Machining Parameters and Toolpath Productivity Optimization Using a Factorial Design and Fit Regression Model in Face Milling and Drilling Operations

Minquiz Gustavo M.; Borja Vicente; Lopez-Parra MarceloRamirez-Reivich Alejandro C.Ruiz-Huerta LeopoldoLazaro R. C. AmbrosioSanchez Alejandro Shigeru YamamotoVazquez-Leal H.Pavon-Solana Maria-EstherFlores Mendez J.

首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >Machining Parameters and Toolpath Productivity Optimization Using a Factorial Design and Fit Regression Model in Face Milling and Drilling Operations

【24h】

Machining Parameters and Toolpath Productivity Optimization Using a Factorial Design and Fit Regression Model in Face Milling and Drilling Operations

机译：Machining Parameters and Toolpath Productivity Optimization Using a Factorial Design and Fit Regression Model in Face Milling and Drilling Operations

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相关主题

摘要

Very commonly, a mechanical workpiece manufactured industrially includes more than one machining operation. Even more, it is a common activity of programmers, who make a decision in this regard every time a milling and drilling operation is performed. This research is focused on better understanding the power behavior for face milling and drilling manufacturing operations, and the methodology followed was the design of experiments (DOEs) with the cutting parameters set in combination with toolpath evaluation available in commercial software, having as main goal to get a predictive power equation validated in two ways, linear or nonlinear, and understanding the energy consumption and the quality surface in face milling and final diameter in drilling. The results show that it is possible to find difference in a power demand of 1.52 kW to 3.9 kW in the same workpiece, depending on the operations (face milling or drilling), cutting parameters, and toolpath chosen. Additionally, the equations modelled showed acceptable values to predict the power, withpvalues higher than 0.05 which is the significance level for the nonlinear and linear equations with anRsquare predictive of 98.36. Some conclusions established that optimization of the cutting parameters combined with toolpath strategies can represent an energy consumption optimization higher than 0.21% and the importance to try to find an energy consumption balance when a workpiece has different milling operations.

著录项

来源
《Mathematical Problems in Engineering: Theory, Methods and Applications》 |2020年第26期|8718597.1-8718597.13|共13页
作者
Minquiz Gustavo M.; Borja Vicente; Lopez-Parra MarceloRamirez-Reivich Alejandro C.Ruiz-Huerta LeopoldoLazaro R. C. AmbrosioSanchez Alejandro Shigeru YamamotoVazquez-Leal H.Pavon-Solana Maria-EstherFlores Mendez J.;
展开▼
作者单位

Benemerita Univ Autonoma Puebla, Ciudad Univ,Blvd Valsequillo & Esquina, Puebla 72570, Pue, Mexico|Tecnol Nacl Mexico IT Puebla, Av Tecnol 420, Puebla 72220, Pue, Mexico;

Univ Nacl Autonoma Mexico, Fac Ingn, Av Univ 3000, Mexico City 04510, DF, Mexico;

Univ Nacl Autonoma Mexico, Inst Ciencias Aplicadas & Tecnol ICAT, Circuito Exterior S-N,Ciudad Univ,AP 70-186, Mexico City 04510, DF, Mexico|Natl Lab Addit & Digital Mfg MADiT, Circuito Exterior S-N,Ciudad Univ,AP 70-186, Mexico City 04510, DF, MexicoBenemerita Univ Autonoma Puebla, Ciudad Univ,Blvd Valsequillo & Esquina, Puebla 72570, Pue, MexicoSandvik Coromant Mexico Parque Ind Queretaro, Av Cerrada Estacada 550 C, Santa Rosa Jauregui 76220, MexicoUniv Veracruzana, Fac Instrumentac Elect, Cto Gonzalo Aguirre Beltran S-N, Xalapa 91000, Veracruz, Mexico;

展开▼
收录信息
原文格式 PDF
正文语种英语
中图分类
关键词

Machining Parameters and Toolpath Productivity Optimization Using a Factorial Design and Fit Regression Model in Face Milling and Drilling Operations

摘要

著录项

相关主题

期刊订阅