کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7121207 | 1461464 | 2018 | 25 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Grey fuzzy optimization of ultrasonic-assisted EDM process parameters for deburring CFRP composites
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
کنترل و سیستم های مهندسی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Grey fuzzy optimization of ultrasonic-assisted EDM process parameters for deburring CFRP composites Grey fuzzy optimization of ultrasonic-assisted EDM process parameters for deburring CFRP composites](/preview/png/7121207.png)
چکیده انگلیسی
The reduction of burr formation during machining of carbon fiber reinforced plastic composite is of great concern in many applications. This novel work presents the deburring of CFRP using ultrasonic-assisted electrical discharge machining. An L18 array varying the input parameters, such as capacitance, pulse-on time, and ultrasonic voltage, was selected to assess the output performances like tool wear rate and burr removal rate. Multi-objective optimization was performed by the grey fuzzy logic method and the optimum input parameter was predicted based on the results of grey fuzzy reasoning grade. The significance of the process parameters on TWR and BRR were also analyzed using analysis of variance. The experimental results show that a capacitance of 10,000â¯pF, TON of 200â¯Âµs, and VUS of 150â¯V were the optimal input parameters to simultaneously minimize TWR and maximize BRR. The burr free exit side walls were observed by scanning electron microscopic image during machining at the optimum condition. However, the vaporization of the resin near the hole surface is unavoidable which is due to the electro-thermal effect of EDM process. The capacitance (63.58%), TON (29.17%), and VUS (2.76%) were found to be the factors most affecting the combined output responses. Finally, the verification of the results shows an overall improvement of 7.14% on TWR, 19.88% on BRR, and 1.3% on GFRG.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Measurement - Volume 123, July 2018, Pages 203-212
Journal: Measurement - Volume 123, July 2018, Pages 203-212
نویسندگان
S. Thirumalai Kumaran, Tae Jo Ko, Rendi Kurniawan,