Skip to content
6CCVD Research

Surface Topography Analysis and Surface Roughness Prediction Model of Diamond Wire-Sawed NdFeB Magnet Based on Optimized Back Propagation Neural Network

At a Glance

Section titled ā€œAt a Glanceā€
MetadataDetails
Publication Date2025-02-15
JournalProcesses
AuthorsGuanzheng Li, Xing-Chun Zhang, Yufei Gao, Feng Cui, Zhenyu Shi
InstitutionsHebei University of Technology, Shandong University
Citations1

Abstract

Section titled ā€œAbstractā€

Wire sawing is an important process in the cutting of NdFeB magnets and the sawed surface topography and surface roughness (SR) are important indicators for assessing surface quality. This paper analyzed the effects of process parameters on the sawed NdFeB surface topography and SR based on orthogonal experiments and then presented an SR prediction model called ISSA-BP, which was based on a BP neural network using an improved sparrow search algorithm (ISSA). For the problem of insufficient optimization capability of the traditional sparrow search algorithm (SSA), Cubic chaotic mapping, Latin hypercube sampling, the sine-cosine algorithm, Levy flight, and Cauchy mutation were introduced to improve the traditional sparrow search algorithm (SSA) to obtain ISSA, improving algorithm convergence speed and global optimization. The ISSA was then used to optimize the initial weights and thresholds of the BP neural network for predicting Ra. Research shows that the sawed surface topography reflects a combination of brittle and ductile material removal. As the workpiece feed speed and size decrease and the wire speed increases, there is a reduction in SR. Compared with the SSA-BP and traditional BP models, the ISSA-BP prediction model has reduced various error indicators such as mean absolute error (MAE) and mean square error (MSE). The mean absolute error (MAE) of the prediction model optimized by the ISSA is 0.064475, the mean square error (MSE) is 0.0072297, the root mean square error (RMSE) is 0.085028, and the mean absolute percentage error (MAPE) is 3.7171%. The research results provide an experimental basis and technical support for predicting the SR and optimizing the process parameters in diamond wire-sawing NdFeB.

Tech Support

Section titled ā€œTech Supportā€

Original Source

Section titled ā€œOriginal Sourceā€

References

Section titled ā€œReferencesā€
  1. 2024 - Recent advances in mechanical properties of sintered NdFeB magnets [Crossref]
  2. 2024 - Surface Characteristics of Precision As-Cut NdFeB Magnet Considering Diamond Wire Wear [Crossref]
  3. 2020 - Perspective and prospects for rare earth permanent magnets [Crossref]
  4. 2015 - Fast magnetic reconstruction of the portal vein with allogeneic blood vessels in canines. Hepatobiliary
  5. 2020 - Characterization of electroplated diamond wires and the resulting workpiece quality in silicon sawing [Crossref]
  6. 2021 - Influence of diamond wire saw slicing parameters on (010) lattice plane beta-gallium oxide single crystal wafer [Crossref]
  7. 2022 - Influence of single diamond wire sawing of photovoltaic monocrystalline silicon on the feed force, surface roughness and micro-crack depth [Crossref]
  8. 2020 - Experimental study on slicing photovoltaic polycrystalline silicon with diamond wire saw [Crossref]

Reads Similar to This

Comparing the environmental and economic performances of different substrate pre-treatment processes for diamond coating deposition Surface performance evaluation method of sawing natural diamond Femtosecond-laser surface modification and micropatterning of diamond-like nanocomposite films to control friction on the micro and macroscale