{"title":"Swarm Optimisation to Model the Surface Roughness of an AISI 4340 Turning using the Hot Machining Process","authors":"Ismail Thamrin, Amrifan Saladin Mohruni, Irsyadi Yani, Riman Sipahutar, Zulkarnain Ali Leman","doi":"10.37934/arfmts.117.2.147156","DOIUrl":null,"url":null,"abstract":"Given that surface roughness is used to determine product quality, it is a crucial consideration in turning machining. Moreover, it considerably affects the cost of machining. This study forecasts surface roughness values for AISI 304 stainless-steel hot lathe machining using the particle swarm optimisation (PSO) methodology. The workpiece is heated to 100, 150 or 200 degrees Celsius before being turned. Afterwards, the depth, speed and feeding rate of cutting are adjusted to determine the surface roughness of the workpiece. The feeding rate is determined to be the most considerable influence in raising the surface roughness value, followed by cutting depth, cutting speed and workpiece temperature. In terms of accuracy, empirical modelling performs better. The PSO methodology illustrates an effective and straightforward method that can be applied to calibrate different empirical machining models.","PeriodicalId":37460,"journal":{"name":"Journal of Advanced Research in Fluid Mechanics and Thermal Sciences","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advanced Research in Fluid Mechanics and Thermal Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37934/arfmts.117.2.147156","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Chemical Engineering","Score":null,"Total":0}
引用次数: 0
Abstract
Given that surface roughness is used to determine product quality, it is a crucial consideration in turning machining. Moreover, it considerably affects the cost of machining. This study forecasts surface roughness values for AISI 304 stainless-steel hot lathe machining using the particle swarm optimisation (PSO) methodology. The workpiece is heated to 100, 150 or 200 degrees Celsius before being turned. Afterwards, the depth, speed and feeding rate of cutting are adjusted to determine the surface roughness of the workpiece. The feeding rate is determined to be the most considerable influence in raising the surface roughness value, followed by cutting depth, cutting speed and workpiece temperature. In terms of accuracy, empirical modelling performs better. The PSO methodology illustrates an effective and straightforward method that can be applied to calibrate different empirical machining models.
期刊介绍:
This journal welcomes high-quality original contributions on experimental, computational, and physical aspects of fluid mechanics and thermal sciences relevant to engineering or the environment, multiphase and microscale flows, microscale electronic and mechanical systems; medical and biological systems; and thermal and flow control in both the internal and external environment.