irep.iium.edu.my/91994/
https://link.springer.com/article/10.1007/s00170-021-07910-w
_version_ 1710675148277809152
score 13.211869

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