Discrete wavelet transformation and genetic algorithm – back propagation neural network applied in monitoring woodworking tool wear conditions in the milling operation spindle power signals Article Swipe
Weihang Dong
,
Xiaolei Guo
,
Yong Hu
,
Jinxin Wang
,
Guangjun Tian
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.15376/biores.16.2.2369-2384
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.15376/biores.16.2.2369-2384
Tool wear conditions monitoring is an important mechanical processing system that can improve the processing quality of wood plastic composite furniture and reduce industrial energy consumption. An appropriate signal, feature extraction method, and model establishment method can effectively improve the accuracy of tool wear monitoring. In this work, an effective method based on discrete wavelet transformation (DWT) and genetic algorithm (GA) – back propagation (BP) neural network was proposed to monitor the tool wear conditions. The spindle power signals under different spindle speeds, depths of milling, and tool wear conditions were collected by power sensors connected to the machine tool control box. Based on the feature extraction method, the approximate coefficients of spindle power signal were extracted by DWT. Then, the extracted approximate coefficients, spindle speeds, depths of milling, and tool wear conditions were taken as samples to train the monitoring model. Threshold and weight of BP neural network were optimized by GA, and the accuracy of monitoring model established by the GA – BP neural network can reach 100%. Thus, the proposed monitoring method can accurately monitor tool wear conditions with different milling parameters, which can achieve the purpose of improving the processing quality of wood plastic composite furniture and reducing energy consumption.
Related Topics
Concepts
Artificial neural network
Tool wear
Genetic algorithm
Energy (signal processing)
Machine tool
Power (physics)
Milling cutter
SIGNAL (programming language)
Feature (linguistics)
Feature extraction
Energy consumption
Transformation (genetics)
Discrete wavelet transform
Signal processing
Engineering
Wavelet transform
Wavelet
Computer science
Machining
Mechanical engineering
Artificial intelligence
Electronic engineering
Digital signal processing
Machine learning
Mathematics
Physics
Biochemistry
Programming language
Electrical engineering
Statistics
Gene
Quantum mechanics
Chemistry
Philosophy
Linguistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.15376/biores.16.2.2369-2384
- https://bioresources.cnr.ncsu.edu/wp-content/uploads/2021/02/BioRes_16_2_2369_Dong_GHWT_Wavelet_Transform_Genetic_Algor_BP_Neural_Net_Wood_18395.pdf
- OA Status
- gold
- Cited By
- 8
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3128561071
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3128561071Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.15376/biores.16.2.2369-2384Digital Object Identifier
- Title
-
Discrete wavelet transformation and genetic algorithm – back propagation neural network applied in monitoring woodworking tool wear conditions in the milling operation spindle power signalsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-02-05Full publication date if available
- Authors
-
Weihang Dong, Xiaolei Guo, Yong Hu, Jinxin Wang, Guangjun TianList of authors in order
- Landing page
-
https://doi.org/10.15376/biores.16.2.2369-2384Publisher landing page
- PDF URL
-
https://bioresources.cnr.ncsu.edu/wp-content/uploads/2021/02/BioRes_16_2_2369_Dong_GHWT_Wavelet_Transform_Genetic_Algor_BP_Neural_Net_Wood_18395.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://bioresources.cnr.ncsu.edu/wp-content/uploads/2021/02/BioRes_16_2_2369_Dong_GHWT_Wavelet_Transform_Genetic_Algor_BP_Neural_Net_Wood_18395.pdfDirect OA link when available
- Concepts
-
Artificial neural network, Tool wear, Genetic algorithm, Energy (signal processing), Machine tool, Power (physics), Milling cutter, SIGNAL (programming language), Feature (linguistics), Feature extraction, Energy consumption, Transformation (genetics), Discrete wavelet transform, Signal processing, Engineering, Wavelet transform, Wavelet, Computer science, Machining, Mechanical engineering, Artificial intelligence, Electronic engineering, Digital signal processing, Machine learning, Mathematics, Physics, Biochemistry, Programming language, Electrical engineering, Statistics, Gene, Quantum mechanics, Chemistry, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 2, 2022: 3, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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26Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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