Parametric study of drilling process in ramie-epoxy composites using grey relational approach and artificial neural networks DOI
Sourav Kumar Mahapatra,

Alok Satapathy

Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering, Год журнала: 2025, Номер unknown

Опубликована: Фев. 5, 2025

Drilling is one of the primary and most preferred machining operations for producing holes in composite materials. Unlike conventional materials, drilling fiber reinforced polymer (FRP) more challenging because its heterogeneous anisotropic nature. In present study on behavior, ramie-epoxy composites are fabricated then subjected to tests following Taguchi L 16 orthogonal array. The influence spindle speed, feed rate drill diameter thrust force, torque delamination studied using technique. optimization parameters multi-response characteristics analyzed grey relational approach. results from analysis (GRA) indicate that dominant factor influencing multi-performance contributing 77.41% followed by speed with individual contributions 6.95% 5.51% respectively. It found optimal setting attaining minimum simultaneously can be obtained while at a 4000 rpm, 40 mm/min 4 mm Further, an artificial neural network (ANN) model proposed as tool effectively predict delamination. showed fairly good predicting high accuracies 89.593%, 95.144% 96.186% errors ±2%, ± 3% ±0.3% Analysis chip formation mechanisms morphological drilled done type chips formed during identify possible causes

Язык: Английский

Fabrication and multi-aspect characterization of polymer composite reinforced with differently stacked flax-fiber and steel-wire meshes DOI

Subham Kumar Bhoi,

Alok Satapathy

Journal of Elastomers & Plastics, Год журнала: 2025, Номер unknown

Опубликована: Янв. 28, 2025

This study focuses on developing a new type of multi-fiber reinforced multi-layer polymer composite using natural and metal fibers. Flax fiber has shown potential as fairly good reinforcement, but its mechanical weaknesses have led researchers to explore hybridization in composites for better performance. The paper reports the fabrication characterization unique kind hybrid that uses advantages stainless steel along with flax fiber, resulting lightweight material suitable engineering uses. In this research, SS-304 wire mesh is used fibers make three different composites, varying stacking arrangements. Experimental results indicate that, although inclusion slightly reduces interlaminar shear strength, significant improvements are observed tensile flexural hardness impact resistance. Stereo-microscopy utilized analyze surface features sequences, while scanning electron microscopy (SEM) reveals fracture surfaces specimens. Energy-dispersive X-ray spectroscopy (EDX) mapping helps identify elemental compositions present composites. demonstrates reinforcement can enhance properties flax-reinforced providing sustainable solution applications.

Язык: Английский

Процитировано

0

Parametric study of drilling process in ramie-epoxy composites using grey relational approach and artificial neural networks DOI
Sourav Kumar Mahapatra,

Alok Satapathy

Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering, Год журнала: 2025, Номер unknown

Опубликована: Фев. 5, 2025

Drilling is one of the primary and most preferred machining operations for producing holes in composite materials. Unlike conventional materials, drilling fiber reinforced polymer (FRP) more challenging because its heterogeneous anisotropic nature. In present study on behavior, ramie-epoxy composites are fabricated then subjected to tests following Taguchi L 16 orthogonal array. The influence spindle speed, feed rate drill diameter thrust force, torque delamination studied using technique. optimization parameters multi-response characteristics analyzed grey relational approach. results from analysis (GRA) indicate that dominant factor influencing multi-performance contributing 77.41% followed by speed with individual contributions 6.95% 5.51% respectively. It found optimal setting attaining minimum simultaneously can be obtained while at a 4000 rpm, 40 mm/min 4 mm Further, an artificial neural network (ANN) model proposed as tool effectively predict delamination. showed fairly good predicting high accuracies 89.593%, 95.144% 96.186% errors ±2%, ± 3% ±0.3% Analysis chip formation mechanisms morphological drilled done type chips formed during identify possible causes

Язык: Английский

Процитировано

0