Unprecedented developed composite polishing system to achieve atomic surface integrating rough and fine polishing using a novel hyper-conjugated pad through controlling the temperature of a proposed green slurry

Advanced Composites and Hybrid Materials, Год журнала: 2025, Номер 8(3)

Опубликована: Апрель 23, 2025

Язык: Английский

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Laser Cladding for Diamond-Reinforced Composites with Low-Melting-Point Transition Layer

Materials, Год журнала: 2025, Номер 18(10), С. 2402 - 2402

Опубликована: Май 21, 2025

To address the graphitization of diamond induced by high temperatures during laser cladding diamond-reinforced composites, this study proposes a method utilizing Inconel 718 (IN718) nickel-based alloy as transition layer which has lower melting point than substrate 45# steel. And then, in order to analyze detailed characteristics samples, scanning electron microscopy (SEM), EDS, Raman spectral analyzer, super-depth-of-field microscope, and friction tests were used. Experimental test results demonstrate that IN718 enhances coating performance through dual mechanisms: firstly, its relatively low (1392 °C) reduces molten pool’s peak temperature, effectively suppressing thermal-induced diamond; on other hand, simultaneously it acts diffusion barrier inhibit Fe migration from weaken Fe–C interfacial catalytic reactions. Microstructural analysis reveals improved encapsulation reduced sintering defects coatings with layer. Tribological confirm samples L exhibit coefficients significantly enhanced wear resistance compared those without. This elucidates synergistic mechanism thermal management optimization reaction suppression, providing an innovative solution overcome high-temperature damage bottleneck laser-clad tools.

Язык: Английский

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Prediction of width and depth of laser‐engraved microgrooves: Machine learning versus response surface modelling

The Canadian Journal of Chemical Engineering, Год журнала: 2025, Номер unknown

Опубликована: Июнь 1, 2025

Abstract This study presents a comparative evaluation of two predictive approaches for determining microgroove dimensions in laser machining. The first approach employs response surface methodology (RSM) regression models to predict width and depth using three input parameters: power (10–20 W), scanning rate (50–150 mm/s), focus distance (6–8 mm). second utilizes data‐driven machine learning (ML) deep neural network (DNN) models, incorporating five power, rate, distance, pass number (1–3), measurement location (edge middle). A total 350 microgrooves were analyzed, results indicate that the DNN model achieved highest prediction accuracy, with an R 2 value exceeding 0.94 mean absolute error 10.96 μm on training data. These findings demonstrate potential improving precision machining predictions.

Язык: Английский

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Multifunctional-layer composite coatings with synergistically enhanced laser protection performance

Ceramics International, Год журнала: 2025, Номер unknown

Опубликована: Июнь 1, 2025

Язык: Английский

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