{"title":"用中心复合设计方法降低熔丝加工(FFF)过程的表面粗糙度","authors":"Karin Kandananond","doi":"10.30657/pea.2022.28.18","DOIUrl":null,"url":null,"abstract":"Abstract The objective of this study is to optimize the fabrication factors of a consumer-grade fused filament fabrication (FFF) system. The input factors were nozzle temperature, bed temperature, printing speed, and layer thickness. The optimization aims to minimize average surface roughness (Ra) indicating the surface quality of benchmarks. In this study, Ra was measured at two positions, the bottom and top surface of benchmarks. For the fabrication, the material used was the Polylactic acid (PLA) filament. A response surface method (RSM), central composite design (CCD), was utilized to carry out the optimization. The analysis of variance (ANOVA) was calculated to explore the significant factors, interactions, quadratic effect, and lack of fit, while the regression analysis was performed to determine the prediction equation of Ra. The model adequacy checking was conducted to check whether the residual assumption still held. The total number of thirty benchmarks was fabricated and measured using a surface roughness tester. For the bottom surface, the analysis results indicated that there was the main effect from only one factor, printing speed. However, for the top surface, the ANOVA signified an interaction between the printing speed and layer thickness. The optimal setting of these factors was also recommended, while the empirical models of Ra at both surface positions were also presented. Finally, an extra benchmark was fabricated to validate the empirical model.","PeriodicalId":36269,"journal":{"name":"Production Engineering Archives","volume":"28 1","pages":"157 - 163"},"PeriodicalIF":1.9000,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Surface Roughness Reduction in A Fused Filament Fabrication (FFF) Process using Central Composite Design Method\",\"authors\":\"Karin Kandananond\",\"doi\":\"10.30657/pea.2022.28.18\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract The objective of this study is to optimize the fabrication factors of a consumer-grade fused filament fabrication (FFF) system. The input factors were nozzle temperature, bed temperature, printing speed, and layer thickness. The optimization aims to minimize average surface roughness (Ra) indicating the surface quality of benchmarks. In this study, Ra was measured at two positions, the bottom and top surface of benchmarks. For the fabrication, the material used was the Polylactic acid (PLA) filament. A response surface method (RSM), central composite design (CCD), was utilized to carry out the optimization. The analysis of variance (ANOVA) was calculated to explore the significant factors, interactions, quadratic effect, and lack of fit, while the regression analysis was performed to determine the prediction equation of Ra. The model adequacy checking was conducted to check whether the residual assumption still held. The total number of thirty benchmarks was fabricated and measured using a surface roughness tester. For the bottom surface, the analysis results indicated that there was the main effect from only one factor, printing speed. However, for the top surface, the ANOVA signified an interaction between the printing speed and layer thickness. The optimal setting of these factors was also recommended, while the empirical models of Ra at both surface positions were also presented. Finally, an extra benchmark was fabricated to validate the empirical model.\",\"PeriodicalId\":36269,\"journal\":{\"name\":\"Production Engineering Archives\",\"volume\":\"28 1\",\"pages\":\"157 - 163\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2022-05-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Production Engineering Archives\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.30657/pea.2022.28.18\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Production Engineering Archives","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.30657/pea.2022.28.18","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
Surface Roughness Reduction in A Fused Filament Fabrication (FFF) Process using Central Composite Design Method
Abstract The objective of this study is to optimize the fabrication factors of a consumer-grade fused filament fabrication (FFF) system. The input factors were nozzle temperature, bed temperature, printing speed, and layer thickness. The optimization aims to minimize average surface roughness (Ra) indicating the surface quality of benchmarks. In this study, Ra was measured at two positions, the bottom and top surface of benchmarks. For the fabrication, the material used was the Polylactic acid (PLA) filament. A response surface method (RSM), central composite design (CCD), was utilized to carry out the optimization. The analysis of variance (ANOVA) was calculated to explore the significant factors, interactions, quadratic effect, and lack of fit, while the regression analysis was performed to determine the prediction equation of Ra. The model adequacy checking was conducted to check whether the residual assumption still held. The total number of thirty benchmarks was fabricated and measured using a surface roughness tester. For the bottom surface, the analysis results indicated that there was the main effect from only one factor, printing speed. However, for the top surface, the ANOVA signified an interaction between the printing speed and layer thickness. The optimal setting of these factors was also recommended, while the empirical models of Ra at both surface positions were also presented. Finally, an extra benchmark was fabricated to validate the empirical model.