Results 81 to 90 of about 4,650,132 (303)

Experimental study on the influence of coal surface roughness on wettability of non-cationic surfactants

open access: yesMeikuang Anquan, 2021
Adding non-cationic surfactant in water can effectively enhance the wettability of water on coal surface, and the wettability of coal surface is affected by the type of solution and the physical and chemical properties of coal surface.
ZHANG Kunyin   +4 more
doaj   +1 more source

Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying

open access: yesAdvanced Engineering Materials, EarlyView.
An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara   +8 more
wiley   +1 more source

Analysis of Circular Saw Tooth Marks Profile on Material Machined Surface After Filtering with Fast Fourier Transform (FFT)

open access: yesDrvna Industrija, 2022
The article presents theoretical analysis of machined surface roughness after sawing on circular saw and implementation of fast Fourier transform (FFT) as a possible simple filtering method for filtering out just the saw blade and saw tooth influence on ...
Igor Đukić   +3 more
doaj   +1 more source

Optimization of the Production of Rubber Compounds Using Mathematical Models

open access: yesAdvanced Engineering Materials, EarlyView.
Rubber compounds were mixed in a batch internal mixer, and symbolic regression was used to derive mathematical models linking recipe and process parameters to ram path, torque, and mixing quality (incorporation, dispersion, distribution). Subsequent optimization with evolutionary algorithms identified operating conditions that reduce specific energy ...
Anke Bardehle   +7 more
wiley   +1 more source

Pre-evaluation on surface profile in turning process based on cutting parameters [PDF]

open access: yes, 2009
Traditional online or in-process surface profile (quality) evaluation (prediction) needs to integrate cutting parameters and several in-process factors (vibration, machine dynamics, tool wear, etc) for high accuracy.
MA, Ning   +3 more
core   +1 more source

Modular Critical Element Recycling Platform Using a Nanoporous Additively Manufactured Gyroid

open access: yesAdvanced Engineering Materials, EarlyView.
A modular recycling platform integrates 3D‐printed nanoporous gyroid structures to enable efficient critical element recovery. This system utilizes a hierarchical architecture, combining macroscopic channels with polymerization‐induced nanoscale porosity. By systematically tuning structural wall thickness and resin formulation, the platform achieves an
Xiangyu Gao   +6 more
wiley   +1 more source

Wettability versus roughness of engineering surfaces [PDF]

open access: yes, 2011
Wetting of real engineering surfaces occurs in many industrial applications (liquid coating, lubrication, printing, painting, ...). Forced and natural wetting can be beneficial in many cases, providing lubrication and therefore reducing friction and wear.
Wilson, M.C.T.   +7 more
core   +1 more source

Inverse Identification of Energy‐Dependent Laser Absorptivity in NiTi Laser Powder‐Bed Fusion via Calibrated Melt Pool Simulation

open access: yesAdvanced Engineering Materials, EarlyView.
A combined experimental–computational framework identifies energy‐dependent laser absorptivity for NiTi in laser powder‐bed fusion, applicable to conduction and transition modes. Single‐track experiments and thermofluid smoothed particle hydrodynamics simulations are coupled through inverse analysis of melt pool geometry.
Mohamadreza Afrasiabi   +3 more
wiley   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

open access: yesAdvanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer   +4 more
wiley   +1 more source

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