Improvement of osprey optimization algorithm and weighted RBM features for predicting the surface roughness and flank wear measurement using 1DCNN with ridge regression
Surface finish is considered the significant factor in the evaluation of product quality. Surface roughness (SR) is highly utilized as an index for determining the surface finish at the time of the machining process. The flank wear (FW) is computed with the varying geometrical connections between a...
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| Published in | Machining science and technology Vol. 29; no. 1; pp. 43 - 92 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Taylor & Francis
02.01.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1091-0344 1532-2483 |
| DOI | 10.1080/10910344.2025.2458050 |
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| Abstract | Surface finish is considered the significant factor in the evaluation of product quality. Surface roughness (SR) is highly utilized as an index for determining the surface finish at the time of the machining process. The flank wear (FW) is computed with the varying geometrical connections between a worn and a new cutter, which assures a full FW mapping. In addition, it is essential to forecast the cutting forces for attaining the stability of the machining system and its dimensional accuracy. To meet this requirement, an adaptive technique is proposed for SR, cutting force and FW quantity. The deep features are extracted by using the deep belief network (DBN) from the data values; here attributes are optimized using the adaptive osprey optimization algorithm (AOOA). Finally, the resultant features are given as input to the multiscale atrous spatial pyramid pooling-based one-dimensional convolution neural network with ridge regression (MASPP-1DCNN-RR) for the prediction task. Diverse performance metrics are used to show the efficiency of the recommended scheme. From those experiments, the developed AOOA-MASPP-1DCNN-RR obtains 36.9%, 30.97%, 27.07%, 16.39% and 3.57% improved performance on FW prediction than the Adaboost, support vector machine (SVM), ASPP-1DCNN, ridge regression (RR) and ASPP-1DCNN-RR, respectively, based on analysis of RMSE. |
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| AbstractList | Surface finish is considered the significant factor in the evaluation of product quality. Surface roughness (SR) is highly utilized as an index for determining the surface finish at the time of the machining process. The flank wear (FW) is computed with the varying geometrical connections between a worn and a new cutter, which assures a full FW mapping. In addition, it is essential to forecast the cutting forces for attaining the stability of the machining system and its dimensional accuracy. To meet this requirement, an adaptive technique is proposed for SR, cutting force and FW quantity. The deep features are extracted by using the deep belief network (DBN) from the data values; here attributes are optimized using the adaptive osprey optimization algorithm (AOOA). Finally, the resultant features are given as input to the multiscale atrous spatial pyramid pooling-based one-dimensional convolution neural network with ridge regression (MASPP-1DCNN-RR) for the prediction task. Diverse performance metrics are used to show the efficiency of the recommended scheme. From those experiments, the developed AOOA-MASPP-1DCNN-RR obtains 36.9%, 30.97%, 27.07%, 16.39% and 3.57% improved performance on FW prediction than the Adaboost, support vector machine (SVM), ASPP-1DCNN, ridge regression (RR) and ASPP-1DCNN-RR, respectively, based on analysis of RMSE. |
| Author | S, John Justin Thangaraj V, Dilli Ganesh |
| Author_xml | – sequence: 1 givenname: Dilli Ganesh surname: V fullname: V, Dilli Ganesh organization: Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University – sequence: 2 givenname: John Justin Thangaraj surname: S fullname: S, John Justin Thangaraj organization: Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University |
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| Cites_doi | 10.3389/fmech.2022.1126450 10.1109/ACCESS.2019.2953490 10.1109/JIOT.2022.3175724 10.1109/ACCESS.2019.2944769 10.1109/JSEN.2019.2927174 10.1016/j.knosys.2022.110011 10.1016/j.sna.2021.113161 10.1109/TIA.2012.2199449 10.1016/j.ifacol.2022.04.249 10.1016/j.mlwa.2021.100099 10.17485/ijst/2016/v9i18/88731 10.1007/s11665-024-09726-7 10.1109/TIM.2019.2961572 10.1109/TIM.2022.3214630 10.1109/TIM.2023.3272052 10.1134/S1061830922020073 10.1155/2022/5191758 10.1109/ACCESS.2022.3179818 10.1155/2022/9378487 10.1016/j.procir.2022.03.110 10.1109/8.768797 10.1016/j.eswa.2024.123168 10.1109/TIM.2022.3144232 10.1109/TASE.2014.2369478 10.1016/j.jmapro.2022.10.072 10.1155/2022/6038804 10.1109/TASE.2008.2005640 10.1016/j.procir.2018.08.253 10.1016/j.measurement.2022.112255 10.1016/j.compind.2022.103638 10.1016/j.procir.2021.09.045 10.1109/ACCESS.2020.2984020 10.1109/ACCESS.2021.3084617 10.1109/JSEN.2021.3103059 10.1007/s12065-022-00762-7 10.1109/ACCESS.2022.3230588 10.3390/app14093811 10.1109/TCAD.2009.2030408 |
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| SubjectTerms | Adaptive osprey optimization algorithm deep belief network flank wear measurement multiscale atrous spatial pyramid pooling based one dimensional convolution neural network with ridge regression surface roughness and cutting force prediction |
| Title | Improvement of osprey optimization algorithm and weighted RBM features for predicting the surface roughness and flank wear measurement using 1DCNN with ridge regression |
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