Full text

Turn on search term navigation

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

The ultrasonic vibration extrusion process is a widely used surface treatment process of stainless steel, e.g., 304 stainless steel, to improve the surface quality and increase hardness and wear resistance. However, for high-hardness 304 stainless steel, the traditional process, i.e., a single ultrasonic vibration extrusion process, does not fulfil its application requirements. To cope with this problem, this paper proposes an improved surface treatment method based on low-temperature chromizing and ultrasonic vibration extrusion to obtain the expected surface quality of 304 stainless steel. Using orthogonal design and multivariate regression, the influence of ultrasonic impact parameters on the surface integrity of 304 stainless steel was studied in this work. Finally, the experimental results show that the hardness of the surface processed by the proposed method is increased by about 2.55 times compared with the ultrasonic vibration extrusion process, and the surface roughness of the composite process is reduced by an average of 60.8% compared with that of unfinished surface. In addition, the optimal combination of process parameters is obtained: the spindle speed of 240 rpm, the feed of 0.1 mm/r, and the static extrusion of 40 μm, which can provide the optimal process parameter support for the surface treatment of 304 stainless steel.

Details

Title
An Improved Surface Treatment Process of 304 Stainless Steel Based on Low-Temperature Chromizing and Ultrasonic Vibration Extrusion
Author
Cao, Yansheng 1 ; Zheng, Lianyu 2 ; Fan, Wei 3   VIAFID ORCID Logo 

 School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China; Beijing Xinfeng Aerospace Equipment Co., Ltd., Beijing 100854, China 
 School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China; Institute of Artificial Intelligence, Beihang University, Beijing 100191, China; MIIT Key Laboratory of Intelligent Manufacturing Technology for Aeronautics Advanced Equipments, Ministry of Industry and Information Technology, Beijing 100191, China 
 School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China; MIIT Key Laboratory of Intelligent Manufacturing Technology for Aeronautics Advanced Equipments, Ministry of Industry and Information Technology, Beijing 100191, China 
First page
11729
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20763417
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2739423196
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.