{"title":"Multi-Objective Optimization for Weld Track Geometry in Wire-Arc Directed Energy Deposition of ER308L Stainless Steel","authors":"V. C. Nguyen, V. Le, N. Pham, Anh-Thang Nguyen","doi":"10.36897/jme/166134","DOIUrl":null,"url":null,"abstract":"In this research, the weld track geometry in wire-arc DED (directed energy deposition) of ER308L stainless steel was predicted and optimized. The studied geometrical attributes of weld tracks include weld track width ( WTW ), weld track height ( WTH ), and contact angle ( α ). The experiment was designed based on Taguchi method with three variables (current I , voltage U , and weld velocity v ) and four levels for each variable. The ANOVA was adopted to evaluate the accuracy of the models and impact levels of variables on the responses. The TOPSIS method was utilized to predict the optimal variables. The results indicated that the predicted models were built with high accuracy levels ( R 2 = 98.92%, 98.77%, and 98.91% for WTW , WTH , and α , respectively). Among the studied variables, U features the highest effects on WTW and with 78.56% and 69.90% of contribution, respectively, while v is the variable that has the most impact on WTH with 39.82% of contribution. The optimal variables predicted by TOPSIS were U = 23 V, I = 140 A, and v = 300 mm/min, which allows building components with stable and regular geometry.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Machine Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.36897/jme/166134","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
Abstract
In this research, the weld track geometry in wire-arc DED (directed energy deposition) of ER308L stainless steel was predicted and optimized. The studied geometrical attributes of weld tracks include weld track width ( WTW ), weld track height ( WTH ), and contact angle ( α ). The experiment was designed based on Taguchi method with three variables (current I , voltage U , and weld velocity v ) and four levels for each variable. The ANOVA was adopted to evaluate the accuracy of the models and impact levels of variables on the responses. The TOPSIS method was utilized to predict the optimal variables. The results indicated that the predicted models were built with high accuracy levels ( R 2 = 98.92%, 98.77%, and 98.91% for WTW , WTH , and α , respectively). Among the studied variables, U features the highest effects on WTW and with 78.56% and 69.90% of contribution, respectively, while v is the variable that has the most impact on WTH with 39.82% of contribution. The optimal variables predicted by TOPSIS were U = 23 V, I = 140 A, and v = 300 mm/min, which allows building components with stable and regular geometry.
期刊介绍:
ournal of Machine Engineering is a scientific journal devoted to current issues of design and manufacturing - aided by innovative computer techniques and state-of-the-art computer systems - of products which meet the demands of the current global market. It favours solutions harmonizing with the up-to-date manufacturing strategies, the quality requirements and the needs of design, planning, scheduling and production process management. The Journal'' s subject matter also covers the design and operation of high efficient, precision, process machines. The Journal is a continuator of Machine Engineering Publisher for five years. The Journal appears quarterly, with a circulation of 100 copies, with each issue devoted entirely to a different topic. The papers are carefully selected and reviewed by distinguished world famous scientists and practitioners. The authors of the publications are eminent specialists from all over the world and Poland. Journal of Machine Engineering provides the best assistance to factories and universities. It enables factories to solve their difficult problems and manufacture good products at a low cost and fast rate. It enables educators to update their teaching and scientists to deepen their knowledge and pursue their research in the right direction.