Study on Surface Roughness of Sidewall When Micro-Milling LF21 Waveguide Slits
Open Access
- 27 May 2022
- journal article
- research article
- Published by MDPI AG in Applied Sciences
- Vol. 12 (11), 5415
- https://doi.org/10.3390/app12115415
Abstract
The surface quality of the sidewall in waveguide antennae is important, especially surface roughness, which directly affects the electrical performance of the slotted waveguide antenna. Micro-milling is a potentially effective processing technique for the antenna. However, surface roughness has been difficult to guarantee within a reasonable accuracy range. In this study, orthogonal experiments of micro-milling LF21 waveguide slits were conducted. The results of the range analysis mainly sorted the factors that affected the surface roughness and also helped to determine how surface roughness could be kept at a minimum. The surface roughness was predicted by using the group method of data handling (GMDH). The importance of the applied GMDH was that it continuously adjusted the network structure according to the potential relationship between cutting parameters and the corresponding surface roughness, which helped determine the model most optimally fitted to the experimental data. This research can be used as a reference for selecting cutting parameters in micro-milling LF21.Funding Information
- National Natural Science Foundation of China (51875080)
- the Fundamental Research Funds for the Central Universities (DUT20ZD204, 2020JJ26GX041)
This publication has 14 references indexed in Scilit:
- Material perspective on the evolution of micro- and nano-scale cutting of metal alloysJournal of Micromanufacturing, 2018
- Chip perforation and ‘burnishing–like’ finishing of Al alloy in precision machiningPrecision Engineering, 2017
- A surface roughness prediction model using response surface methodology in micro-milling Inconel 718International Journal of Machining and Machinability of Materials, 2017
- Experimental Investigation and Estimation of Surface Roughness using ANN, GMDH & MRA models in High Speed Micro End Milling of Titanium Alloy (Grade-5)Materials Today, 2017
- Mathematical modelling to predict the roughness average in micro milling processIOP Conference Series: Materials Science and Engineering, 2016
- Surface roughness prediction model of micro-milling Inconel 718 with consideration of tool wearInternational Journal of Nanomanufacturing, 2016
- Research on the prediction model of micro-milling surface roughnessInternational Journal of Nanomanufacturing, 2013
- Capability of tungsten carbide micro-mills to machine hardened tool steelInternational Journal of Precision Engineering and Manufacturing, 2012
- An experimental investigation on the machining characteristics of microscale end millingThe International Journal of Advanced Manufacturing Technology, 2011
- Modeling of dynamic micro-milling cutting forcesInternational Journal of Machine Tools and Manufacture, 2009