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

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...

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Main Authors: Gustavo M. Minquiz, Vicente Borja, Marcelo López-Parra, Alejandro C. Ramírez-Reivich, Leopoldo Ruiz-Huerta, R. C. Ambrosio Lázaro, Alejandro Shigeru Yamamoto Sánchez, H. Vazquez-Leal, María-Esther Pavon-Solana, J. Flores Méndez
Format: Article
Language:English
Published: Hindawi Limited 2020-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2020/8718597
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spelling doaj-d8547003b1844feb823c0047ad8ecf552020-11-25T03:18:59ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472020-01-01202010.1155/2020/87185978718597Machining Parameters and Toolpath Productivity Optimization Using a Factorial Design and Fit Regression Model in Face Milling and Drilling OperationsGustavo M. Minquiz0Vicente Borja1Marcelo López-Parra2Alejandro C. Ramírez-Reivich3Leopoldo Ruiz-Huerta4R. C. Ambrosio Lázaro5Alejandro Shigeru Yamamoto Sánchez6H. Vazquez-Leal7María-Esther Pavon-Solana8J. Flores Méndez9Benemérita Universidad Autónoma de Puebla-Ciudad Universitaria, Blvd. Valsequillo y Esquina, Av. San Claudio s/n, Col. San Manuel, C.P. 72570, Puebla, Pue, MexicoUniversidad Nacional Autónoma de México, Facultad de Ingeniería, Av. Universidad No. 3000, C.P. 04510, Ciudad de México, MexicoUniversidad Nacional Autónoma de México, Facultad de Ingeniería, Av. Universidad No. 3000, C.P. 04510, Ciudad de México, MexicoUniversidad Nacional Autónoma de México, Facultad de Ingeniería, Av. Universidad No. 3000, C.P. 04510, Ciudad de México, MexicoUniversidad Nacional Autónoma de México, Instituto de Ciencias Aplicadas y Tecnología (ICAT), Circuito Exterior s/n, Ciudad Universitaria AP 70-186, C.P. 04510, Ciudad de México, MexicoBenemérita Universidad Autónoma de Puebla-Ciudad Universitaria, Blvd. Valsequillo y Esquina, Av. San Claudio s/n, Col. San Manuel, C.P. 72570, Puebla, Pue, MexicoSandvik Coromant México-Parque Industrial Querétaro, Av. Cerrada de la Estacada #550 C, Santa Rosa Jaúregui, C.P. 76220, MexicoFacultad de Instrumentación Electrónica, Universidad Veracruzana, Cto. Gonzalo Aguirre Beltrán S/N 91000, Xalapa-Veracruz, MexicoBenemérita Universidad Autónoma de Puebla-Ciudad Universitaria, Blvd. Valsequillo y Esquina, Av. San Claudio s/n, Col. San Manuel, C.P. 72570, Puebla, Pue, MexicoBenemérita Universidad Autónoma de Puebla-Ciudad Universitaria, Blvd. Valsequillo y Esquina, Av. San Claudio s/n, Col. San Manuel, C.P. 72570, Puebla, Pue, MexicoVery 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, with p values higher than 0.05 which is the significance level for the nonlinear and linear equations with an R square 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.http://dx.doi.org/10.1155/2020/8718597
collection DOAJ
language English
format Article
sources DOAJ
author Gustavo M. Minquiz
Vicente Borja
Marcelo López-Parra
Alejandro C. Ramírez-Reivich
Leopoldo Ruiz-Huerta
R. C. Ambrosio Lázaro
Alejandro Shigeru Yamamoto Sánchez
H. Vazquez-Leal
María-Esther Pavon-Solana
J. Flores Méndez
spellingShingle Gustavo M. Minquiz
Vicente Borja
Marcelo López-Parra
Alejandro C. Ramírez-Reivich
Leopoldo Ruiz-Huerta
R. C. Ambrosio Lázaro
Alejandro Shigeru Yamamoto Sánchez
H. Vazquez-Leal
María-Esther Pavon-Solana
J. Flores Méndez
Machining Parameters and Toolpath Productivity Optimization Using a Factorial Design and Fit Regression Model in Face Milling and Drilling Operations
Mathematical Problems in Engineering
author_facet Gustavo M. Minquiz
Vicente Borja
Marcelo López-Parra
Alejandro C. Ramírez-Reivich
Leopoldo Ruiz-Huerta
R. C. Ambrosio Lázaro
Alejandro Shigeru Yamamoto Sánchez
H. Vazquez-Leal
María-Esther Pavon-Solana
J. Flores Méndez
author_sort Gustavo M. Minquiz
title Machining Parameters and Toolpath Productivity Optimization Using a Factorial Design and Fit Regression Model in Face Milling and Drilling Operations
title_short Machining Parameters and Toolpath Productivity Optimization Using a Factorial Design and Fit Regression Model in Face Milling and Drilling Operations
title_full Machining Parameters and Toolpath Productivity Optimization Using a Factorial Design and Fit Regression Model in Face Milling and Drilling Operations
title_fullStr Machining Parameters and Toolpath Productivity Optimization Using a Factorial Design and Fit Regression Model in Face Milling and Drilling Operations
title_full_unstemmed Machining Parameters and Toolpath Productivity Optimization Using a Factorial Design and Fit Regression Model in Face Milling and Drilling Operations
title_sort machining parameters and toolpath productivity optimization using a factorial design and fit regression model in face milling and drilling operations
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2020-01-01
description 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, with p values higher than 0.05 which is the significance level for the nonlinear and linear equations with an R square 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.
url http://dx.doi.org/10.1155/2020/8718597
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