Application of a Fuzzy Inference System for the Optimization of Material Removal Rate and Multiple Surface Roughness Characteristics in the Machining of GFRP Polyester Composites

• Autorzy: Ankita Singh , Saurav Datta , Siba Sankar Mahapatra
• Języki publikacji: EN

Abstrakty

EN

This paper presents a multi-objective extended optimization methodology applied in the machining of a randomly oriented GFRP rod. Design of Experiment (DOE) has been selected based on a L9 orthogonal array design with varying process control parameters like: spindle speed, feed rate and depth of cut. Multiple surface roughness parameters of the machined FRP product along with the Material Removal Rate (MRR) of the machining process have been optimized simultaneously. The Fuzzy Inference System (FIS) has been proposed for providing feasible means for the meaningful aggregation of multiple objective functions into an equivalent single performance index (MPCI). This Multi-Performance Characteristic Index (MPCI) has been optimized using the Taguchi method. The approach adapted here is capable of overcoming limitations/ assumptions of existing optimization methodologies available in the literature. (original abstract)

Słowa kluczowe

EN

Fuzzy systems; Polymers; Materials science; Research results

PL

Systemy rozmyte; Polimery; Materiałoznawstwo; Wyniki badań

• Czasopismo: Decision Making in Manufacturing and Services
• Rocznik: 2013
• Tom: 7
• Numer: nr 1/2
• Strony: 19--42

Twórcy

autor

Ankita Singh

autor

Saurav Datta

autor

Siba Sankar Mahapatra

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Identyfikatory

DOI

10.7494/dmms.2013.7.1.19