View in EDS

Fuzzy rule based predictive model for cutting force in turning of reinforced PEEK composite

Saved in:
Bibliographic Details
Title: Fuzzy rule based predictive model for cutting force in turning of reinforced PEEK composite
Authors: Hanafi, Issam1 hanafi.issam@yahoo.fr, Khamlichi, Abdellatif1 khamlichi7@yahoo.es, Cabrera, Francisco Mata2 francisco.mcabrera@uclm.es, Nuñez López, Pedro J.3 Pedro.Nunez@uclm.es, Jabbouri, Abdallah4 a.jabbouri@gmail.com
Source: Measurement (02632241). Jul2012, Vol. 45 Issue 6, p1424-1435. 12p.
Subjects: Fuzzy logic, Cutting force, Prediction models, Reinforced plastics, Composite materials, Industrial applications
Abstract: Abstract: Carbon fiber reinforced plastics have gained large interest among the community of composites manufactures and consumers due to their excellent adaptability to various industrial applications. In particular, there exists a demand for optimizing machining conditions of mechanical parts made from poly ether ether ketone reinforced with 30% of carbon fiber when using TiN coated cutting tools. In this work, predictive models that describe the relationship between the independent machining variables: cutting speed, feed rate and depth of cut, and the criteria of machinability: cutting force, cutting power and specific cutting pressure were derived. This was achieved by using either classical response surface regression technique or by implementing fuzzy logic models which are based on the compositional rule of inference that establish a parametric relation between a given response and the independent input variables. Effectiveness of these models has been proved by analyzing their coefficients of correlation and by comparing predictions they give with experimental results. [Copyright &y& Elsevier]
Copyright of Measurement (02632241) is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:Abstract: Carbon fiber reinforced plastics have gained large interest among the community of composites manufactures and consumers due to their excellent adaptability to various industrial applications. In particular, there exists a demand for optimizing machining conditions of mechanical parts made from poly ether ether ketone reinforced with 30% of carbon fiber when using TiN coated cutting tools. In this work, predictive models that describe the relationship between the independent machining variables: cutting speed, feed rate and depth of cut, and the criteria of machinability: cutting force, cutting power and specific cutting pressure were derived. This was achieved by using either classical response surface regression technique or by implementing fuzzy logic models which are based on the compositional rule of inference that establish a parametric relation between a given response and the independent input variables. Effectiveness of these models has been proved by analyzing their coefficients of correlation and by comparing predictions they give with experimental results. [Copyright &y& Elsevier]
ISSN:02632241
DOI:10.1016/j.measurement.2012.03.022