Prediction of wear loss quantities of ferro-alloy coating using different machine learning algorithms
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Prediction of wear loss quantities of ferro-alloy coating using different machine learning algorithms
Authors
Keywords
surface coating, plasma transfer arc (PTA) welding, wear, prediction, machine learning algorithms
Journal
Friction
Volume -, Issue -, Pages -
Publisher
Springer Nature
Online
2019-01-18
DOI
10.1007/s40544-018-0249-z
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Gaussian process regression for tool wear prediction
- (2018) Dongdong Kong et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Effect of heat input on microstructure, wear and friction behavior of (wt.-%) 50FeCrC-20FeW-30FeB coating on AISI 1020 produced by using PTA welding
- (2018) Cihan Özel et al. PLoS One
- Enhanced wear resistance of molybdenum nitride coatings deposited by high power impulse magnetron sputtering by using micropatterned surfaces
- (2018) M. Kommer et al. SURFACE & COATINGS TECHNOLOGY
- Galvanically induced potentials to enable minimal tribochemical wear of stainless steel lubricated with sodium chloride and ionic liquid aqueous solution
- (2018) Tobias Amann et al. Friction
- Characterizing the magnetic memory signals on the surface of plasma transferred arc cladding coating under fatigue loads
- (2017) Haihong Huang et al. JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS
- An integrated Gaussian process regression for prediction of remaining useful life of slow speed bearings based on acoustic emission
- (2017) S.A. Aye et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Microstructure and wear resistance performance of Cu–Ni–Mn alloy based hardfacing coatings reinforced by WC particles
- (2016) Jun Liu et al. JOURNAL OF ALLOYS AND COMPOUNDS
- Establishing quantitative structure tribo-ability relationship model using Bayesian regularization neural network
- (2016) Xinlei Gao et al. Friction
- The effect of microstructure on abrasive wear of a Fe–Cr–C–Nb hardfacing alloy deposited by the open arc welding process
- (2015) E.O. Correa et al. SURFACE & COATINGS TECHNOLOGY
- Artificial neural network prediction of aging effects on the wear behavior of IN706 superalloy
- (2015) Osman Palavar et al. MATERIALS & DESIGN
- Hardfacing using ferro-alloy powder mixtures by submerged arc welding
- (2014) Ramin Zahiri et al. SURFACE & COATINGS TECHNOLOGY
- Tribological properties and wear prediction model of TiC particles reinforced Ni-base alloy composite coatings
- (2014) Ye-fa TAN et al. TRANSACTIONS OF NONFERROUS METALS SOCIETY OF CHINA
- Evolutionary support vector regression algorithm applied to the prediction of the thickness of the chromium layer in a hard chromium plating process
- (2013) F. Sánchez Lasheras et al. APPLIED MATHEMATICS AND COMPUTATION
- Gaussian processes for time-series modelling
- (2013) S. Roberts et al. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- Continuous tool wear prediction based on Gaussian mixture regression model
- (2012) Guofeng Wang et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Support Vector Machines for classification and regression
- (2009) Richard G. Brereton et al. ANALYST
- Boride coatings on steel using shielded metal arc welding electrode: Microstructure and hardness
- (2009) Mehmet Eroglu SURFACE & COATINGS TECHNOLOGY
- Effect of molybdenum on the microstructure and wear resistance of Fe-based hardfacing coatings
- (2007) X.H. Wang et al. MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started