材料科学
机械加工
表面粗糙度
田口方法
实验设计
蚀刻(微加工)
表面光洁度
工艺工程
过程(计算)
复合材料
冶金
计算机科学
工程类
操作系统
图层(电子)
数学
统计
作者
Murat Tunç,Hasan Gökkaya,Gökhan Sur,Ali Rıza Motorcu
标识
DOI:10.1108/mmms-07-2022-0138
摘要
Purpose The purpose of the paper is to investigate photochemical machining characteristics of stainless steel (AISI 304-SS304) parts with a novel design are investigated experimentally from the aspect of process parameters. The effects of phototool pattern geometry, ultraviole (UV) exposure time and etching time on of AISI 304 were evaluated. Design/methodology/approach The designed semi-automated photochemical manufacturing (PCM) equipment consists of 4 units, which include UV exposure, etching, developing and surface cleaning units. Experimental procedure has been designed via Taguchi method. Results were evaluated via Analysis of Variance (ANOVA) method. Findings Etching time is the most effective factor in PCM quality of AISI 304 stainless steel. Surface roughness is sensitive to geometrical pattern of the phototool for PCM of AISI 304 UV exposure time is less influential on the PCM quality for stainless steel. Research limitations/implications The designed PCM equipment prototype is not fully automated, which requires automation for part replacements into units. The effects of the temperature inside chemical processing units on process characteristics cannot be evaluated due to equipment limitations. The effects of surface cleaning time inside surface cleaning unit are not analyzed. Originality/value The utilized PCM equipment is semi-automated equipment, with which the process parameters such as etching time, surface cleaning time, UV exposure time and developing time can be controlled. Different from literature, the effects of phototool pattern geometries on the photochemical machining quality parameters are evaluated for the processing of AISI 304. The effects of processing parameters on dimensional accuracy, which is not common in the literature for AISI 304 stainless steel, are also evaluated.
科研通智能强力驱动
Strongly Powered by AbleSci AI