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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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 |
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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 |
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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. |
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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 |
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13.18916 |