Comparison of Statistical and Soft Computing Models for Predicting Hardness and Wear Rate of Cu-Ni-Sn Alloy

Ilangovan, S.; Vignesh, R. Vaira; Padmanaban, R.; Gokulachandran, J.

doi:10.1007/978-981-10-7871-2_54

Cited by 11 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, the mass loss was obtained. The mass loss was correlated to the wear rate using Equation (3) [5,19,20]. The average friction coefficient was determined using Equation (4) [5,19,20].…”

Section: Methodsmentioning

confidence: 99%

Tribological characterization of functionally gradient composite (Cu–Fe–CeO₂–Al₂O₃–C_g) for wind turbine brake pad

Kumar

Kannan

Srivathsan

et al. 2023

Tribology - Materials, Surfaces & Interfaces

View full text Add to dashboard Cite

Copper-based functionally gradient composite material is developed using powder metallurgy processing technique, as a potential wind turbine brake pad material. The developed composite has a gradient composition of Cu, CeO 2 , Al 2 O 3 , Fe, and C g to enable joint strength at the interface (brake calliper) and wear resistance at the contact surface (brake disc). The article presents a comprehensive analysis on the microstructure, microhardness, and tribological performance of the developed composite. The wear mechanism is deduced through surface morphology, elemental composition, and phase composition analysis using field emission scanning electron microscope, energy dispersive X-ray spectroscope, X-ray diffractometer, and X-ray photoelectron spectroscope. A maximum hardness of 198.2 HV was obtained at the contact surface. Experimental values from tribology tests show that a decreasing trend was obtained with a wear rate of 2.013 × 10 −7 g N-m −1 and a friction coefficient was 0.215.

show abstract

“…Then, the mass loss was obtained. The mass loss was correlated to the wear rate using Equation (3) [5,19,20]. The average friction coefficient was determined using Equation (4) [5,19,20].…”

Section: Methodsmentioning

confidence: 99%

Tribological characterization of functionally gradient composite (Cu–Fe–CeO₂–Al₂O₃–C_g) for wind turbine brake pad

Kumar

Kannan

Srivathsan

et al. 2023

Tribology - Materials, Surfaces & Interfaces

View full text Add to dashboard Cite

show abstract

“…Cu is the most commonly used material in brake pads because of its good brake performance characteristics and excellent thermal conductivity [11][12][13][14]. Besides, Cu enables a stable friction coefficient and stabilizes smooth sliding [15 -17].…”

Section: Introductionmentioning

confidence: 99%

Tribological Performance of the Continuous Steel Fiber-reinforced Cu based Friction Material for Heavy-duty Braking Applications

et al. 2022

View full text Add to dashboard Cite

Brake pads are the major components of the wind turbine, which primarily consists of metal matrix and ceramic reinforcements. The ceramic particles plunge out during the application of brakes, which in turn leads to the transformation of the wear mechanism from adhesive to abrasive wear. This increases the wear rate and therefore the study investigates the possible replacement of ceramic reinforcements with continuous fibers that exhibit high strength and compatibility. In recent years, Cu-based brake pads that are fabricated by powder metallurgy route are widely used in wind turbines. In this study, continuous fibers of stainless steel SS304 are reinforced in the Cu matrix by the casting method to preserve the fiber alignment. The fabricated composite is characterized for microstructure, microhardness, and tribological behavior. Besides, a comprehensive analysis of the wear mechanism in the developed composite is presented based on the surface morphology, elemental composition, and phase analysis.

show abstract

“…Gokulchandran et al [13] used Matlab to train a neural network to predict the tool life in which 70 % of the data was used for measurement, 15 % for testing, and remaining for validation purposes. S.Illangovan et al [14] implemented the integration of neural network and fuzzy logic for predicting the hardness and wear rate of specific alloy specimens. Izabela Rojek [15] did a comparative analysis utilizing MLP, RBF, and Kohonen systems for the machine choice, tool choice, and machine parameter choice.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Based Machining Operation Selection for Prismatic Components

Natarajan¹,

Gokulachandran²

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

Computer-aided process planning systems are used to assist human planners in producing better process plans. New artificial intelligence techniques play a significant role in CAPP. CAPP research includes neural network approaches, knowledgebased techniques, Petri nets, agent-based, fuzzy set theory, genetic algorithm, Standard for the Exchange of Product model data (STEP)-Compliant CAPP, and Internet-based techniques. This study deals with the application of the Artificial Neural Network techniques (ANN) in CAPP because of their learning ability and massive potential toward dynamic planning. This study focuses on the usage of artificial neural networks machining operation selection and sequences of operations for prismatic components. The intelligent CAPP system suggests the best machining operation and its sequences for the prismatic components using tolerances, material requirements, and surface finish details. The process planning of machining features in part is the starting point. An enormous amount of knowledge is required for part feature process planning, like selecting proper material, size, stock, dimensional tolerance, and surface finish. In this work, various prismatic features, such as a hole, slot, pocket, boss, chamfer, fillet, and face are taken and details like material, size, stock, dimensional tolerance and surface finish are properly normalized and given as input to neural networks to find the required sequence of machining operation. LevenbergMarquidt algorithm was used to train the networks and was found very effective in operation sequence selection. A sample prismatic component with nine features have been analyzed and found to be more productive. Levenberg Marquidt algorithm is then compared with the conjugant space algorithm, and it is found that the former produces less error in outputs compared to them later.

show abstract

Comparison of Statistical and Soft Computing Models for Predicting Hardness and Wear Rate of Cu-Ni-Sn Alloy

Cited by 11 publications

References 13 publications

Tribological characterization of functionally gradient composite (Cu–Fe–CeO₂–Al₂O₃–C_g) for wind turbine brake pad

Tribological characterization of functionally gradient composite (Cu–Fe–CeO₂–Al₂O₃–C_g) for wind turbine brake pad

Tribological Performance of the Continuous Steel Fiber-reinforced Cu based Friction Material for Heavy-duty Braking Applications

Artificial Neural Network Based Machining Operation Selection for Prismatic Components

Contact Info

Product

Resources

About

Comparison of Statistical and Soft Computing Models for Predicting Hardness and Wear Rate of Cu-Ni-Sn Alloy

Cited by 11 publications

References 13 publications

Tribological characterization of functionally gradient composite (Cu–Fe–CeO2–Al2O3–Cg) for wind turbine brake pad

Tribological characterization of functionally gradient composite (Cu–Fe–CeO2–Al2O3–Cg) for wind turbine brake pad

Tribological Performance of the Continuous Steel Fiber-reinforced Cu based Friction Material for Heavy-duty Braking Applications

Artificial Neural Network Based Machining Operation Selection for Prismatic Components

Contact Info

Product

Resources

About

Tribological characterization of functionally gradient composite (Cu–Fe–CeO₂–Al₂O₃–C_g) for wind turbine brake pad

Tribological characterization of functionally gradient composite (Cu–Fe–CeO₂–Al₂O₃–C_g) for wind turbine brake pad