Prediction of cutting force for self-propelled rotary tool using artificial neural networks

Simple item page
dc.contributor.authorHao, Wangshen
dc.contributor.authorZhu, Xunsheng
dc.contributor.authorLi, Xifeng
dc.contributor.authorTuryagyenda, Gelvis
dc.date.accessioned2023-05-05T14:39:30Z
dc.date.available2023-05-05T14:39:30Z
dc.date.issued2006
dc.description.abstractIn this paper, a cutting force model for self-propelled rotary tool (SPRT) cutting force prediction using artificial neural networks (ANN) has been introduced. The basis of this approach is to train and test the ANN model with cutting force samples of SPRT, from which their neurons relations are gradually extracted out. Then, ANN cutting force model is achieved by obtaining all weights for each layer. The inputs to the model consist of cutting velocity V, feed rate f, depth of cut ap and tool inclination angle λ, while the outputs are composed of thrust force Fx, radial force Fy and main cutting force Fz. It significantly reduces the complexity of modeling for SPRT cutting force, and employs non-structure operator parameters more conveniently. Considering the disadvantages of back propagation (BP) such as the convergence to local minima in the error space, developments have been achieved by applying hybrid of genetic algorithm (GA) and BP algorithm hence improve the performance of the ANN model. Validity and efficiency of the model were verified through a variety of SPRT cutting samples from our experiment tested in the cutting force model. The performance of the hybrid of GA–BP cutting force model is fairly satisfactory.en_US
dc.identifier.citationHao, W., Zhu, X., Li, X., & Turyagyenda, G. (2006). Prediction of cutting force for self-propelled rotary tool using artificial neural networks. Journal of Materials Processing Technology, 180(1-3), 23-29.https://doi.org/10.1016/j.jmatprotec.2006.04.123en_US
dc.identifier.issn0924-0136
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/8624
dc.language.isoenen_US
dc.publisherJournal of Materials Processing Technologyen_US
dc.subjectSelf-propelled rotary toolen_US
dc.subjectNeural networken_US
dc.subjectCutting forceen_US
dc.titlePrediction of cutting force for self-propelled rotary tool using artificial neural networksen_US
dc.typeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Prediction of cutting force for self-propelled rotary tool using artificial neural networks.pdf
Size:
234.94 KB
Format:
Adobe Portable Document Format
Description:
Prediction of cutting force for self-propelled rotary tool using artificial neural networks
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Engineering and Technology