{"title":"Novel Optimization of Friction Welding Parameters to Improve Mechanical Properties","authors":"P. P, D. Jebakani, Krishnaveni A","doi":"10.2139/ssrn.3653416","DOIUrl":"https://doi.org/10.2139/ssrn.3653416","url":null,"abstract":"This paper explores the use of multi-criteria optimization to find better combination of parameters of friction welding process to provide better mechanical properties at the welded joints. This is achieved by developing a new hybrid methodology by integrating TOPSIS method and entropy measurement. TOPSIS method is used to find the order preference by similarity to ideal solution. Entropy method is used to find the weights of output responses. The mechanical strength of friction welded joints depends on several process parameters namely size of work piece, spindle speed, friction time, forging time, friction pressure, etc., Better combination of process parameters leads to quality joint, reduction of cost and wastages. Hybrid TOPSIS method was proposed to predict parameters that resulting superior mechanical properties. Specimen were produced with the predicted parameters and tested to confirm the superiority. Taguchi’s L9 orthogonal array is selected for conducting experiments. It is observed that friction time and spindle speed are the foremost factors but friction pressure influences less on mechanical strength.","PeriodicalId":402421,"journal":{"name":"EngRN: Multidisciplinary Design Optimization (Topic)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116510602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Single- and Multi-Period Portfolio Optimization with Cone Constraints and Discrete Decisions","authors":"Ümit Sağlam, Hande Y. Benson","doi":"10.2139/ssrn.2820294","DOIUrl":"https://doi.org/10.2139/ssrn.2820294","url":null,"abstract":"Portfolio optimization literature has come quite far in the decades since the first publication, and many modern models are formulated using second-order cone constraints and take discrete decisions into consideration. In this study we consider both single-period and multi-period portfolio optimization problems based on the Markowitz (1952) mean/variance framework, where there is a trade-off between expected return and the risk that the investor may be willing to take on. Our model is aggregated from current literature. In this model, we have included transaction costs, conditional value-at-risk (CVaR) constraints, diversification-by-sector constraints and buy-in-thresholds. Our numerical experiments are conducted on portfolios drawn from 20 to 400 different stocks available from the S&P 500 for the single period-model. The multi-period portfolio optimization model is obtained using a binary scenario tree that is constructed with monthly returns of the closing price of the stocks from the S&P 500. We solve these models with a MATLAB based Mixed Integer Linear and Nonlinear Optimizer (MILANO). We provide substantial improvement in runtimes using warmstarts in both branch-and-bound and outer approximation algorithms.","PeriodicalId":402421,"journal":{"name":"EngRN: Multidisciplinary Design Optimization (Topic)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125590054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}